@Article{info:doi/10.2196/64349, author="Elvas, B. Luis and Almeida, Ana and Ferreira, C. Joao", title="The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review", journal="JMIR Med Inform", year="2025", month="Mar", day="6", volume="13", pages="e64349", keywords="artificial intelligence", keywords="machine learning", keywords="cardiovascular diseases", keywords="cardiovascular events", keywords="health care", keywords="monitoring", keywords="early detection", keywords="AI", keywords="cardiovascular", keywords="literature review", keywords="medical data", keywords="detect", keywords="patient outcomes", keywords="neural network", keywords="ML model", keywords="mobile phone", abstract="Background: Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different health care settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVDs), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI's capacity to analyze complex datasets and uncover critical patterns. Objective: This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of CVDs, identifying areas for further research to enhance patient outcomes and support early interventions. Methods: This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field. Results: Through the methodology used, 64 documents were retrieved, of which 40 documents met the inclusion criteria. The reviewed papers demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in health care supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVDs. Conclusions: The study concludes that AI and ML techniques can improve the accuracy of CVD classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in health care. ", doi="10.2196/64349", url="https://medinform.jmir.org/2025/1/e64349" } @Article{info:doi/10.2196/62758, author="Park, Katie Jinkyung and Singh, K. Vivek and Wisniewski, Pamela", title="Current Landscape and Future Directions for Mental Health Conversational Agents for Youth: Scoping Review", journal="JMIR Med Inform", year="2025", month="Feb", day="28", volume="13", pages="e62758", keywords="conversational agent", keywords="chatbot", keywords="mental health", keywords="youth", keywords="adolescent", keywords="scoping review", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", keywords="artificial intelligence", abstract="Background: Conversational agents (CAs; chatbots) are systems with the ability to interact with users using natural human dialogue. They are increasingly used to support interactive knowledge discovery of sensitive topics such as mental health topics. While much of the research on CAs for mental health has focused on adult populations, the insights from such research may not apply to CAs for youth. Objective: This study aimed to comprehensively evaluate the state-of-the-art research on mental health CAs for youth. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we identified 39 peer-reviewed studies specific to mental health CAs designed for youth across 4 databases, including ProQuest, Scopus, Web of Science, and PubMed. We conducted a scoping review of the literature to evaluate the characteristics of research on mental health CAs designed for youth, the design and computational considerations of mental health CAs for youth, and the evaluation outcomes reported in the research on mental health CAs for youth. Results: We found that many mental health CAs (11/39, 28\%) were designed as older peers to provide therapeutic or educational content to promote youth mental well-being. All CAs were designed based on expert knowledge, with a few that incorporated inputs from youth. The technical maturity of CAs was in its infancy, focusing on building prototypes with rule-based models to deliver prewritten content, with limited safety features to respond to imminent risk. Research findings suggest that while youth appreciate the 24/7 availability of friendly or empathetic conversation on sensitive topics with CAs, they found the content provided by CAs to be limited. Finally, we found that most (35/39, 90\%) of the reviewed studies did not address the ethical aspects of mental health CAs, while youth were concerned about the privacy and confidentiality of their sensitive conversation data. Conclusions: Our study highlights the need for researchers to continue to work together to align evidence-based research on mental health CAs for youth with lessons learned on how to best deliver these technologies to youth. Our review brings to light mental health CAs needing further development and evaluation. The new trend of large language model--based CAs can make such technologies more feasible. However, the privacy and safety of the systems should be prioritized. Although preliminary evidence shows positive trends in mental health CAs, long-term evaluative research with larger sample sizes and robust research designs is needed to validate their efficacy. More importantly, collaboration between youth and clinical experts is essential from the early design stages through to the final evaluation to develop safe, effective, and youth-centered mental health chatbots. Finally, best practices for risk mitigation and ethical development of CAs with and for youth are needed to promote their mental well-being. ", doi="10.2196/62758", url="https://medinform.jmir.org/2025/1/e62758", url="http://www.ncbi.nlm.nih.gov/pubmed/40053735" } @Article{info:doi/10.2196/62914, author="Scribano Parada, Paz Mar{\'i}a de la and Gonz{\'a}lez Palau, F{\'a}tima and Valladares Rodr{\'i}guez, Sonia and Rincon, Mariano and Rico Barroeta, Jos{\'e} Maria and Garc{\'i}a Rodriguez, Marta and Bueno Aguado, Yolanda and Herrero Blanco, Ana and D{\'i}az-L{\'o}pez, Estela and Bachiller Mayoral, Margarita and Losada Dur{\'a}n, Raquel", title="Preclinical Cognitive Markers of Alzheimer Disease and Early Diagnosis Using Virtual Reality and Artificial Intelligence: Literature Review", journal="JMIR Med Inform", year="2025", month="Jan", day="28", volume="13", pages="e62914", keywords="dementia", keywords="Alzheimer disease", keywords="mild cognitive impairment", keywords="virtual reality", keywords="artificial intelligence", keywords="early detection", keywords="qualitative review", keywords="literature review", keywords="AI", abstract="Background: This review explores the potential of virtual reality (VR) and artificial intelligence (AI) to identify preclinical cognitive markers of Alzheimer disease (AD). By synthesizing recent studies, it aims to advance early diagnostic methods to detect AD before significant symptoms occur. Objective: Research emphasizes the significance of early detection in AD during the preclinical phase, which does not involve cognitive impairment but nevertheless requires reliable biomarkers. Current biomarkers face challenges, prompting the exploration of cognitive behavior indicators beyond episodic memory. Methods: Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched Scopus, PubMed, and Google Scholar for studies on neuropsychiatric disorders utilizing conversational data. Results: Following an analysis of 38 selected articles, we highlight verbal episodic memory as a sensitive preclinical AD marker, with supporting evidence from neuroimaging and genetic profiling. Executive functions precede memory decline, while processing speed is a significant correlate. The potential of VR remains underexplored, and AI algorithms offer a multidimensional approach to early neurocognitive disorder diagnosis. Conclusions: Emerging technologies like VR and AI show promise for preclinical diagnostics, but thorough validation and regulation for clinical safety and efficacy are necessary. Continued technological advancements are expected to enhance early detection and management of AD. ", doi="10.2196/62914", url="https://medinform.jmir.org/2025/1/e62914" } @Article{info:doi/10.2196/53781, author="Leblanc, Victor and Hamroun, Aghiles and Bentegeac, Rapha{\"e}l and Le Guellec, Bastien and Lenain, R{\'e}mi and Chazard, Emmanuel", title="Added Value of Medical Subject Headings Terms in Search Strategies of Systematic Reviews: Comparative Study", journal="J Med Internet Res", year="2024", month="Nov", day="19", volume="26", pages="e53781", keywords="Medical Subject Headings", keywords="MeSH", keywords="MeSH thesaurus", keywords="systematic review", keywords="PubMed", keywords="search strategy", keywords="comparative analysis", keywords="literature review", keywords="positive predictive value", keywords="PPV", keywords="review", keywords="scientific knowledge", keywords="medical knowledge", keywords="utility", keywords="systematic literature review", abstract="Background: The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically. Objective: The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs). Methods: SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values. Results: In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41\%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8\% (62.1\%-95.2\%) and 71.4\% (42.6\%-90\%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67). Conclusions: Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs. ", doi="10.2196/53781", url="https://www.jmir.org/2024/1/e53781" } @Article{info:doi/10.2196/58130, author="Penev, P. Yordan and Buchanan, R. Timothy and Ruppert, M. Matthew and Liu, Michelle and Shekouhi, Ramin and Guan, Ziyuan and Balch, Jeremy and Ozrazgat-Baslanti, Tezcan and Shickel, Benjamin and Loftus, J. Tyler and Bihorac, Azra", title="Electronic Health Record Data Quality and Performance Assessments: Scoping Review", journal="JMIR Med Inform", year="2024", month="Nov", day="6", volume="12", pages="e58130", keywords="electronic health record", keywords="EHR", keywords="record", keywords="data quality", keywords="data performance", keywords="clinical informatics", keywords="performance", keywords="data science", keywords="synthesis", keywords="review methods", keywords="review methodology", keywords="search", keywords="scoping", abstract="Background: Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment. Objective: This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field. Methods: PubMed was systematically searched for original research articles assessing EHR DQ and performance from inception until May 7, 2023. Results: Our search yielded 26 original research articles. Most articles had 1 or more significant limitations, including incomplete or inconsistent reporting (n=6, 30\%), poor replicability (n=5, 25\%), and limited generalizability of results (n=5, 25\%). Completeness (n=21, 81\%), conformance (n=18, 69\%), and plausibility (n=16, 62\%) were the most cited indicators of DQ, while correctness or accuracy (n=14, 54\%) was most cited for data performance, with context-specific supplementation by recency (n=7, 27\%), fairness (n=6, 23\%), stability (n=4, 15\%), and shareability (n=2, 8\%) assessments. Artificial intelligence--based techniques, including natural language data extraction, data imputation, and fairness algorithms, were demonstrated to play a rising role in improving both dataset quality and performance. Conclusions: This review highlights the need for incentivizing DQ and performance assessments and their standardization. The results suggest the usefulness of artificial intelligence--based techniques for enhancing DQ and performance to unlock the full potential of EHRs to improve medical research and practice. ", doi="10.2196/58130", url="https://medinform.jmir.org/2024/1/e58130" } @Article{info:doi/10.2196/60164, author="Nunes, Miguel and Bone, Joao and Ferreira, C. Joao and Elvas, B. Luis", title="Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review", journal="JMIR Med Inform", year="2024", month="Oct", day="21", volume="12", pages="e60164", keywords="language model", keywords="information extraction", keywords="healthcare", keywords="PRISMA-ScR", keywords="scoping literature review", keywords="transformers", keywords="natural language processing", keywords="European Portuguese", abstract="Background: In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non--English-language with fewer resources. Objective: This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted. Methods: This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes. Results: Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general--health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms. Conclusions: The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive progress in AI. Health care professionals could benefit from highlighting medically relevant information and optimizing the reading of the textual data, or this information could be used to create patient medical timelines, allowing for profiling. ", doi="10.2196/60164", url="https://medinform.jmir.org/2024/1/e60164", url="http://www.ncbi.nlm.nih.gov/pubmed/39432345" } @Article{info:doi/10.2196/56343, author="Mollalo, Abolfazl and Hamidi, Bashir and Lenert, A. Leslie and Alekseyenko, V. Alexander", title="Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review", journal="JMIR Med Inform", year="2024", month="Oct", day="15", volume="12", pages="e56343", keywords="clinical phenotypes", keywords="electronic health records", keywords="geocoding", keywords="geographic information systems", keywords="patient phenotypes", keywords="spatial analysis", abstract="Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes. Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains. Results: A substantial proportion of studies (>85\%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86\%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support. ", doi="10.2196/56343", url="https://medinform.jmir.org/2024/1/e56343" } @Article{info:doi/10.2196/49781, author="Grothman, Allison and Ma, J. William and Tickner, G. Kendra and Martin, A. Elliot and Southern, A. Danielle and Quan, Hude", title="Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review", journal="JMIR Med Inform", year="2024", month="Oct", day="14", volume="12", pages="e49781", keywords="electronic medical records", keywords="EMR phenotyping", keywords="depression", keywords="algorithms", keywords="health services research", keywords="precision public health", keywords="inpatient", keywords="clinical information", keywords="phenotyping", keywords="data accessibility", keywords="scoping review", keywords="disparity", keywords="development", keywords="phenotype", keywords="PRISMA-ScR", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews", abstract="Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders. Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones. Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75\% (15/20). The United Kingdom and Spain followed this, accounting for 15\% (3/20) and 10\% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms. Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general. ", doi="10.2196/49781", url="https://medinform.jmir.org/2024/1/e49781" } @Article{info:doi/10.2196/62924, author="Chang, Eunsuk and Sung, Sumi", title="Use of SNOMED CT in Large Language Models: Scoping Review", journal="JMIR Med Inform", year="2024", month="Oct", day="7", volume="12", pages="e62924", keywords="SNOMED CT", keywords="ontology", keywords="knowledge graph", keywords="large language models", keywords="natural language processing", keywords="language models", abstract="Background: Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed. Objective: This scoping review aims to examine how SNOMED CT is integrated into LLMs, focusing on (1) the types and components of LLMs being integrated with SNOMED CT, (2) which contents of SNOMED CT are being integrated, and (3) whether this integration improves LLM performance on NLP tasks. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched ACM Digital Library, ACL Anthology, IEEE Xplore, PubMed, and Embase for relevant studies published from 2018 to 2023. Studies were included if they incorporated SNOMED CT into LLM pipelines for natural language understanding or generation tasks. Data on LLM types, SNOMED CT integration methods, end tasks, and performance metrics were extracted and synthesized. Results: The review included 37 studies. Bidirectional Encoder Representations from Transformers and its biomedical variants were the most commonly used LLMs. Three main approaches for integrating SNOMED CT were identified: (1) incorporating SNOMED CT into LLM inputs (28/37, 76\%), primarily using concept descriptions to expand training corpora; (2) integrating SNOMED CT into additional fusion modules (5/37, 14\%); and (3) using SNOMED CT as an external knowledge retriever during inference (5/37, 14\%). The most frequent end task was medical concept normalization (15/37, 41\%), followed by entity extraction or typing and classification. While most studies (17/19, 89\%) reported performance improvements after SNOMED CT integration, only a small fraction (19/37, 51\%) provided direct comparisons. The reported gains varied widely across different metrics and tasks, ranging from 0.87\% to 131.66\%. However, some studies showed either no improvement or a decline in certain performance metrics. Conclusions: This review demonstrates diverse approaches for integrating SNOMED CT into LLMs, with a focus on using concept descriptions to enhance biomedical language understanding and generation. While the results suggest potential benefits of SNOMED CT integration, the lack of standardized evaluation methods and comprehensive performance reporting hinders definitive conclusions about its effectiveness. Future research should prioritize consistent reporting of performance comparisons and explore more sophisticated methods for incorporating SNOMED CT's relational structure into LLMs. In addition, the biomedical NLP community should develop standardized evaluation frameworks to better assess the impact of ontology integration on LLM performance. ", doi="10.2196/62924", url="https://medinform.jmir.org/2024/1/e62924", url="http://www.ncbi.nlm.nih.gov/pubmed/39374057" } @Article{info:doi/10.2196/58445, author="Tabari, Parinaz and Costagliola, Gennaro and De Rosa, Mattia and Boeker, Martin", title="State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)--Based Data Model and Structure Implementations: Systematic Scoping Review", journal="JMIR Med Inform", year="2024", month="Sep", day="24", volume="12", pages="e58445", keywords="data model", keywords="Fast Healthcare Interoperability Resources", keywords="FHIR", keywords="interoperability", keywords="modeling", keywords="PRISMA", abstract="Background: Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research. Objective: This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers. Methods: To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies. Results: On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was ``Observation'' followed by ``Condition'' and ``Patient.'' The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability. Conclusions: FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models. ", doi="10.2196/58445", url="https://medinform.jmir.org/2024/1/e58445", url="http://www.ncbi.nlm.nih.gov/pubmed/39316433" } @Article{info:doi/10.2196/57195, author="van der Meijden, Lise Siri and van Boekel, M. Anna and van Goor, Harry and Nelissen, GHH Rob and Schoones, W. Jan and Steyerberg, W. Ewout and Geerts, F. Bart and de Boer, GJ Mark and Arbous, Sesmu M.", title="Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review", journal="JMIR Med Inform", year="2024", month="Sep", day="10", volume="12", pages="e57195", keywords="postoperative infections", keywords="surveillance", keywords="prediction", keywords="surgery", keywords="artificial intelligence", keywords="chart review", keywords="electronic health record", keywords="scoping review", keywords="postoperative", keywords="surgical", keywords="infection", keywords="infections", keywords="predictions", keywords="predict", keywords="predictive", keywords="bacterial", keywords="machine learning", keywords="record", keywords="records", keywords="EHR", keywords="EHRs", keywords="synthesis", keywords="review methods", keywords="review methodology", keywords="search", keywords="searches", keywords="searching", keywords="scoping", abstract="Background: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. Objective: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. Methods: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. Results: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65\% (49/75) of the identified methods use structured data, and 45\% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. Conclusions: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data. ", doi="10.2196/57195", url="https://medinform.jmir.org/2024/1/e57195", url="http://www.ncbi.nlm.nih.gov/pubmed/39255011" } @Article{info:doi/10.2196/56628, author="Hindelang, Michael and Sitaru, Sebastian and Zink, Alexander", title="Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review", journal="JMIR Med Inform", year="2024", month="Aug", day="29", volume="12", pages="e56628", keywords="medical history-taking", keywords="chatbots", keywords="artificial intelligence", keywords="natural language processing", keywords="health care data collection", keywords="patient engagement", keywords="clinical decision-making", keywords="usability", keywords="acceptability", keywords="systematic review", keywords="diagnostic accuracy", keywords="patient-doctor communication", keywords="cybersecurity", keywords="machine learning", keywords="conversational agents", keywords="health informatics", abstract="Background: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence--driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice. Objective: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history--taking. It also examines potential challenges and future opportunities for integration into clinical practice. Methods: A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history--taking. Interventions focused on chatbots designed to facilitate medical history--taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history--taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included ``chatbot*,'' ``conversational agent*,'' ``virtual assistant,'' ``artificial intelligence chatbot,'' ``medical history,'' and ``history-taking.'' The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs). Results: The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33\%) studies were of high quality, 5 (33\%) studies were of moderate quality, and 5 (33\%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk. Conclusions: This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history--taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine. Trial Registration: PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero ", doi="10.2196/56628", url="https://medinform.jmir.org/2024/1/e56628" } @Article{info:doi/10.2196/50307, author="Marsh, Meghan and Shah, Rafia Syeda and Munce, P. Sarah E. and Perrier, Laure and Lee, Joan Tin-Suet and Colella, F. Tracey J. and Kokorelias, Marie Kristina", title="Characteristics of Existing Online Patient Navigation Interventions: Scoping Review", journal="JMIR Med Inform", year="2024", month="Aug", day="19", volume="12", pages="e50307", keywords="online", keywords="patient navigation", keywords="peer navigation", keywords="patient navigation interventions", keywords="online patient navigation interventions", keywords="scoping review", keywords="patient portals", keywords="social care services", keywords="online medical tools", keywords="eHealth", keywords="telehealth", keywords="personal support", keywords="social care", keywords="patient navigation intervention", abstract="Background: Patient navigation interventions (PNIs) can provide personalized support and promote appropriate coordination or continuation of health and social care services. Online PNIs have demonstrated excellent potential for improving patient knowledge, transition readiness, self-efficacy, and use of services. However, the characteristics (ie, intervention type, mode of delivery, duration, frequency, outcomes and outcome measures, underlying theories or mechanisms of change of the intervention, and impact) of existing online PNIs to support the health and social needs of individuals with illness remain unclear. Objective: This scoping review of the existing literature aims to identify the characteristics of existing online PNIs reported in the literature. Methods: A scoping review based on the guidelines outlined in the Joanna Briggs Institute framework was conducted. A search for peer-reviewed literature published between 1989 and 2022 on online PNIs was conducted using MEDLINE, CINAHL, Embase, PsycInfo, and Cochrane Library databases. Two independent reviewers conducted 2 levels of screening. Data abstraction was conducted to outline key study characteristics (eg, study design, population, and intervention characteristics). The data were analyzed using descriptive statistics and qualitative content analysis. Results: A total of 100 studies met the inclusion criteria. Our findings indicate that a variety of study designs are used to describe and evaluate online PNIs, with literature being published between 2003 and 2022 in Western countries. Of these studies, 39 (39\%) studies were randomized controlled trials. In addition, we noticed an increase in reported online PNIs since 2019. The majority of studies involved White females with a diagnosis of cancer and a lack of participants aged 70 years or older was observed. Most online PNIs provide support through navigation, self-management and lifestyle changes, counseling, coaching, education, or a combination of support. Variation was noted in terms of mode of delivery, duration, and frequency. Only a small number of studies described theoretical frameworks or change mechanisms to guide intervention. Conclusions: To our knowledge, this is the first review to comprehensively synthesize the existing literature on online PNIs, by focusing on the characteristics of interventions and studies in this area. Inconsistency in reporting the country of publication, population characteristics, duration and frequency of interventions, and a lack of the use of underlying theories and working mechanisms to inform intervention development, provide guidance for the reporting of future online PNIs. ", doi="10.2196/50307", url="https://medinform.jmir.org/2024/1/e50307" } @Article{info:doi/10.2196/56361, author="Zha, Bowen and Cai, Angshu and Wang, Guiqi", title="Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review", journal="JMIR Med Inform", year="2024", month="Jul", day="15", volume="12", pages="e56361", keywords="endoscopy", keywords="artificial intelligence", keywords="umbrella review", keywords="meta-analyses", keywords="AI", keywords="diagnostic", keywords="researchers", keywords="researcher", keywords="tools", keywords="tool", keywords="assessment", abstract="Background: Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. Objective: This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy. Methods: Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. Results: A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97\%, while the invasion depth of colon neoplasia, with 71\%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98\%, while the gastrointestinal stromal tumor, with only 80\%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. Conclusions: AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness. ", doi="10.2196/56361", url="https://medinform.jmir.org/2024/1/e56361" } @Article{info:doi/10.2196/54811, author="Wu, Yuxuan and Wu, Mingyue and Wang, Changyu and Lin, Jie and Liu, Jialin and Liu, Siru", title="Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis", journal="JMIR Med Inform", year="2024", month="Jun", day="12", volume="12", pages="e54811", keywords="clinical decision support system", keywords="electronic health record", keywords="electronic medical record", keywords="health information technology", keywords="alert fatigue", keywords="burnout", keywords="health care professionals", keywords="health care service", keywords="EHR", keywords="systematic review", keywords="meta-analysis", keywords="health information system", keywords="clinician burnout", keywords="health informatics", abstract="Background: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. Objective: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. Methods: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. Results: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4\% (95\% CI 37.5\%-43.2\%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95\% CI 2.31-2.57). Conclusions: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display\_record.php?ID=CRD42021281173 ", doi="10.2196/54811", url="https://medinform.jmir.org/2024/1/e54811", url="http://www.ncbi.nlm.nih.gov/pubmed/38865188" } @Article{info:doi/10.2196/51822, author="Lefkovitz, Ilana and Walsh, Samantha and Blank, J. Leah and Jett{\'e}, Nathalie and Kummer, R. Benjamin", title="Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review", journal="JMIR Neurotech", year="2024", month="May", day="22", volume="3", pages="e51822", keywords="natural language processing", keywords="NLP", keywords="unstructured", keywords="text", keywords="machine learning", keywords="deep learning", keywords="neurology", keywords="headache disorders", keywords="migraine", keywords="Parkinson disease", keywords="cerebrovascular disease", keywords="stroke", keywords="transient ischemic attack", keywords="epilepsy", keywords="multiple sclerosis", keywords="cardiovascular", keywords="artificial intelligence", keywords="Parkinson", keywords="neurological", keywords="neurological disorder", keywords="scoping review", keywords="diagnosis", keywords="treatment", keywords="prediction", abstract="Background: Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear. Objective: We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders. Methods: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study. Results: In total, 916 studies were identified, of which 41 (4.5\%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49\%), followed by epilepsy (n=10, 24\%), Alzheimer disease (n=6, 15\%), and multiple sclerosis (n=5, 12\%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49\%), followed by disease phenotyping (n=17, 41\%), prognostication (n=9, 22\%), and treatment (n=4, 10\%). In total, 18 (44\%) studies used only machine learning approaches, 6 (15\%) used only rule-based methods, and 17 (41\%) used both. Conclusions: We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry. Trial Registration: Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display\_record.php?RecordID=228703 ", doi="10.2196/51822", url="https://neuro.jmir.org/2024/1/e51822" } @Article{info:doi/10.2196/50117, author="Tabashum, Thasina and Snyder, Cooper Robert and O'Brien, K. Megan and Albert, V. Mark", title="Machine Learning Models for Parkinson Disease: Systematic Review", journal="JMIR Med Inform", year="2024", month="May", day="17", volume="12", pages="e50117", keywords="Parkinson disease", keywords="machine learning", keywords="systematic review", keywords="deep learning", keywords="clinical adoption", keywords="validation techniques", keywords="PRISMA", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", abstract="Background: With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems. Objective: To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression. Methods: We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: ``Parkinson's'' AND (``ML'' OR ``prediction'' OR ``classification'' OR ``detection'' or ``artificial intelligence'' OR ``AI''). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms. Results: Only 65.5\% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54\%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9\% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4\% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15\% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results. Conclusions: This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD. ", doi="10.2196/50117", url="https://medinform.jmir.org/2024/1/e50117" } @Article{info:doi/10.2196/57026, author="Zhang, Jinbo and Yang, Pingping and Zeng, Lu and Li, Shan and Zhou, Jiamei", title="Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review", journal="JMIR Med Inform", year="2024", month="May", day="14", volume="12", pages="e57026", keywords="artificial intelligence", keywords="machine learning", keywords="ventilator-associated pneumonia", keywords="prediction", keywords="scoping", keywords="PRISMA", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", abstract="Background: Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patients' treatments and prognoses. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective: This paper reviews VAP prediction models that are based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods: A scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The Wanfang database, the Chinese Biomedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase were searched to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. The data extracted from the included studies were synthesized narratively. Results: Of the 137 publications retrieved, 11 were included in this scoping review. The included studies reported the use of AI for predicting VAP. All 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were the primary sources of data for model building (studies: 6/11, 55\%), and 5 studies had sample sizes of <1000. Machine learning was the primary algorithm for studying the VAP prediction models. However, deep learning and large language models were not used to construct VAP prediction models. The random forest model was the most commonly used model (studies: 5/11, 45\%). All studies only performed internal validations, and none of them addressed how to implement and apply the final model in real-life clinical settings. Conclusions: This review presents an overview of studies that used AI to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide indispensable tools for VAP risk prediction in the future. However, the current research is in the model construction and validation stage, and the implementation of and guidance for clinical VAP prediction require further research. ", doi="10.2196/57026", url="https://medinform.jmir.org/2024/1/e57026" } @Article{info:doi/10.2196/53535, author="Palojoki, Sari and Lehtonen, Lasse and Vuokko, Riikka", title="Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability", journal="JMIR Med Inform", year="2024", month="Apr", day="25", volume="12", pages="e53535", keywords="electronic health record", keywords="health records", keywords="EHR", keywords="EHRs", keywords="semantic", keywords="health care data", keywords="semantic interoperability", keywords="interoperability", keywords="standardize", keywords="standardized", keywords="standardization", keywords="cross-border data exchange", keywords="systematic review", keywords="synthesis", keywords="syntheses", keywords="review methods", keywords="review methodology", keywords="search", keywords="searches", keywords="searching", keywords="systematic", keywords="data exchange", keywords="information sharing", keywords="ontology", keywords="ontologies", keywords="terminology", keywords="terminologies", keywords="standard", keywords="standards", keywords="classification", keywords="PRISMA", keywords="data sharing", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", abstract="Background: Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025. Objective: To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development? Methods: Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research. Results: Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43\%), increasing the quality of care (n=4, 29\%), and enhancing clinical data use and reuse for varied purposes (n=4, 29\%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57\%). Enhancing health data quality through standardization (n=5, 36\%) and developing EHR-integrated tools based on interoperable data (n=1, 7\%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers). Conclusions: When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes. ", doi="10.2196/53535", url="https://medinform.jmir.org/2024/1/e53535" } @Article{info:doi/10.2196/50048, author="Singhal, Aditya and Neveditsin, Nikita and Tanveer, Hasnaat and Mago, Vijay", title="Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review", journal="JMIR Med Inform", year="2024", month="Apr", day="3", volume="12", pages="e50048", keywords="fairness, accountability, transparency, and ethics", keywords="artificial intelligence", keywords="social media", keywords="health care", abstract="Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research. ", doi="10.2196/50048", url="https://medinform.jmir.org/2024/1/e50048", url="http://www.ncbi.nlm.nih.gov/pubmed/38568737" } @Article{info:doi/10.2196/46699, author="Mannoubi, Choumous and Kairy, Dahlia and Menezes, Vanessa Karla and Desroches, Sophie and Layani, Geraldine and Vachon, Brigitte", title="The Key Digital Tool Features of Complex Telehealth Interventions Used for Type 2 Diabetes Self-Management and Monitoring With Health Professional Involvement: Scoping Review", journal="JMIR Med Inform", year="2024", month="Mar", day="13", volume="12", pages="e46699", keywords="telehealth", keywords="telemedicine", keywords="telenutrition", keywords="telemonitoring", keywords="electronic coaching", keywords="e-coaching", keywords="scoping review", keywords="type 2 diabetes", keywords="prediabetes", keywords="diabetes management", keywords="diabetes self-management", keywords="mobile phone", abstract="Background: Therapeutic education and patient self-management are crucial in diabetes prevention and treatment. Improving diabetes self-management requires multidisciplinary team intervention, nutrition education that facilitates self-management, informed decision-making, and the organization and delivery of appropriate health care services. The emergence of telehealth services has provided the public with various tools for educating themselves and for evaluating, monitoring, and improving their health and nutrition-related behaviors. Combining health technologies with clinical expertise, social support, and health professional involvement could help persons living with diabetes improve their disease self-management skills and prevent its long-term consequences. Objective: This scoping review's primary objective was to identify the key digital tool features of complex telehealth interventions used for type 2 diabetes or prediabetes self-management and monitoring with health professional involvement that help improve health outcomes. A secondary objective was to identify how these key features are developed and combined. Methods: A 5-step scoping review methodology was used to map relevant literature published between January 1, 2010 and March 31, 2022. Electronic searches were performed in the MEDLINE, CINAHL, and Embase databases. The searches were limited to scientific publications in English and French that either described the conceptual development of a complex telehealth intervention that combined self-management and monitoring with health professional involvement or evaluated its effects on the therapeutic management of patients with type 2 diabetes or prediabetes. Three reviewers independently identified the articles and extracted the data. Results: The results of 42 studies on complex telehealth interventions combining diabetes self-management and monitoring with the involvement of at least 1 health professional were synthesized. The health professionals participating in these studies were physicians, dietitians, nurses, and psychologists. The digital tools involved were smartphone apps or web-based interfaces that could be used with medical devices. We classified the features of these technologies into eight categories, depending on the intervention objective: (1) monitoring of glycemia levels, (2) physical activity monitoring, (3) medication monitoring, (4) diet monitoring, (5) therapeutic education, (6) health professional support, (7) other health data monitoring, and (8) health care management. The patient-logged data revealed behavior patterns that should be modified to improve health outcomes. These technologies, used with health professional involvement, patient self-management, and therapeutic education, translate into better control of glycemia levels and the adoption of healthier lifestyles. Likewise, they seem to improve monitoring by health professionals and foster multidisciplinary collaboration through data sharing and the development of more concise automatically generated reports. Conclusions: This scoping review synthesizes multiple studies that describe the development and evaluation of complex telehealth interventions used in combination with health professional support. It suggests that combining different digital tools that incorporate diabetes self-management and monitoring features with a health professional's advice and interaction results in more effective interventions and outcomes. ", doi="10.2196/46699", url="https://medinform.jmir.org/2024/1/e46699", url="http://www.ncbi.nlm.nih.gov/pubmed/38477979" } @Article{info:doi/10.2196/51560, author="Declerck, Jens and Kalra, Dipak and Vander Stichele, Robert and Coorevits, Pascal", title="Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews", journal="JMIR Med Inform", year="2024", month="Mar", day="6", volume="12", pages="e51560", keywords="data quality", keywords="data quality dimensions", keywords="data quality assessment", keywords="secondary use", keywords="data quality framework", keywords="fit for purpose", abstract="Background: Health care has not reached the full potential of the secondary use of health data because of---among other issues---concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. Objective: This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. Methods: A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. Results: A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. Conclusions: The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions. ", doi="10.2196/51560", url="https://medinform.jmir.org/2024/1/e51560", url="http://www.ncbi.nlm.nih.gov/pubmed/38446534" } @Article{info:doi/10.2196/50642, author="Pigat, Lena and Geisler, P. Benjamin and Sheikhalishahi, Seyedmostafa and Sander, Julia and Kaspar, Mathias and Schmutz, Maximilian and Rohr, Olaf Sven and Wild, Mathis Carl and Goss, Sebastian and Zaghdoudi, Sarra and Hinske, Christian Ludwig", title="Predicting Hypoxia Using Machine Learning: Systematic Review", journal="JMIR Med Inform", year="2024", month="Feb", day="2", volume="12", pages="e50642", keywords="artificial intelligence", keywords="machine learning", keywords="hypoxia", keywords="hypoxemia", keywords="anoxia", keywords="hypoxic", keywords="deterioration", keywords="oxygen", keywords="prediction", keywords="systematic review", keywords="review methods", keywords="review methodology", keywords="systematic", keywords="hospital", keywords="predict", keywords="predictive", abstract="Background: Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration. Objective: This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population. Methods: A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83\%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67\%) using a long short-term memory algorithm. Conclusions: Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance. Trial Registration: PROSPERO CRD42023381710; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=381710 ", doi="10.2196/50642", url="https://medinform.jmir.org/2024/1/e50642" } @Article{info:doi/10.2196/47701, author="Aldaghi, Tahmineh and Muzik, Jan", title="Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review", journal="JMIR Med Inform", year="2024", month="Feb", day="1", volume="12", pages="e47701", keywords="analytical hierarchy process", keywords="diabetes management", keywords="diabetes recognition", keywords="glucose management", keywords="multi-criteria decision making", keywords="technique for order of preference by similarity to ideal solution", keywords="decision support", keywords="diabetes", keywords="diabetic", keywords="glucose", keywords="blood sugar", keywords="review methodology", keywords="systematic review", keywords="decision making", keywords="self-management", keywords="digital health tool", abstract="Background: Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques. Objective: The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field. Methods: The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined. Results: The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications). Conclusions: Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges. ", doi="10.2196/47701", url="https://medinform.jmir.org/2024/1/e47701", url="http://www.ncbi.nlm.nih.gov/pubmed/38300703" } @Article{info:doi/10.2196/42477, author="Bazoge, Adrien and Morin, Emmanuel and Daille, B{\'e}atrice and Gourraud, Pierre-Antoine", title="Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review", journal="JMIR Med Inform", year="2023", month="Dec", day="15", volume="11", pages="e42477", keywords="natural language processing", keywords="data warehousing", keywords="clinical data warehouse", keywords="artificial intelligence", keywords="AI", abstract="Background: In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective: The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods: This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results: We identified 1353 articles, of which 194 (14.34\%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7\%) and the identification of patients (51/194, 26.3\%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4\%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2\%) and deep learning (38/232, 16.4\%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9\%). Conclusions: CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice. ", doi="10.2196/42477", url="https://medinform.jmir.org/2023/1/e42477", url="http://www.ncbi.nlm.nih.gov/pubmed/38100200" } @Article{info:doi/10.2196/44639, author="Keszthelyi, Daniel and Gaudet-Blavignac, Christophe and Bjelogrlic, Mina and Lovis, Christian", title="Patient Information Summarization in Clinical Settings: Scoping Review", journal="JMIR Med Inform", year="2023", month="Nov", day="28", volume="11", pages="e44639", keywords="summarization", keywords="electronic health records", keywords="EHR", keywords="medical record", keywords="visualization", keywords="dashboard", keywords="natural language processing", abstract="Background: Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field. Objective: This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties. Methods: A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework ``collect---synthesize---communicate'' referring to information gathering from data, its synthesis, and communication to the end user. Results: Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1\% (59/128) of papers, text in 41.4\% (53/128) of articles, and both in 10.2\% (13/128) of papers. Using the proposed framework, 42.2\% (54/128) of the records contributed to information collection, 27.3\% (35/128) contributed to information synthesis, and 46.1\% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8\% and 113/128, 88.3\%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8\%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6\%) reports described a system deployed in clinical settings. Conclusions: The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the ``collect---synthesize---communicate'' framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary. ", doi="10.2196/44639", url="https://medinform.jmir.org/2023/1/e44639", url="http://www.ncbi.nlm.nih.gov/pubmed/38015588" } @Article{info:doi/10.2196/47052, author="Ye, Jiancheng and Xiong, Shangzhi and Wang, Tengyi and Li, Jingyi and Cheng, Nan and Tian, Maoyi and Yang, Yang", title="The Roles of Electronic Health Records for Clinical Trials in Low- and Middle-Income Countries: Scoping Review", journal="JMIR Med Inform", year="2023", month="Nov", day="22", volume="11", pages="e47052", keywords="electronic health records", keywords="clinical trials", keywords="low- and middle-income countries", abstract="Background: Clinical trials are a crucial element in advancing medical knowledge and developing new treatments by establishing the evidence base for safety and therapeutic efficacy. However, the success of these trials depends on various factors, including trial design, project planning, research staff training, and adequate sample size. It is also crucial to recruit participants efficiently and retain them throughout the trial to ensure timely completion. Objective: There is an increasing interest in using electronic health records (EHRs)---a widely adopted tool in clinical practice---for clinical trials. This scoping review aims to understand the use of EHR in supporting the conduct of clinical trials in low- and middle-income countries (LMICs) and to identify its strengths and limitations. Methods: A comprehensive search was performed using 5 databases: MEDLINE, Embase, Scopus, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature. We followed the latest version of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline to conduct this review. We included clinical trials that used EHR at any step, conducted a narrative synthesis of the included studies, and mapped the roles of EHRs into the life cycle of a clinical trial. Results: A total of 30 studies met the inclusion criteria: 13 were randomized controlled trials, 3 were cluster randomized controlled trials, 12 were quasi-experimental studies, and 2 were feasibility pilot studies. Most of the studies addressed infectious diseases (15/30, 50\%), with 80\% (12/15) of them about HIV or AIDS and another 40\% (12/30) focused on noncommunicable diseases. Our synthesis divided the roles of EHRs into 7 major categories: participant identification and recruitment (12/30, 40\%), baseline information collection (6/30, 20\%), intervention (8/30, 27\%), fidelity assessment (2/30, 7\%), primary outcome assessment (24/30, 80\%), nonprimary outcome assessment (13/30, 43\%), and extended follow-up (2/30, 7\%). None of the studies used EHR for participant consent and randomization. Conclusions: Despite the enormous potential of EHRs to increase the effectiveness and efficiency of conducting clinical trials in LMICs, challenges remain. Continued exploration of the appropriate uses of EHRs by navigating their strengths and limitations to ensure fitness for use is necessary to better understand the most optimal uses of EHRs for conducting clinical trials in LMICs. ", doi="10.2196/47052", url="https://medinform.jmir.org/2023/1/e47052", url="http://www.ncbi.nlm.nih.gov/pubmed/37991820" } @Article{info:doi/10.2196/47833, author="Liu, Kui and Li, Linyi and Ma, Yifei and Jiang, Jun and Liu, Zhenhua and Ye, Zichen and Liu, Shuang and Pu, Chen and Chen, Changsheng and Wan, Yi", title="Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis", journal="JMIR Med Inform", year="2023", month="Nov", day="20", volume="11", pages="e47833", keywords="machine learning", keywords="diabetes", keywords="hypoglycemia", keywords="blood glucose", keywords="blood glucose management", abstract="Background: Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. Objective: In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. Methods: PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. Results: In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95\% CI 5.7-12.0) and 0.31 (95\% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95\% CI 1.6-3.7) and 0.37 (95\% CI 0.29-0.46), respectively, for detecting hypoglycemia. Conclusions: Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. Trial Registration: PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=375250 ", doi="10.2196/47833", url="https://medinform.jmir.org/2023/1/e47833", url="http://www.ncbi.nlm.nih.gov/pubmed/37983072" } @Article{info:doi/10.2196/47445, author="Ali, Hazrat and Qureshi, Rizwan and Shah, Zubair", title="Artificial Intelligence--Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review", journal="JMIR Med Inform", year="2023", month="Nov", day="17", volume="11", pages="e47445", keywords="artificial intelligence", keywords="AI", keywords="brain cancer", keywords="brain tumor", keywords="medical imaging", keywords="segmentation", keywords="vision transformers", abstract="Background: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. Objective: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. Methods: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. Results: Of the 736 retrieved studies, 22 (3\%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer--based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. Conclusions: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer. ", doi="10.2196/47445", url="https://medinform.jmir.org/2023/1/e47445", url="http://www.ncbi.nlm.nih.gov/pubmed/37976086" } @Article{info:doi/10.2196/48693, author="De Rosario, Helios and Pitarch-Corresa, Salvador and Pedrosa, Ignacio and Vidal-Pedr{\'o}s, Marina and de Otto-L{\'o}pez, Beatriz and Garc{\'i}a-Mieres, Helena and {\'A}lvarez-Rodr{\'i}guez, Lydia", title="Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review", journal="JMIR Med Inform", year="2023", month="Sep", day="6", volume="11", pages="e48693", keywords="stroke", keywords="natural language processing", keywords="artificial intelligence", keywords="scoping review", keywords="scoping", keywords="review methods", keywords="review methodology", keywords="NLP", keywords="cardiovascular", keywords="machine learning", keywords="deep learning", abstract="Background: Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. Objective: The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. Methods: Data were extracted from Scopus and Medline through PubMed, using the keywords ``natural language processing'' and ``stroke.'' Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. Results: Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. Conclusions: Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images. ", doi="10.2196/48693", url="https://medinform.jmir.org/2023/1/e48693", url="http://www.ncbi.nlm.nih.gov/pubmed/37672328" } @Article{info:doi/10.2196/45013, author="Inau, Thea Esther and Sack, Jean and Waltemath, Dagmar and Zeleke, Alamirrew Atinkut", title="Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review", journal="J Med Internet Res", year="2023", month="Aug", day="28", volume="25", pages="e45013", keywords="data stewardship", keywords="findable, accessible, interoperable, and reusable data principles", keywords="FAIR data principles", keywords="health research", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", keywords="PRISMA", keywords="qualitative analysis", keywords="scoping review", keywords="information retrieval", keywords="health information exchange", abstract="Background: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains. Objective: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data. Methods: The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results: A total of 2.18\% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic. Conclusions: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID): RR2-10.2196/22505 ", doi="10.2196/45013", url="https://www.jmir.org/2023/1/e45013", url="http://www.ncbi.nlm.nih.gov/pubmed/37639292" } @Article{info:doi/10.2196/48297, author="Balch, A. Jeremy and Ruppert, M. Matthew and Loftus, J. Tyler and Guan, Ziyuan and Ren, Yuanfang and Upchurch, R. Gilbert and Ozrazgat-Baslanti, Tezcan and Rashidi, Parisa and Bihorac, Azra", title="Machine Learning--Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review", journal="JMIR Med Inform", year="2023", month="Aug", day="24", volume="11", pages="e48297", keywords="ontologies", keywords="clinical decision support system", keywords="Fast Healthcare Interoperability Resources", keywords="FHIR", keywords="machine learning", keywords="ontology", keywords="interoperability", keywords="interoperable", keywords="decision support", keywords="information systems", keywords="review methodology", keywords="review methods", keywords="scoping review", keywords="clinical informatics", abstract="Background: Machine learning--enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective: This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods: Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results: A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions: Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations. ", doi="10.2196/48297", url="https://medinform.jmir.org/2023/1/e48297" } @Article{info:doi/10.2196/44842, author="Nan, Jingwen and Xu, Li-Qun", title="Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review", journal="JMIR Med Inform", year="2023", month="Aug", day="21", volume="11", pages="e44842", keywords="Health level 7 Fast Healthcare Interoperability Resources", keywords="HL7 FHIR", keywords="interoperability", keywords="literature review", keywords="practice guideline", keywords="mobile phone", abstract="Background: With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. Objective: This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. Methods: We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. Results: The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. Conclusions: The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services. ", doi="10.2196/44842", url="https://medinform.jmir.org/2023/1/e44842", url="http://www.ncbi.nlm.nih.gov/pubmed/37603388" } @Article{info:doi/10.2196/45116, author="Ahmadi, Najia and Zoch, Michele and Kelbert, Patricia and Noll, Richard and Schaaf, Jannik and Wolfien, Markus and Sedlmayr, Martin", title="Methods Used in the Development of Common Data Models for Health Data: Scoping Review", journal="JMIR Med Inform", year="2023", month="Aug", day="3", volume="11", pages="e45116", keywords="common data model", keywords="common data elements", keywords="health data", keywords="electronic health record", keywords="Observational Medical Outcomes Partnership", keywords="stakeholder involvement", keywords="Data harmonisation", keywords="Interoperability", keywords="Standardized Data Repositories", keywords="Suggestive Development Process", keywords="Healthcare", keywords="Medical Informatics", keywords="", abstract="Background: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems. Objective: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. Methods: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. Results: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. Conclusions: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future. ", doi="10.2196/45116", url="https://medinform.jmir.org/2023/1/e45116", url="http://www.ncbi.nlm.nih.gov/pubmed/37535410" } @Article{info:doi/10.2196/41153, author="Diaz, Claudio and Caillaud, Corinne and Yacef, Kalina", title="Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review", journal="JMIR Med Inform", year="2023", month="Mar", day="6", volume="11", pages="e41153", keywords="activity tracker", keywords="wearable electronic devices", keywords="fitness trackers", keywords="data mining", keywords="artificial intelligence", keywords="health", keywords="education", keywords="behavior change", keywords="physical activity", keywords="wearable devices", keywords="trackers", keywords="health education", keywords="sensor data", abstract="Background: Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants' physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants' physical activity evolves. Objective: The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? Methods: The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. Results: All studies used accelerometers, sometimes in combination with another sensor (37\%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58\%) and analysis of physical activity behaviors (42\%). Conclusions: Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce. ", doi="10.2196/41153", url="https://medinform.jmir.org/2023/1/e41153", url="http://www.ncbi.nlm.nih.gov/pubmed/36877559" } @Article{info:doi/10.2196/44161, author="Zhang, Zhan and Bai, Enze and Joy, Karen and Ghelaa, Naressh Partth and Adelgais, Kathleen and Ozkaynak, Mustafa", title="Smart Glasses for Supporting Distributed Care Work: Systematic Review", journal="JMIR Med Inform", year="2023", month="Feb", day="28", volume="11", pages="e44161", keywords="smart glass", keywords="care coordination", keywords="telemedicine", keywords="distributed teamwork", keywords="mobile phone", abstract="Background: Over the past 2 decades, various desktop and mobile telemedicine systems have been developed to support communication and care coordination among distributed medical teams. However, in the hands-busy care environment, such technologies could become cumbersome because they require medical professionals to manually operate them. Smart glasses have been gaining momentum because of their advantages in enabling hands-free operation and see-what-I-see video-based consultation. Previous research has tested this novel technology in different health care settings. Objective: The aim of this study was to review how smart glasses were designed, used, and evaluated as a telemedicine tool to support distributed care coordination and communication, as well as highlight the potential benefits and limitations regarding medical professionals' use of smart glasses in practice. Methods: We conducted a literature search in 6 databases that cover research within both health care and computer science domains. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to review articles. A total of 5865 articles were retrieved and screened by 3 researchers, with 21 (0.36\%) articles included for in-depth analysis. Results: All of the reviewed articles (21/21, 100\%) used off-the-shelf smart glass device and videoconferencing software, which had a high level of technology readiness for real-world use and deployment in care settings. The common system features used and evaluated in these studies included video and audio streaming, annotation, augmented reality, and hands-free interactions. These studies focused on evaluating the technical feasibility, effectiveness, and user experience of smart glasses. Although the smart glass technology has demonstrated numerous benefits and high levels of user acceptance, the reviewed studies noted a variety of barriers to successful adoption of this novel technology in actual care settings, including technical limitations, human factors and ergonomics, privacy and security issues, and organizational challenges. Conclusions: User-centered system design, improved hardware performance, and software reliability are needed to realize the potential of smart glasses. More research is needed to examine and evaluate medical professionals' needs, preferences, and perceptions, as well as elucidate how smart glasses affect the clinical workflow in complex care environments. Our findings inform the design, implementation, and evaluation of smart glasses that will improve organizational and patient outcomes. ", doi="10.2196/44161", url="https://medinform.jmir.org/2023/1/e44161", url="http://www.ncbi.nlm.nih.gov/pubmed/36853760" } @Article{info:doi/10.2196/39876, author="Zhao, Yang and Tsubota, Tadashi", title="The Current Status of Secondary Use of Claims, Electronic Medical Records, and Electronic Health Records in Epidemiology in Japan: Narrative Literature Review", journal="JMIR Med Inform", year="2023", month="Feb", day="14", volume="11", pages="e39876", keywords="claims", keywords="electronic medical records", keywords="EMRs", keywords="electronic health records", keywords="EHRs", keywords="epidemiology", keywords="narrative literature review", abstract="Background: Real-world data, such as claims, electronic medical records (EMRs), and electronic health records (EHRs), are increasingly being used in clinical epidemiology. Understanding the current status of existing approaches can help in designing high-quality epidemiological studies. Objective: We conducted a comprehensive narrative literature review to clarify the secondary use of claims, EMRs, and EHRs in clinical epidemiology in Japan. Methods: We searched peer-reviewed publications in PubMed from January 1, 2006, to June 30, 2021 (the date of search), which met the following 3 inclusion criteria: involvement of claims, EMRs, EHRs, or medical receipt data; mention of Japan; and published from January 1, 2006, to June 30, 2021. Eligible articles that met any of the following 6 exclusion criteria were filtered: review articles; non--disease-related articles; articles in which the Japanese population is not the sample; articles without claims, EMRs, or EHRs; full text not available; and articles without statistical analysis. Investigations of the titles, abstracts, and full texts of eligible articles were conducted automatically or manually, from which 7 categories of key information were collected. The information included organization, study design, real-world data type, database, disease, outcome, and statistical method. Results: A total of 620 eligible articles were identified for this narrative literature review. The results of the 7 categories suggested that most of the studies were conducted by academic institutes (n=429); the cohort study was the primary design that longitudinally measured outcomes of proper patients (n=533); 594 studies used claims data; the use of databases was concentrated in well-known commercial and public databases; infections (n=105), cardiovascular diseases (n=100), neoplasms (n=78), and nutritional and metabolic diseases (n=75) were the most studied diseases; most studies have focused on measuring treatment patterns (n=218), physiological or clinical characteristics (n=184), and mortality (n=137); and multivariate models were commonly used (n=414). Most (375/414, 90.6\%) of these multivariate modeling studies were performed for confounder adjustment. Logistic regression was the first choice for assessing many of the outcomes, with the exception of hospitalization or hospital stay and resource use or costs, for both of which linear regression was commonly used. Conclusions: This literature review provides a good understanding of the current status and trends in the use of claims, EMRs, and EHRs data in clinical epidemiology in Japan. The results demonstrated appropriate statistical methods regarding different outcomes, Japan-specific trends of disease areas, and the lack of use of artificial intelligence techniques in existing studies. In the future, a more precise comparison of relevant domestic research with worldwide research will be conducted to clarify the Japan-specific status and challenges. ", doi="10.2196/39876", url="https://medinform.jmir.org/2023/1/e39876", url="http://www.ncbi.nlm.nih.gov/pubmed/36787161" } @Article{info:doi/10.2196/43750, author="Vuokko, Riikka and Vakkuri, Anne and Palojoki, Sari", title="Systematized Nomenclature of Medicine--Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review", journal="JMIR Med Inform", year="2023", month="Feb", day="6", volume="11", pages="e43750", keywords="clinical", keywords="electronic health record", keywords="EHR", keywords="review method", keywords="literature review", keywords="SNOMED CT", keywords="Systematized Nomenclature for Medicine", keywords="use case", keywords="terminology", keywords="terminologies", keywords="SNOMED", abstract="Background: The Systematized Medical Nomenclature for Medicine--Clinical Terminology (SNOMED CT) is a clinical terminology system that provides a standardized and scientifically validated way of representing clinical information captured by clinicians. It can be integrated into electronic health records (EHRs) to increase the possibilities for effective data use and ensure a better quality of documentation that supports continuity of care, thus enabling better quality in the care process. Even though SNOMED CT consists of extensively studied clinical terminology, previous research has repeatedly documented a lack of scientific evidence for SNOMED CT in the form of reported clinical use cases in electronic health record systems. Objective: The aim of this study was to explore evidence in previous literature reviews of clinical use cases of SNOMED CT integrated into EHR systems or other clinical applications during the last 5 years of continued development. The study sought to identify the main clinical use purposes, use phases, and key clinical benefits documented in SNOMED CT use cases. Methods: The Cochrane review protocol was applied for the study design. The application of the protocol was modified step-by-step to fit the research problem by first defining the search strategy, identifying the articles for the review by isolating the exclusion and inclusion criteria for assessing the search results, and lastly, evaluating and summarizing the review results. Results: In total, 17 research articles illustrating SNOMED CT clinical use cases were reviewed. The use purpose of SNOMED CT was documented in all the articles, with the terminology as a standard in EHR being the most common (8/17). The clinical use phase was documented in all the articles. The most common category of use phases was SNOMED CT in development (6/17). Core benefits achieved by applying SNOMED CT in a clinical context were identified by the researchers. These were related to terminology use outcomes, that is, to data quality in general or to enabling a consistent way of indexing, storing, retrieving, and aggregating clinical data (8/17). Additional benefits were linked to the productivity of coding or to advances in the quality and continuity of care. Conclusions: While the SNOMED CT use categories were well supported by previous research, this review demonstrates that further systematic research on clinical use cases is needed to promote the scalability of the review results. To achieve the best out-of-use case reports, more emphasis is suggested on describing the contextual factors, such as the electronic health care system and the use of previous frameworks to enable comparability of results. A lesson to be drawn from our study is that SNOMED CT is essential for structuring clinical data; however, research is needed to gather more evidence of how SNOMED CT benefits clinical care and patient safety. ", doi="10.2196/43750", url="https://medinform.jmir.org/2023/1/e43750", url="http://www.ncbi.nlm.nih.gov/pubmed/36745498" } @Article{info:doi/10.2196/43053, author="Jing, Xia and Min, Hua and Gong, Yang and Biondich, Paul and Robinson, David and Law, Timothy and Nohr, Christian and Faxvaag, Arild and Rennert, Lior and Hubig, Nina and Gimbel, Ronald", title="Ontologies Applied in Clinical Decision Support System Rules: Systematic Review", journal="JMIR Med Inform", year="2023", month="Jan", day="19", volume="11", pages="e43053", keywords="clinical decision support system rules", keywords="clinical decision support systems", keywords="interoperability", keywords="ontology", keywords="Semantic Web technology", abstract="Background: Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. Objective: Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. Methods: The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing \& Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. Results: CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. Conclusions: Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules. ", doi="10.2196/43053", url="https://medinform.jmir.org/2023/1/e43053", url="http://www.ncbi.nlm.nih.gov/pubmed/36534739" } @Article{info:doi/10.2196/40469, author="Ward, Kanesha and Vagholkar, Sanjyot and Sakur, Fareeya and Khatri, Nafees Neha and Lau, S. Annie Y.", title="Visit Types in Primary Care With Telehealth Use During the COVID-19 Pandemic: Systematic Review", journal="JMIR Med Inform", year="2022", month="Nov", day="28", volume="10", number="11", pages="e40469", keywords="primary care", keywords="general practitioners", keywords="telehealth", keywords="telemedicine", keywords="COVID-19", keywords="remote consultation", keywords="video consultation", keywords="eHealth", abstract="Background: Telehealth was rapidly incorporated into primary care during the COVID-19 pandemic. However, there is limited evidence on which primary care visits used telehealth. Objective: The objective of this study was to conduct a systematic review to assess what visit types in primary care with use of telehealth during the COVID-19 pandemic were reported; for each visit type identified in primary care, under what circumstances telehealth was suitable; and reported benefits and drawbacks of using telehealth in primary care during the COVID-19 pandemic. Methods: This study was a systematic review using narrative synthesis. Studies were obtained from four databases (Ovid [MEDLINE], CINAHL Complete, PDQ-Evidence, and ProQuest) and gray literature (NSW Health, Royal Australian College of General Practitioners guidelines, and World Health Organization guidelines). In total, 3 independent reviewers screened studies featuring telehealth use during the COVID-19 pandemic in primary care. Levels of evidence were assessed according to the Grading of Recommendations Assessment, Development, and Evaluation. Critical appraisal was conducted using the Mixed Methods Appraisal Tool. Benefits and drawbacks of telehealth were assessed according to the National Quality Forum Telehealth Framework. Results: A total of 19 studies, predominately cross-sectional surveys or interviews (13/19, 68\%), were included. Seven primary care visit types were identified: chronic condition management (17/19, 89\%),?existing patients?(17/19, 89\%), medication management?(17/19, 89\%), new patients?(16/19, 84\%), mental health/behavioral management (15/19, 79\%), post--test result follow-up?(14/19, 74\%), and postdischarge follow-up?(7/19, 37\%).?Benefits and drawbacks of telehealth were reported across all visit types, with chronic condition management being one of the visits reporting the greatest use because of a pre-existing patient-provider relationship, established diagnosis, and lack of complex physical examinations. Both patients and clinicians reported benefits of telehealth, including improved convenience, focused discussions, and continuity of care despite social distancing. Reported drawbacks included technical barriers, impersonal interactions, and semi-established reimbursement models. Conclusions: Telehealth?was used for different visit types during the COVID-19 pandemic in primary care, with most visits for chronic condition management, existing patients, and medication management. Further research is required to validate our findings and explore the long-term impact of hybrid models of care for different visit types in primary care. Trial Registration: PROSPERO CRD42022312202; https://tinyurl.com/5n82znf4 ", doi="10.2196/40469", url="https://medinform.jmir.org/2022/11/e40469", url="http://www.ncbi.nlm.nih.gov/pubmed/36265039" } @Article{info:doi/10.2196/39542, author="Banguilan, Louise Kaila and Sonnenberg, Frank and Chen, Catherine", title="Physicians' Perspectives on Inpatient Portals: Systematic Review", journal="Interact J Med Res", year="2022", month="Nov", day="15", volume="11", number="2", pages="e39542", keywords="inpatient portals", keywords="personal health record", keywords="physician perspectives", keywords="patient portals", keywords="inpatients", keywords="technology", abstract="Background: Inpatient portals are online platforms that allow patients to access their personal health information and monitor their health while in the acute care setting. Despite their potential to improve quality of care and empower patients and families to participate in their treatment, adoption remains low. Outpatient portal studies have shown that physician endorsement can drive patients' adoption of these systems. Insights on physicians' perspectives on use of these platforms can help improve patient and physician satisfaction and inpatient portal uptake. Objective: The purpose of this systematic review is to better understand physicians' perspectives toward inpatient portals. Methods: A systematic literature review was conducted for studies published between 1994 and November 2021 using keywords for physicians' perspectives toward patient portals and personal health records. Databases included PubMed, MEDLINE, Web of Science, and Scopus. Articles solely focused on nonphysician clinicians or addressing only outpatient settings or shared notes were excluded from this review. Two reviewers performed title, abstract, and full-text screening independently. Bias assessment was performed using the JBI SUMARI Critical Appraisal Tool (Joanna Briggs Institute). Inductive thematic analysis was done based on themes reported by original authors. Data were synthesized using narrative synthesis and reported according to overarching themes. Results: In all, 4199 articles were collected and 9 included. All but 2 of the studies were conducted in the United States. Common themes identified were communication and privacy, portal functionality and patient use, and workflow. In studies where physicians had no prior patient portal experience, concerns were expressed about communication issues created by patients' access to laboratory results and potential impact on existing workflow. Concerns about negative communication impacts were not borne out in postimplementation studies. Conclusions: Physicians perceived inpatient portals to be beneficial to patients and saw improvement in communication as a result. This is consistent with outpatient studies and highlights the need to improve training on portal use and include physicians during the design process. Health care organizations and information technology entities can take steps to increasing clinician comfort. Physician concerns involving patient portal usage and managing patient expectations also need to be addressed. With improved clinician support, initial pessimism about communication and workload issues can be overcome. Limitations of this review include the small number of pre- and postimplementation studies found. This is also not a review of perspectives on open notes, which merits separate discussion. ", doi="10.2196/39542", url="https://www.i-jmr.org/2022/2/e39542", url="http://www.ncbi.nlm.nih.gov/pubmed/36378521" } @Article{info:doi/10.2196/36199, author="Abbasgholizadeh Rahimi, Samira and Cwintal, Michelle and Huang, Yuhui and Ghadiri, Pooria and Grad, Roland and Poenaru, Dan and Gore, Genevieve and Zomahoun, Vignon Herv{\'e} Tchala and L{\'e}gar{\'e}, France and Pluye, Pierre", title="Application of Artificial Intelligence in Shared Decision Making: Scoping Review", journal="JMIR Med Inform", year="2022", month="Aug", day="9", volume="10", number="8", pages="e36199", keywords="artificial intelligence", keywords="machine learning", keywords="shared decision making", keywords="patient-centered care", keywords="scoping review", abstract="Background: Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective: We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods: We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results: The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions: Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients' values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings. ", doi="10.2196/36199", url="https://medinform.jmir.org/2022/8/e36199", url="http://www.ncbi.nlm.nih.gov/pubmed/35943793" } @Article{info:doi/10.2196/35724, author="Vorisek, Nina Carina and Lehne, Moritz and Klopfenstein, Ines Sophie Anne and Mayer, Josephine Paula and Bartschke, Alexander and Haese, Thomas and Thun, Sylvia", title="Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review", journal="JMIR Med Inform", year="2022", month="Jul", day="19", volume="10", number="7", pages="e35724", keywords="Fast Healthcare Interoperability Resources", keywords="FHIR", keywords="interoperability", keywords="health research", keywords="health care", keywords="health information technology", keywords="research", keywords="clinical research", keywords="public health", keywords="epidemiology", abstract="Background: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as ``Public Health \& Research'' and ``Evidence-Based Medicine'' while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective: The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73\%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6\%, n=3) or did not specify their research domain (20\%, n=10). Studies used FHIR for data capture (29\%, n=14), standardization of data (41\%, n=20), analysis (12\%, n=6), recruitment (14\%, n=7), and consent management (4\%, n=2). Most (55\%, 27/49) of the studies had a generic approach, and 55\% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63\%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29\%, n=14), Logical Observation Identifiers Names and Codes (37\%, n=18), International Classification of Diseases 10th Revision (18\%, n=9), Observational Medical Outcomes Partnership common data model (12\%, n=6), and others (43\%, n=21). Only 4 (8\%) studies used a FHIR resource from the domain ``Public Health \& Research.'' Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions: Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research. ", doi="10.2196/35724", url="https://medinform.jmir.org/2022/7/e35724", url="http://www.ncbi.nlm.nih.gov/pubmed/35852842" } @Article{info:doi/10.2196/37365, author="Ali, Hazrat and Shah, Zubair", title="Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review", journal="JMIR Med Inform", year="2022", month="Jun", day="29", volume="10", number="6", pages="e37365", keywords="augmentation", keywords="artificial intelligence", keywords="COVID-19", keywords="diagnosis", keywords="generative adversarial networks", keywords="diagnostic", keywords="lung image", keywords="imaging", keywords="data augmentation", keywords="X-ray", keywords="CT scan", keywords="data scarcity", keywords="image data", keywords="neural network", keywords="clinical informatics", abstract="Background: Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective: This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods: A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as ``generative adversarial networks'' and ``GANs,'' and application keywords, such as ``COVID-19'' and ``coronavirus.'' The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results: This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74\%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51\%) studies used chest X-ray images, while 21 (37\%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82\%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4\%) studies. Conclusions: Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs' performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications. ", doi="10.2196/37365", url="https://medinform.jmir.org/2022/6/e37365", url="http://www.ncbi.nlm.nih.gov/pubmed/35709336" } @Article{info:doi/10.2196/36388, author="Huang, Jonathan and Galal, Galal and Etemadi, Mozziyar and Vaidyanathan, Mahesh", title="Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review", journal="JMIR Med Inform", year="2022", month="May", day="31", volume="10", number="5", pages="e36388", keywords="artificial intelligence", keywords="machine learning", keywords="race", keywords="bias", keywords="racial bias", keywords="scoping review", keywords="algorithm", keywords="algorithmic fairness", keywords="clinical machine learning", keywords="medical machine learning", keywords="fairness", keywords="assessment", keywords="model", keywords="diagnosis", keywords="outcome prediction", keywords="score prediction", keywords="prediction", keywords="mitigation", abstract="Background: Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear. Objective: Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included. Results: Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8\%) described a model in routine clinical use, 2 (17\%) examined prospectively validated clinical models, and the remaining 9 (75\%) described internally validated models. In addition, 8 (67\%) studies concluded that racial bias was present, 2 (17\%) concluded that it was not, and 2 (17\%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42\%), accuracy (4/12, 25\%), and disparate impact (2/12, 17\%). All 8 (67\%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors' chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them. Conclusions: The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias. ", doi="10.2196/36388", url="https://medinform.jmir.org/2022/5/e36388", url="http://www.ncbi.nlm.nih.gov/pubmed/35639450" } @Article{info:doi/10.2196/35293, author="Barboi, Cristina and Tzavelis, Andreas and Muhammad, NaQiyba Lutfiyya", title="Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature", journal="JMIR Med Inform", year="2022", month="May", day="31", volume="10", number="5", pages="e35293", keywords="artificial intelligence", keywords="machine learning", keywords="intensive care unit mortality", keywords="severity of illness models", abstract="Background: Severity of illness scores---Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Sequential Organ Failure Assessment---are current risk stratification and mortality prediction tools used in intensive care units (ICUs) worldwide. Developers of artificial intelligence or machine learning (ML) models predictive of ICU mortality use the severity of illness scores as a reference point when reporting the performance of these computational constructs. Objective: This study aimed to perform a literature review and meta-analysis of articles that compared binary classification ML models with the severity of illness scores that predict ICU mortality and determine which models have superior performance. This review intends to provide actionable guidance to clinicians on the performance and validity of ML models in supporting clinical decision-making compared with the severity of illness score models. Methods: Between December 15 and 18, 2020, we conducted a systematic search of PubMed, Scopus, Embase, and IEEE databases and reviewed studies published between 2000 and 2020 that compared the performance of binary ML models predictive of ICU mortality with the performance of severity of illness score models on the same data sets. We assessed the studies' characteristics, synthesized the results, meta-analyzed the discriminative performance of the ML and severity of illness score models, and performed tests of heterogeneity within and among studies. Results: We screened 461 abstracts, of which we assessed the full text of 66 (14.3\%) articles. We included in the review 20 (4.3\%) studies that developed 47 ML models based on 7 types of algorithms and compared them with 3 types of the severity of illness score models. Of the 20 studies, 4 (20\%) were found to have a low risk of bias and applicability in model development, 7 (35\%) performed external validation, 9 (45\%) reported on calibration, 12 (60\%) reported on classification measures, and 4 (20\%) addressed explainability. The discriminative performance of the ML-based models, which was reported as AUROC, ranged between 0.728 and 0.99 and between 0.58 and 0.86 for the severity of illness score--based models. We noted substantial heterogeneity among the reported models and considerable variation among the AUROC estimates for both ML and severity of illness score model types. Conclusions: ML-based models can accurately predict ICU mortality as an alternative to traditional scoring models. Although the range of performance of the ML models is superior to that of the severity of illness score models, the results cannot be generalized due to the high degree of heterogeneity. When presented with the option of choosing between severity of illness score or ML models for decision support, clinicians should select models that have been externally validated, tested in the practice environment, and updated to the patient population and practice environment. Trial Registration: PROSPERO CRD42021203871; https://tinyurl.com/28v2nch8 ", doi="10.2196/35293", url="https://medinform.jmir.org/2022/5/e35293", url="http://www.ncbi.nlm.nih.gov/pubmed/35639445" } @Article{info:doi/10.2196/35061, author="Ackermann, Khalia and Baker, Jannah and Festa, Marino and McMullan, Brendan and Westbrook, Johanna and Li, Ling", title="Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: Scoping Review", journal="JMIR Med Inform", year="2022", month="May", day="6", volume="10", number="5", pages="e35061", keywords="sepsis", keywords="early detection of disease", keywords="computerized clinical decision support", keywords="patient safety", keywords="electronic health records", keywords="sepsis care pathway", abstract="Background: Sepsis is a severe condition associated with extensive morbidity and mortality worldwide. Pediatric, neonatal, and maternal patients represent a considerable proportion of the sepsis burden. Identifying sepsis cases as early as possible is a key pillar of sepsis management and has prompted the development of sepsis identification rules and algorithms that are embedded in computerized clinical decision support (CCDS) systems. Objective: This scoping review aimed to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of pediatric, neonatal, and maternal inpatients at risk of sepsis. Methods: MEDLINE, Embase, CINAHL, Cochrane, Latin American and Caribbean Health Sciences Literature (LILACS), Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and ProQuest Dissertations and Theses Global (PQDT) were searched by using a search strategy that incorporated terms for sepsis, clinical decision support, and early detection. Title, abstract, and full-text screening was performed by 2 independent reviewers, who consulted a third reviewer as needed. One reviewer performed data charting with a sample of data. This was checked by a second reviewer and via discussions with the review team, as necessary. Results: A total of 33 studies were included in this review---13 (39\%) pediatric studies, 18 (55\%) neonatal studies, and 2 (6\%) maternal studies. All studies were published after 2011, and 27 (82\%) were published from 2017 onward. The most common outcome investigated in pediatric studies was the accuracy of sepsis identification (9/13, 69\%). Pediatric CCDS systems used different combinations of 18 diverse clinical criteria to detect sepsis across the 13 identified studies. In neonatal studies, 78\% (14/18) of the studies investigated the Kaiser Permanente early-onset sepsis risk calculator. All studies investigated sepsis treatment and management outcomes, with 83\% (15/18) reporting on antibiotics-related outcomes. Usability and cost-related outcomes were each reported in only 2 (6\%) of the 31 pediatric or neonatal studies. Both studies on maternal populations were short abstracts. Conclusions: This review found limited research investigating CCDS systems to support the early detection of sepsis among pediatric, neonatal, and maternal patients, despite the high burden of sepsis in these vulnerable populations. We have highlighted the need for a consensus definition for pediatric and neonatal sepsis and the study of usability and cost-related outcomes as critical areas for future research. International Registered Report Identifier (IRRID): RR2-10.2196/24899 ", doi="10.2196/35061", url="https://medinform.jmir.org/2022/5/e35061", url="http://www.ncbi.nlm.nih.gov/pubmed/35522467" } @Article{info:doi/10.2196/33842, author="He, Xuefei and Peng, Cheng and Xu, Yingxin and Zhang, Ye and Wang, Zhongqing", title="Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis", journal="JMIR Med Inform", year="2022", month="Apr", day="21", volume="10", number="4", pages="e33842", keywords="medical informatics", keywords="bibliometrics", keywords="VOSviewer", keywords="data visualization", abstract="Background: With the emerging information and communication technology, the field of medical informatics has dramatically evolved in health care and medicine. Thus, it is crucial to explore the global scientific research landscape on medical informatics. Objective: This study aims to present a visual form to clarify the overall scientific research trends of medical informatics in the past decade. Methods: A bibliometric analysis of data retrieved and extracted from the Web of Science Core Collection (WoSCC) database was performed to analyze global scientific research trends on medical informatics, including publication year, journals, authors, institutions, countries/regions, references, and keywords, from January 1, 2011, to December 31, 2020. Results: The data set recorded 34,742 articles related to medical informatics from WoSCC between 2011 and 2020. The annual global publications increased by 193.86\% from 1987 in 2011 to 5839 in 2020. Journal of Medical Internet Research (3600 publications and 63,932 citations) was the most productive and most highly cited journal in the field of medical informatics. David W Bates (99 publications), Harvard University (1161 publications), and the United States (12,927 publications) were the most productive author, institution, and country, respectively. The co-occurrence cluster analysis of high-frequency author keywords formed 4 clusters: (1) artificial intelligence in health care and medicine; (2) mobile health; (3) implementation and evaluation of electronic health records; (4) medical informatics technology application in public health. COVID-19, which ranked third in 2020, was the emerging theme of medical informatics. Conclusions: We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, and theme evolution through bibliometric analysis and visualization maps. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research. ", doi="10.2196/33842", url="https://medinform.jmir.org/2022/4/e33842", url="http://www.ncbi.nlm.nih.gov/pubmed/35451986" } @Article{info:doi/10.2196/33799, author="Yang, Xinyu and Mu, Dongmei and Peng, Hao and Li, Hua and Wang, Ying and Wang, Ping and Wang, Yue and Han, Siqi", title="Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review", journal="JMIR Med Inform", year="2022", month="Apr", day="20", volume="10", number="4", pages="e33799", keywords="electronic health records", keywords="artificial intelligence", keywords="neoplasms", keywords="machine learning", abstract="Background: With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care. Objective: The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care. Methods: Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes. Results: Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases. Conclusions: Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists. ", doi="10.2196/33799", url="https://medinform.jmir.org/2022/4/e33799", url="http://www.ncbi.nlm.nih.gov/pubmed/35442195" } @Article{info:doi/10.2196/33875, author="Sharifi-Heris, Zahra and Laitala, Juho and Airola, Antti and Rahmani, M. Amir and Bender, Miriam", title="Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review", journal="JMIR Med Inform", year="2022", month="Apr", day="20", volume="10", number="4", pages="e33875", keywords="preterm birth", keywords="prediction model", keywords="machine learning approach", keywords="artificial intelligence", abstract="Background: Preterm birth (PTB), a common pregnancy complication, is responsible for 35\% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50\% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs). Objective: This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach. Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported. Results: A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1\%) were screened by full text, resulting in 13 (1.8\%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models' characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Conclusions: Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set. ", doi="10.2196/33875", url="https://medinform.jmir.org/2022/4/e33875", url="http://www.ncbi.nlm.nih.gov/pubmed/35442214" } @Article{info:doi/10.2196/32578, author="Chew, Jocelyn Han Shi", title="The Use of Artificial Intelligence--Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations", journal="JMIR Med Inform", year="2022", month="Apr", day="13", volume="10", number="4", pages="e32578", keywords="chatbot", keywords="conversational agent", keywords="artificial intelligence", keywords="weight loss", keywords="obesity", keywords="overweight", keywords="natural language processing", keywords="sentiment analysis", keywords="machine learning", keywords="behavior change", keywords="mobile phone", abstract="Background: Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective: This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods: A scoping review was conducted using the 5-stage framework by Arksey and O'Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results: A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35\%) focused on using AI chatbots to promote both a healthy diet and exercise, 13\% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4\% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48\% (11/23) of the studies used only text-based AI chatbots, 52\% (12/23) operationalized AI chatbots through smartphones, and 39\% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87\%), motivational messages (18/23, 78\%), gamification (6/23, 26\%), and emotional support (6/23, 26\%). Study participants who experienced speech- and augmented reality--based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions: AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss. ", doi="10.2196/32578", url="https://medinform.jmir.org/2022/4/e32578", url="http://www.ncbi.nlm.nih.gov/pubmed/35416791" } @Article{info:doi/10.2196/26511, author="Maramba, Daniel Inocencio and Jones, Ray and Austin, Daniela and Edwards, Katie and Meinert, Edward and Chatterjee, Arunangsu", title="The Role of Health Kiosks: Scoping Review", journal="JMIR Med Inform", year="2022", month="Mar", day="29", volume="10", number="3", pages="e26511", keywords="kiosk", keywords="health systems", keywords="internet", keywords="review", keywords="online health information", keywords="telemonitoring", keywords="teleconsultation", keywords="consultation", keywords="telemedicine", keywords="behavior", keywords="promotion", keywords="health service", keywords="user experience", keywords="barrier", keywords="facilitator", keywords="remote consultation", keywords="mobile phone", abstract="Background: Health kiosks are publicly accessible computing devices that provide access to services, including health information provision, clinical measurement collection, patient self--check-in, telemonitoring, and teleconsultation. Although the increase in internet access and ownership of smart personal devices could make kiosks redundant, recent reports have predicted that the market will continue to grow. Objective: We seek to clarify the current and future roles of health kiosks by investigating the settings, roles, and clinical domains in which kiosks are used; whether usability evaluations of health kiosks are being reported, and if so, what methods are being used; and what the barriers and facilitators are for the deployment of kiosks. Methods: We conducted a scoping review using a bibliographic search of Google Scholar, PubMed, and Web of Science databases for studies and other publications between January 2009 and June 2020. Eligible papers described the implementation as primary studies, systematic reviews, or news and feature articles. Additional reports were obtained by manual searching and querying the key informants. For each article, we abstracted settings, purposes, health domains, whether the kiosk was opportunistic or integrated with a clinical pathway, and whether the kiosk included usability testing. We then summarized the data in frequency tables. Results: A total of 141 articles were included, of which 134 (95\%) were primary studies, and 7 (5\%) were reviews. Approximately 47\% (63/134) of the primary studies described kiosks in secondary care settings. Other settings included community (32/134, 23.9\%), primary care (24/134, 17.9\%), and pharmacies (8/134, 6\%). The most common roles of the health kiosks were providing health information (47/134, 35.1\%), taking clinical measurements (28/134, 20.9\%), screening (17/134, 12.7\%), telehealth (11/134, 8.2\%), and patient registration (8/134, 6.0\%). The 5 most frequent health domains were multiple conditions (33/134, 24.6\%), HIV (10/134, 7.5\%), hypertension (10/134, 7.5\%), pediatric injuries (7/134, 5.2\%), health and well-being (6/134, 4.5\%), and drug monitoring (6/134, 4.5\%). Kiosks were integrated into the clinical pathway in 70.1\% (94/134) of studies, opportunistic kiosks accounted for 23.9\% (32/134) of studies, and in 6\% (8/134) of studies, kiosks were used in both. Usability evaluations of kiosks were reported in 20.1\% (27/134) of papers. Barriers (e.g., use of expensive proprietary software) and enablers (e.g., handling of on-demand consultations) of deploying health kiosks were identified. Conclusions: Health kiosks still play a vital role in the health care system, including collecting clinical measurements and providing access to web-based health services and information to those with little or no digital literacy skills and others without personal internet access. We identified research gaps, such as training needs for teleconsultations and scant reporting on usability evaluation methods. ", doi="10.2196/26511", url="https://medinform.jmir.org/2022/3/e26511", url="http://www.ncbi.nlm.nih.gov/pubmed/35348457" } @Article{info:doi/10.2196/33182, author="Lu, Sheng-Chieh and Xu, Cai and Nguyen, H. Chandler and Geng, Yimin and Pfob, Andr{\'e} and Sidey-Gibbons, Chris", title="Machine Learning--Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal", journal="JMIR Med Inform", year="2022", month="Mar", day="14", volume="10", number="3", pages="e33182", keywords="machine learning", keywords="cancer mortality", keywords="artificial intelligence", keywords="clinical prediction models", keywords="end-of-life care", abstract="Background: In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality. Objective: This study aims to summarize the evidence for applying ML in ?1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer. Methods: We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ?1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies. Results: We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80\%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size. Conclusions: We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting. ", doi="10.2196/33182", url="https://medinform.jmir.org/2022/3/e33182", url="http://www.ncbi.nlm.nih.gov/pubmed/35285816" } @Article{info:doi/10.2196/30328, author="Evans, Richard and Burns, Jennifer and Damschroder, Laura and Annis, Ann and Freitag, B. Michelle and Raffa, Susan and Wiitala, Wyndy", title="Deriving Weight From Big Data: Comparison of Body Weight Measurement--Cleaning Algorithms", journal="JMIR Med Inform", year="2022", month="Mar", day="9", volume="10", number="3", pages="e30328", keywords="veterans", keywords="weight", keywords="algorithms", keywords="obesity", keywords="measurement", keywords="electronic health record", abstract="Background: Patient body weight is a frequently used measure in biomedical studies, yet there are no standard methods for processing and cleaning weight data. Conflicting documentation on constructing body weight measurements presents challenges for research and program evaluation. Objective: In this study, we aim to describe and compare methods for extracting and cleaning weight data from electronic health record databases to develop guidelines for standardized approaches that promote reproducibility. Methods: We conducted a systematic review of studies published from 2008 to 2018 that used Veterans Health Administration electronic health record weight data and documented the algorithms for constructing patient weight. We applied these algorithms to a cohort of veterans with at least one primary care visit in 2016. The resulting weight measures were compared at the patient and site levels. Results: We identified 496 studies and included 62 (12.5\%) that used weight as an outcome. Approximately 48\% (27/62) included a replicable algorithm. Algorithms varied from cutoffs of implausible weights to complex models using measures within patients over time. We found differences in the number of weight values after applying the algorithms (71,961/1,175,995, 6.12\% to 1,175,177/1,175,995, 99.93\% of raw data) but little difference in average weights across methods (93.3, SD 21.0 kg to 94.8, SD 21.8 kg). The percentage of patients with at least 5\% weight loss over 1 year ranged from 9.37\% (4933/52,642) to 13.99\% (3355/23,987). Conclusions: Contrasting algorithms provide similar results and, in some cases, the results are not different from using raw, unprocessed data despite algorithm complexity. Studies using point estimates of weight may benefit from a simple cleaning rule based on cutoffs of implausible values; however, research questions involving weight trajectories and other, more complex scenarios may benefit from a more nuanced algorithm that considers all available weight data. ", doi="10.2196/30328", url="https://medinform.jmir.org/2022/3/e30328", url="http://www.ncbi.nlm.nih.gov/pubmed/35262492" } @Article{info:doi/10.2196/28781, author="Hong, Na and Liu, Chun and Gao, Jianwei and Han, Lin and Chang, Fengxiang and Gong, Mengchun and Su, Longxiang", title="State of the Art of Machine Learning--Enabled Clinical Decision Support in Intensive Care Units: Literature Review", journal="JMIR Med Inform", year="2022", month="Mar", day="3", volume="10", number="3", pages="e28781", keywords="machine learning", keywords="intensive care units", keywords="clinical decision support", keywords="prediction model", keywords="artificial intelligence", keywords="electronic health records", abstract="Background: Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning--based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care. Objective: We aimed to review the research and application of machine learning--enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning--supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations. Methods: We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning--enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics. Results: A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics---monitoring, detection, and diagnosis (13/97, 13.4\%), early identification of clinical events (32/97, 33.0\%), outcome prediction and prognosis assessment (46/97, 47.6\%), and treatment decision (6/97, 6.2\%). Of the 97 papers, 82 (84.5\%) studies used data from adult patients, 9 (9.3\%) studies used data from pediatric patients, and 6 (6.2\%) studies used data from neonates. We found that 65 (67.0\%) studies used data from a single center, and 32 (33.0\%) studies used a multicenter data set; 88 (90.7\%) studies used supervised learning, 3 (3.1\%) studies used unsupervised learning, and 6 (6.2\%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23). Conclusions: Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80\% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future. ", doi="10.2196/28781", url="https://medinform.jmir.org/2022/3/e28781", url="http://www.ncbi.nlm.nih.gov/pubmed/35238790" } @Article{info:doi/10.2196/34907, author="Ellis, A. Louise and Sarkies, Mitchell and Churruca, Kate and Dammery, Genevieve and Meulenbroeks, Isabelle and Smith, L. Carolynn and Pomare, Chiara and Mahmoud, Zeyad and Zurynski, Yvonne and Braithwaite, Jeffrey", title="The Science of Learning Health Systems: Scoping Review of Empirical Research", journal="JMIR Med Inform", year="2022", month="Feb", day="23", volume="10", number="2", pages="e34907", keywords="learning health systems", keywords="learning health care systems", keywords="implementation science", keywords="evaluation", keywords="health system", keywords="health care system", keywords="empirical research", keywords="medical informatics", keywords="review", abstract="Background: The development and adoption of a learning health system (LHS) has been proposed as a means to address key challenges facing current and future health care systems. The first review of the LHS literature was conducted 5 years ago, identifying only a small number of published papers that had empirically examined the implementation or testing of an LHS. It is timely to look more closely at the published empirical research and to ask the question, Where are we now? 5 years on from that early LHS review. Objective: This study performed a scoping review of empirical research within the LHS domain. Taking an ``implementation science'' lens, the review aims to map out the empirical research that has been conducted to date, identify limitations, and identify future directions for the field. Methods: Two academic databases (PubMed and Scopus) were searched using the terms ``learning health* system*'' for papers published between January 1, 2016, to January 31, 2021, that had an explicit empirical focus on LHSs. Study information was extracted relevant to the review objective, including each study's publication details; primary concern or focus; context; design; data type; implementation framework, model, or theory used; and implementation determinants or outcomes examined. Results: A total of 76 studies were included in this review. Over two-thirds of the studies were concerned with implementing a particular program, system, or platform (53/76, 69.7\%) designed to contribute to achieving an LHS. Most of these studies focused on a particular clinical context or patient population (37/53, 69.8\%), with far fewer studies focusing on whole hospital systems (4/53, 7.5\%) or on other broad health care systems encompassing multiple facilities (12/53, 22.6\%). Over two-thirds of the program-specific studies utilized quantitative methods (37/53, 69.8\%), with a smaller number utilizing qualitative methods (10/53, 18.9\%) or mixed-methods designs (6/53, 11.3\%). The remaining 23 studies were classified into 1 of 3 key areas: ethics, policies, and governance (10/76, 13.2\%); stakeholder perspectives of LHSs (5/76, 6.6\%); or LHS-specific research strategies and tools (8/76, 10.5\%). Overall, relatively few studies were identified that incorporated an implementation science framework. Conclusions: Although there has been considerable growth in empirical applications of LHSs within the past 5 years, paralleling the recent emergence of LHS-specific research strategies and tools, there are few high-quality studies. Comprehensive reporting of implementation and evaluation efforts is an important step to moving the LHS field forward. In particular, the routine use of implementation determinant and outcome frameworks will improve the assessment and reporting of barriers, enablers, and implementation outcomes in this field and will enable comparison and identification of trends across studies. ", doi="10.2196/34907", url="https://medinform.jmir.org/2022/2/e34907", url="http://www.ncbi.nlm.nih.gov/pubmed/35195529" } @Article{info:doi/10.2196/32695, author="Bucalon, Bernard and Shaw, Tim and Brown, Kerri and Kay, Judy", title="State-of-the-art Dashboards on Clinical Indicator Data to Support Reflection on Practice: Scoping Review", journal="JMIR Med Inform", year="2022", month="Feb", day="14", volume="10", number="2", pages="e32695", keywords="practice analytics dashboards", keywords="data visualization", keywords="reflective practice", keywords="professional learning", keywords="mobile phone", abstract="Background: There is an increasing interest in using routinely collected eHealth data to support reflective practice and long-term professional learning. Studies have evaluated the impact of dashboards on clinician decision-making, task completion time, user satisfaction, and adherence to clinical guidelines. Objective: This scoping review aims to summarize the literature on dashboards based on patient administrative, medical, and surgical data for clinicians to support reflective practice. Methods: A scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in 5 electronic databases (MEDLINE, Embase, Scopus, ACM Digital Library, and Web of Science) to identify studies that met the inclusion criteria. Study selection and characterization were performed by 2 independent reviewers (BB and CP). One reviewer extracted the data that were analyzed descriptively to map the available evidence. Results: A total of 18 dashboards from 8 countries were assessed. Purposes for the dashboards were designed for performance improvement (10/18, 56\%), to support quality and safety initiatives (6/18, 33\%), and management and operations (4/18, 22\%). Data visualizations were primarily designed for team use (12/18, 67\%) rather than individual clinicians (4/18, 22\%). Evaluation methods varied among asking the clinicians directly (11/18, 61\%), observing user behavior through clinical indicators and use log data (14/18, 78\%), and usability testing (4/18, 22\%). The studies reported high scores on standard usability questionnaires, favorable surveys, and interview feedback. Improvements to underlying clinical indicators were observed in 78\% (7/9) of the studies, whereas 22\% (2/9) of the studies reported no significant changes in performance. Conclusions: This scoping review maps the current literature landscape on dashboards based on routinely collected clinical indicator data. Although there were common data visualization techniques and clinical indicators used across studies, there was diversity in the design of the dashboards and their evaluation. There was a lack of detail regarding the design processes documented for reproducibility. We identified a lack of interface features to support clinicians in making sense of and reflecting on their personal performance data. ", doi="10.2196/32695", url="https://medinform.jmir.org/2022/2/e32695", url="http://www.ncbi.nlm.nih.gov/pubmed/35156928" } @Article{info:doi/10.2196/33518, author="Willis, C. Van and Thomas Craig, Jean Kelly and Jabbarpour, Yalda and Scheufele, L. Elisabeth and Arriaga, E. Yull and Ajinkya, Monica and Rhee, B. Kyu and Bazemore, Andrew", title="Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review", journal="JMIR Med Inform", year="2022", month="Jan", day="21", volume="10", number="1", pages="e33518", keywords="digital technology", keywords="primary health care", keywords="preventive medicine", keywords="telemedicine", keywords="clinical decision support systems", abstract="Background: Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated. Objective: This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings. Methods: A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted. Results: The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9\%), clinical decision support (88/241, 36.5\%), telehealth (88/241, 36.5\%), and multiple technologies (154/241, 63.9\%). DHIs were predominantly used for tertiary prevention (131/241, 54.4\%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4\%). DHI users were providers (205/241, 85.1\%), patients (111/241, 46.1\%), or multiple types (89/241, 36.9\%). Reported outcomes were primarily clinical (179/241, 70.1\%), and statistically significant improvements were common (192/241, 79.7\%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low. Conclusions: Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored. ", doi="10.2196/33518", url="https://medinform.jmir.org/2022/1/e33518", url="http://www.ncbi.nlm.nih.gov/pubmed/35060909" } @Article{info:doi/10.2196/17278, author="Elangovan, Deepa and Long, Soon Chiau and Bakrin, Safina Faizah and Tan, Siang Ching and Goh, Wen Khang and Yeoh, Fei Siang and Loy, Jun Mei and Hussain, Zahid and Lee, Seng Kah and Idris, Che Azam and Ming, Chiau Long", title="The Use of Blockchain Technology in the Health Care Sector: Systematic Review", journal="JMIR Med Inform", year="2022", month="Jan", day="20", volume="10", number="1", pages="e17278", keywords="blockchain", keywords="health care", keywords="hospital information system", keywords="data integrity", keywords="access control", keywords="data logging", keywords="health informatics", abstract="Background: Blockchain technology is a part of Industry 4.0's new Internet of Things applications: decentralized systems, distributed ledgers, and immutable and cryptographically secure technology. This technology entails a series of transaction lists with identical copies shared and retained by different groups or parties. One field where blockchain technology has tremendous potential is health care, due to the more patient-centric approach to the health care system as well as blockchain's ability to connect disparate systems and increase the accuracy of electronic health records. Objective: The aim of this study was to systematically review studies on the use of blockchain technology in health care and to analyze the characteristics of the studies that have implemented blockchain technology. Methods: This study used a systematic review methodology to find literature related to the implementation aspect of blockchain technology in health care. Relevant papers were searched for using PubMed, SpringerLink, IEEE Xplore, Embase, Scopus, and EBSCOhost. A quality assessment of literature was performed on the 22 selected papers by assessing their trustworthiness and relevance. Results: After full screening, 22 papers were included. A table of evidence was constructed, and the results of the selected papers were interpreted. The results of scoring for measuring the quality of the publications were obtained and interpreted. Out of 22 papers, a total of 3 (14\%) high-quality papers, 9 (41\%) moderate-quality papers, and 10 (45\%) low-quality papers were identified. Conclusions: Blockchain technology was found to be useful in real health care environments, including for the management of electronic medical records, biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas. The main reasons for the implementation of blockchain technology in the health care sector were identified as data integrity, access control, data logging, data versioning, and nonrepudiation. The findings could help the scientific community to understand the implementation aspect of blockchain technology. The results from this study help in recognizing the accessibility and use of blockchain technology in the health care sector. ", doi="10.2196/17278", url="https://medinform.jmir.org/2022/1/e17278", url="http://www.ncbi.nlm.nih.gov/pubmed/35049516" } @Article{info:doi/10.2196/29434, author="Naseri Jahfari, Arman and Tax, David and Reinders, Marcel and van der Bilt, Ivo", title="Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View", journal="JMIR Med Inform", year="2022", month="Jan", day="19", volume="10", number="1", pages="e29434", keywords="mHealth", keywords="wearable", keywords="machine learning", keywords="cardiovascular disease", keywords="digital health", keywords="review", keywords="mobile phone", abstract="Background: Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. Objective: This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. Methods: We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as ``wearables,'' ``machine learning,'' and ``cardiovascular disease.'' Methodologies were categorized and analyzed according to machine learning--based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). Results: After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. Conclusions: Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation. ", doi="10.2196/29434", url="https://medinform.jmir.org/2022/1/e29434", url="http://www.ncbi.nlm.nih.gov/pubmed/35044316" } @Article{info:doi/10.2196/19250, author="Yeng, Kandabongee Prosper and Nweke, Obiora Livinus and Yang, Bian and Ali Fauzi, Muhammad and Snekkenes, Arthur Einar", title="Artificial Intelligence--Based Framework for Analyzing Health Care Staff Security Practice: Mapping Review and Simulation Study", journal="JMIR Med Inform", year="2021", month="Dec", day="22", volume="9", number="12", pages="e19250", keywords="artificial intelligence", keywords="machine learning", keywords="health care", keywords="security practice", keywords="framework", keywords="security", keywords="modeling", keywords="analysis", abstract="Background: Blocklisting malicious activities in health care is challenging in relation to access control in health care security practices due to the fear of preventing legitimate access for therapeutic reasons. Inadvertent prevention of legitimate access can contravene the availability trait of the confidentiality, integrity, and availability triad, and may result in worsening health conditions, leading to serious consequences, including deaths. Therefore, health care staff are often provided with a wide range of access such as a ``breaking-the-glass'' or ``self-authorization'' mechanism for emergency access. However, this broad access can undermine the confidentiality and integrity of sensitive health care data because breaking-the-glass can lead to vast unauthorized access, which could be problematic when determining illegitimate access in security practices. Objective: A review was performed to pinpoint appropriate artificial intelligence (AI) methods and data sources that can be used for effective modeling and analysis of health care staff security practices. Based on knowledge obtained from the review, a framework was developed and implemented with simulated data to provide a comprehensive approach toward effective modeling and analyzing security practices of health care staff in real access logs. Methods: The flow of our approach was a mapping review to provide AI methods, data sources and their attributes, along with other categories as input for framework development. To assess implementation of the framework, electronic health record (EHR) log data were simulated and analyzed, and the performance of various approaches in the framework was compared. Results: Among the total 130 articles initially identified, 18 met the inclusion and exclusion criteria. A thorough assessment and analysis of the included articles revealed that K-nearest neighbor, Bayesian network, and decision tree (C4.5) algorithms were predominantly applied to EHR and network logs with varying input features of health care staff security practices. Based on the review results, a framework was developed and implemented with simulated logs. The decision tree obtained the best precision of 0.655, whereas the best recall was achieved by the support vector machine (SVM) algorithm at 0.977. However, the best F1-score was obtained by random forest at 0.775. In brief, three classifiers (random forest, decision tree, and SVM) in the two-class approach achieved the best precision of 0.998. Conclusions: The security practices of health care staff can be effectively analyzed using a two-class approach to detect malicious and nonmalicious security practices. Based on our comparative study, the algorithms that can effectively be used in related studies include random forest, decision tree, and SVM. Deviations of security practices from required health care staff's security behavior in the big data context can be analyzed with real access logs to define appropriate incentives for improving conscious care security practice. ", doi="10.2196/19250", url="https://medinform.jmir.org/2021/12/e19250", url="http://www.ncbi.nlm.nih.gov/pubmed/34941549" } @Article{info:doi/10.2196/30798, author="Alamgir, Asma and Mousa, Osama and Shah, Zubair", title="Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review", journal="JMIR Med Inform", year="2021", month="Dec", day="17", volume="9", number="12", pages="e30798", keywords="artificial intelligence", keywords="machine learning", keywords="deep learning", keywords="cardiac arrest", keywords="predict", abstract="Background: Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective: This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods: A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results: Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55\%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34\%) studies developed an AI-based warning system. The remaining 11\% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96\%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68\%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81\%), and the most used algorithm was the neural network (23/47, 49\%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51\%). Conclusions: AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary. ", doi="10.2196/30798", url="https://medinform.jmir.org/2021/12/e30798", url="http://www.ncbi.nlm.nih.gov/pubmed/34927595" } @Article{info:doi/10.2196/27363, author="Maile, Howard and Li, Olivia Ji-Peng and Gore, Daniel and Leucci, Marcello and Mulholland, Padraig and Hau, Scott and Szabo, Anita and Moghul, Ismail and Balaskas, Konstantinos and Fujinami, Kaoru and Hysi, Pirro and Davidson, Alice and Liskova, Petra and Hardcastle, Alison and Tuft, Stephen and Pontikos, Nikolas", title="Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review", journal="JMIR Med Inform", year="2021", month="Dec", day="13", volume="9", number="12", pages="e27363", keywords="artificial intelligence", keywords="machine learning", keywords="cornea", keywords="keratoconus", keywords="corneal tomography", keywords="subclinical", keywords="corneal imaging", keywords="decision support systems", keywords="corneal disease", keywords="keratometry", abstract="Background: Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements. Objective: The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions. Methods: For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results: We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study. Conclusions: Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression. ", doi="10.2196/27363", url="https://medinform.jmir.org/2021/12/e27363", url="http://www.ncbi.nlm.nih.gov/pubmed/34898463" } @Article{info:doi/10.2196/28962, author="Kundu, Anasua and Chaiton, Michael and Billington, Rebecca and Grace, Daniel and Fu, Rui and Logie, Carmen and Baskerville, Bruce and Yager, Christina and Mitsakakis, Nicholas and Schwartz, Robert", title="Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review", journal="JMIR Med Inform", year="2021", month="Nov", day="11", volume="9", number="11", pages="e28962", keywords="sexual and gender minorities", keywords="mental health", keywords="mental disorders", keywords="substance-related disorders", keywords="machine learning", abstract="Background: A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. Objective: This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. Methods: The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. Results: We included 11 studies in this review, of which 81\% (9/11) were on mental health and 18\% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45\%), whereas sexual minority women were studied the least (2/11, 18\%). Studies were categorized into 3 major domains: web content analysis (6/11, 54\%), prediction modeling (4/11, 36\%), and imaging studies (1/11, 9\%). Conclusions: ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health. ", doi="10.2196/28962", url="https://medinform.jmir.org/2021/11/e28962", url="http://www.ncbi.nlm.nih.gov/pubmed/34762059" } @Article{info:doi/10.2196/29871, author="Zuo, Zheming and Watson, Matthew and Budgen, David and Hall, Robert and Kennelly, Chris and Al Moubayed, Noura", title="Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study", journal="JMIR Med Inform", year="2021", month="Oct", day="15", volume="9", number="10", pages="e29871", keywords="healthcare", keywords="privacy-preserving", keywords="GDPR", keywords="DPA 2018", keywords="EHR", keywords="SLM", keywords="data science", keywords="anonymization", keywords="reidentification risk", keywords="usability", abstract="Background: Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union's General Data Protection Regulation (2016) and the United Kingdom's Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy. Objective: In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care. Methods: We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization. Results: We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning--based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization. Conclusions: This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications. ", doi="10.2196/29871", url="https://medinform.jmir.org/2021/10/e29871", url="http://www.ncbi.nlm.nih.gov/pubmed/34652278" } @Article{info:doi/10.2196/30022, author="Monahan, Corneille Ann and Feldman, S. Sue", title="Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS", journal="JMIR Med Inform", year="2021", month="Sep", day="16", volume="9", number="9", pages="e30022", keywords="emergency services", keywords="hospital", keywords="decision support techniques", keywords="patient-specific modeling", keywords="crowding", keywords="boarding", keywords="exit block", keywords="systematic review", keywords="PROBAST", keywords="CHARMS", keywords="predictive model", keywords="medical informatics", keywords="health services research", keywords="prehospital assessment", keywords="process improvement", keywords="management information system", keywords="predict admission", keywords="emergency department", abstract="Background: Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective: The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods: We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients' imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results: Potential biases were found in most studies, which suggested that each model's predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions: There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages. ", doi="10.2196/30022", url="https://medinform.jmir.org/2021/9/e30022", url="http://www.ncbi.nlm.nih.gov/pubmed/34528893" } @Article{info:doi/10.2196/21974, author="Ouyang, Wei and Xie, Wenzhao and Xin, Zirui and He, Haiyan and Wen, Tingxiao and Peng, Xiaoqing and Dai, Pingping and Yuan, Yifeng and Liu, Fei and Chen, Yang and Luo, Aijing", title="Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019", journal="J Med Internet Res", year="2021", month="Sep", day="9", volume="23", number="9", pages="e21974", keywords="consumer health informatics", keywords="consumer health information", keywords="thematic evaluation", keywords="co-word analysis", keywords="informatics", keywords="SciMAT", abstract="Background: Consumer health informatics (CHI) originated in the 1990s. With the rapid development of computer and information technology for health decision making, an increasing number of consumers have obtained health-related information through the internet, and CHI has also attracted the attention of an increasing number of scholars. Objective: The aim of this study was to analyze the research themes and evolution characteristics of different study periods and to discuss the dynamic evolution path and research theme rules in a time-series framework from the perspective of a strategy map and a data flow in CHI. Methods: The Web of Science core collection database of the Institute for Scientific Information was used as the data source to retrieve relevant articles in the field of CHI. SciMAT was used to preprocess the literature data and construct the overlapping map, evolution map, strategic diagram, and cluster network characterized by keywords. Besides, a bibliometric analysis of the general characteristics, the evolutionary characteristics of the theme, and the evolutionary path of the theme was conducted. Results: A total of 986 articles were obtained after the retrieval, and 931 articles met the document-type requirement. In the past 21 years, the number of articles increased every year, with a remarkable growth after 2015. The research content in 4 different study periods formed the following 38 themes: patient education, medicine, needs, and bibliographic database in the 1999-2003 study period; world wide web, patient education, eHealth, patients, medication, terminology, behavior, technology, and disease in the 2004-2008 study period; websites, information seeking, physicians, attitudes, technology, risk, food labeling, patient, strategies, patient education, and eHealth in the 2009-2014 study period; and electronic medical records, health information seeking, attitudes, health communication, breast cancer, health literacy, technology, natural language processing, user-centered design, pharmacy, academic libraries, costs, internet utilization, and online health information in the 2015-2019 study period. Besides, these themes formed 10 evolution paths in 3 research directions: patient education and intervention, consumer demand attitude and behavior, and internet information technology application. Conclusions: Averaging 93 publications every year since 2015, CHI research is in a rapid growth period. The research themes mainly focus on patient education, health information needs, health information search behavior, health behavior intervention, health literacy, health information technology, eHealth, and other aspects. Patient education and intervention research, consumer demand, attitude, and behavior research comprise the main theme evolution path, whose evolution process has been relatively stable. This evolution path will continue to become the research hotspot in this field. Research on the internet and information technology application is a secondary theme evolution path with development potential. ", doi="10.2196/21974", url="https://www.jmir.org/2021/9/e21974", url="http://www.ncbi.nlm.nih.gov/pubmed/34499042" } @Article{info:doi/10.2196/20675, author="Jing, Xia", title="The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis", journal="JMIR Med Inform", year="2021", month="Aug", day="27", volume="9", number="8", pages="e20675", keywords="Unified Medical Language System", keywords="systematic literature analysis", keywords="biomedical informatics", keywords="health informatics", abstract="Background: The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications. Objective: Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years. Methods: PubMed, ACM Digital Library, and the Nursing \& Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6\%), information retrieval (125/975, 12.8\%), terminology study (90/975, 9.2\%), ontology and modeling (80/975, 8.2\%), medical subdomains (76/975, 7.8\%), other language studies (53/975, 5.4\%), artificial intelligence tools and applications (46/975, 4.7\%), patient care (35/975, 3.6\%), data mining and knowledge discovery (25/975, 2.6\%), medical education (20/975, 2.1\%), degree-related theses (13/975, 1.3\%), digital library (5/975, 0.5\%), and the UMLS itself (150/975, 15.4\%), as well as the UMLS for other purposes (27/975, 2.8\%). Conclusions: The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions. ", doi="10.2196/20675", url="https://medinform.jmir.org/2021/8/e20675", url="http://www.ncbi.nlm.nih.gov/pubmed/34236337" } @Article{info:doi/10.2196/28023, author="Damoiseaux-Volman, A. Birgit and van der Velde, Nathalie and Ruige, G. Sil and Romijn, A. Johannes and Abu-Hanna, Ameen and Medlock, Stephanie", title="Effect of Interventions With a Clinical Decision Support System for Hospitalized Older Patients: Systematic Review Mapping Implementation and Design Factors", journal="JMIR Med Inform", year="2021", month="Jul", day="16", volume="9", number="7", pages="e28023", keywords="aged", keywords="clinical decision support systems", keywords="geriatrics", keywords="hospital", keywords="quality of care", abstract="Background: Clinical decision support systems (CDSSs) form an implementation strategy that can facilitate and support health care professionals in the care of older hospitalized patients. Objective: Our study aims to systematically review the effects of CDSS interventions in older hospitalized patients. As a secondary aim, we aim to summarize the implementation and design factors described in effective and ineffective interventions and identify gaps in the current literature. Methods: We conducted a systematic review with a search strategy combining the categories older patients, geriatric topic, hospital, CDSS, and intervention in the databases MEDLINE, Embase, and SCOPUS. We included controlled studies, extracted data of all reported outcomes, and potentially beneficial design and implementation factors. We structured these factors using the Grol and Wensing Implementation of Change model, the GUIDES (Guideline Implementation with Decision Support) checklist, and the two-stream model. The risk of bias of the included studies was assessed using the Cochrane Collaboration's Effective Practice and Organisation of Care risk of bias approach. Results: Our systematic review included 18 interventions, of which 13 (72\%) were effective in improving care. Among these interventions, 8 (6 effective) focused on medication review, 8 (6 effective) on delirium, 7 (4 effective) on falls, 5 (4 effective) on functional decline, 4 (3 effective) on discharge or aftercare, and 2 (0 effective) on pressure ulcers. In 77\% (10/13) effective interventions, the effect was based on process-related outcomes, in 15\% (2/13) interventions on both process- and patient-related outcomes, and in 8\% (1/13) interventions on patient-related outcomes. The following implementation and design factors were potentially associated with effectiveness: a priori problem or performance analyses (described in 9/13, 69\% effective vs 0/5, 0\% ineffective interventions), multifaceted interventions (8/13, 62\% vs 1/5, 20\%), and consideration of the workflow (9/13, 69\% vs 1/5, 20\%). Conclusions: CDSS interventions can improve the hospital care of older patients, mostly on process-related outcomes. We identified 2 implementation factors and 1 design factor that were reported more frequently in articles on effective interventions. More studies with strong designs are needed to measure the effect of CDSS on relevant patient-related outcomes, investigate personalized (data-driven) interventions, and quantify the impact of implementation and design factors on CDSS effectiveness. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews): CRD42019124470; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=124470. ", doi="10.2196/28023", url="https://medinform.jmir.org/2021/7/e28023", url="http://www.ncbi.nlm.nih.gov/pubmed/34269682" } @Article{info:doi/10.2196/26601, author="Enriquez, S. Jos{\'e} and Chu, Yan and Pudakalakatti, Shivanand and Hsieh, Lin Kang and Salmon, Duncan and Dutta, Prasanta and Millward, Zacharias Niki and Lurie, Eugene and Millward, Steven and McAllister, Florencia and Maitra, Anirban and Sen, Subrata and Killary, Ann and Zhang, Jian and Jiang, Xiaoqian and Bhattacharya, K. Pratip and Shams, Shayan", title="Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer", journal="JMIR Med Inform", year="2021", month="Jun", day="17", volume="9", number="6", pages="e26601", keywords="artificial intelligence", keywords="deep learning", keywords="hyperpolarization", keywords="metabolic imaging", keywords="MRI", keywords="13C", keywords="HP-MR", keywords="pancreatic ductal adenocarcinoma", keywords="pancreatic cancer", keywords="early detection", keywords="assessment of treatment response", keywords="probes", keywords="cancer", keywords="marker", keywords="imaging", keywords="treatment", keywords="review", keywords="detection", keywords="efficacy", abstract="Background: There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). Objective: Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. Methods: A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. Results: Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR--related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. Conclusions: Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC. ", doi="10.2196/26601", url="https://medinform.jmir.org/2021/6/e26601", url="http://www.ncbi.nlm.nih.gov/pubmed/34137725" } @Article{info:doi/10.2196/20713, author="Velmovitsky, Elkind Pedro and Bublitz, Moreira Frederico and Fadrique, Xavier Laura and Morita, Pelegrini Plinio", title="Blockchain Applications in Health Care and Public Health: Increased Transparency", journal="JMIR Med Inform", year="2021", month="Jun", day="8", volume="9", number="6", pages="e20713", keywords="health care", keywords="blockchain", keywords="EHR", keywords="health insurance", keywords="drug supply chain", keywords="genomics", keywords="consent", keywords="digital ledger", keywords="food supply chain", abstract="Background: Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges. Objective: In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies. Methods: We conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium. Results: Health care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a product's provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process. Conclusions: Blockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint. ", doi="10.2196/20713", url="https://medinform.jmir.org/2021/6/e20713", url="http://www.ncbi.nlm.nih.gov/pubmed/34100768" } @Article{info:doi/10.2196/25704, author="Jiang, Mengyao and Ma, Yuxia and Guo, Siyi and Jin, Liuqi and Lv, Lin and Han, Lin and An, Ning", title="Using Machine Learning Technologies in Pressure Injury Management: Systematic Review", journal="JMIR Med Inform", year="2021", month="Mar", day="10", volume="9", number="3", pages="e25704", keywords="pressure injuries", keywords="pressure ulcer", keywords="pressure sore", keywords="pressure damage", keywords="decubitus ulcer", keywords="decubitus sore", keywords="bedsore", keywords="artificial intelligence", keywords="machine learning", keywords="neural network", keywords="support vector machine", keywords="natural language processing", keywords="Naive Bayes", keywords="bayesian learning", keywords="support vector", keywords="random forest", keywords="boosting", keywords="deep learning", keywords="machine intelligence", keywords="computational intelligence", keywords="computer reasoning", keywords="management", keywords="systematic review", abstract="Background: Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective: The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods: We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: A total of 32 articles met the inclusion criteria. Twelve of those articles (38\%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34\%) reported using them in posture detection and recognition, and 9 (28\%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions: There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality. ", doi="10.2196/25704", url="https://medinform.jmir.org/2021/3/e25704", url="http://www.ncbi.nlm.nih.gov/pubmed/33688846" } @Article{info:doi/10.2196/23934, author="Lee, Seungwon and Doktorchik, Chelsea and Martin, Asher Elliot and D'Souza, Giles Adam and Eastwood, Cathy and Shaheen, Aziz Abdel and Naugler, Christopher and Lee, Joon and Quan, Hude", title="Electronic Medical Record--Based Case Phenotyping for the Charlson Conditions: Scoping Review", journal="JMIR Med Inform", year="2021", month="Feb", day="1", volume="9", number="2", pages="e23934", keywords="electronic medical records", keywords="Charlson comorbidity", keywords="EMR phenotyping", keywords="health services research", abstract="Background: Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods: A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results: A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5\%), followed by the United Kingdom (42/299, 14.0\%) and Canada (15/299, 5.0\%). These algorithms were mostly developed either in primary care (103/299, 34.4\%) or inpatient (168/299, 56.2\%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule--based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions: Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed. ", doi="10.2196/23934", url="https://medinform.jmir.org/2021/2/e23934", url="http://www.ncbi.nlm.nih.gov/pubmed/33522976" } @Article{info:doi/10.2196/23811, author="Syeda, Bareen Hafsa and Syed, Mahanazuddin and Sexton, Wayne Kevin and Syed, Shorabuddin and Begum, Salma and Syed, Farhanuddin and Prior, Fred and Yu Jr, Feliciano", title="Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review", journal="JMIR Med Inform", year="2021", month="Jan", day="11", volume="9", number="1", pages="e23811", keywords="COVID-19", keywords="coronavirus", keywords="SARS-CoV-2", keywords="artificial intelligence", keywords="machine learning", keywords="deep learning", keywords="systematic review", keywords="epidemiology", keywords="pandemic", keywords="neural network", abstract="Background: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)--based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods: A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results: The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6\%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8\%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6\%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions: In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research. ", doi="10.2196/23811", url="http://medinform.jmir.org/2021/1/e23811/", url="http://www.ncbi.nlm.nih.gov/pubmed/33326405" } @Article{info:doi/10.2196/23562, author="Xu, Xin and Hu, Jiming and Lyu, Xiaoguang and Huang, He and Cheng, Xingyu", title="Exploring the Interdisciplinary Nature of Precision Medicine?Network Analysis and Visualization", journal="JMIR Med Inform", year="2021", month="Jan", day="11", volume="9", number="1", pages="e23562", keywords="precision medicine", keywords="interdisciplinary", keywords="social network analysis", keywords="co-occurrence analysis", abstract="Background: Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear. Objective: The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. Methods: A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. Results: The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications. Conclusions: This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine. ", doi="10.2196/23562", url="http://medinform.jmir.org/2021/1/e23562/", url="http://www.ncbi.nlm.nih.gov/pubmed/33427681" } @Article{info:doi/10.2196/medinform.6924, author="Capurro, Daniel and Barbe, Mario and Daza, Claudio and Santa Maria, Josefa and Trincado, Javier", title="Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis", journal="JMIR Med Inform", year="2020", month="Nov", day="24", volume="8", number="11", pages="e6924", keywords="digital phenotyping", keywords="clinical data", keywords="temporal abstraction", abstract="Background: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record--based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. Objective: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. Methods: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. Results: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. Conclusions: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system. ", doi="10.2196/medinform.6924", url="http://medinform.jmir.org/2020/11/e6924/", url="http://www.ncbi.nlm.nih.gov/pubmed/33231554" } @Article{info:doi/10.2196/21621, author="Araujo, Magalhaes Sabrina and Sousa, Paulino and Dutra, In{\^e}s", title="Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review", journal="JMIR Med Inform", year="2020", month="Oct", day="16", volume="8", number="10", pages="e21621", keywords="pressure ulcer", keywords="decision support systems, clinical", keywords="systematic review", abstract="Background: The clinical decision-making process in pressure ulcer management is complex, and its quality depends on both the nurse's experience and the availability of scientific knowledge. This process should follow evidence-based practices incorporating health information technologies to assist health care professionals, such as the use of clinical decision support systems. These systems, in addition to increasing the quality of care provided, can reduce errors and costs in health care. However, the widespread use of clinical decision support systems still has limited evidence, indicating the need to identify and evaluate its effects on nursing clinical practice. Objective: The goal of the review was to identify the effects of nurses using clinical decision support systems on clinical decision making for pressure ulcer management. Methods: The systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. The search was conducted in April 2019 on 5 electronic databases: MEDLINE, SCOPUS, Web of Science, Cochrane, and CINAHL, without publication date or study design restrictions. Articles that addressed the use of computerized clinical decision support systems in pressure ulcer care applied in clinical practice were included. The reference lists of eligible articles were searched manually. The Mixed Methods Appraisal Tool was used to assess the methodological quality of the studies. Results: The search strategy resulted in 998 articles, 16 of which were included. The year of publication ranged from 1995 to 2017, with 45\% of studies conducted in the United States. Most addressed the use of clinical decision support systems by nurses in pressure ulcers prevention in inpatient units. All studies described knowledge-based systems that assessed the effects on clinical decision making, clinical effects secondary to clinical decision support system use, or factors that influenced the use or intention to use clinical decision support systems by health professionals and the success of their implementation in nursing practice. Conclusions: The evidence in the available literature about the effects of clinical decision support systems (used by nurses) on decision making for pressure ulcer prevention and treatment is still insufficient. No significant effects were found on nurses' knowledge following the integration of clinical decision support systems into the workflow, with assessments made for a brief period of up to 6 months. Clinical effects, such as outcomes in the incidence and prevalence of pressure ulcers, remain limited in the studies, and most found clinically but nonstatistically significant results in decreasing pressure ulcers. It is necessary to carry out studies that prioritize better adoption and interaction of nurses with clinical decision support systems, as well as studies with a representative sample of health care professionals, randomized study designs, and application of assessment instruments appropriate to the professional and institutional profile. In addition, long-term follow-up is necessary to assess the effects of clinical decision support systems that can demonstrate a more real, measurable, and significant effect on clinical decision making. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019127663; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=127663 ", doi="10.2196/21621", url="http://medinform.jmir.org/2020/10/e21621/", url="http://www.ncbi.nlm.nih.gov/pubmed/33064099" } @Article{info:doi/10.2196/20359, author="Kruse, Clemens and Fohn, Joanna and Wilson, Nakia and Nunez Patlan, Evangelina and Zipp, Stephanie and Mileski, Michael", title="Utilization Barriers and Medical Outcomes Commensurate With the Use of Telehealth Among Older Adults: Systematic Review", journal="JMIR Med Inform", year="2020", month="Aug", day="12", volume="8", number="8", pages="e20359", keywords="telehealth", keywords="telemedicine", keywords="older adults", keywords="barriers", keywords="health outcomes", abstract="Background: Rising telehealth capabilities and improving access to older adults can aid in improving health outcomes and quality of life indicators. Telehealth is not being used ubiquitously at present. Objective: This review aimed to identify the barriers that prevent ubiquitous use of telehealth and the ways in which telehealth improves health outcomes and quality of life indicators for older adults. Methods: This systematic review was conducted and reported in accordance with the Kruse protocol and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Reviewers queried the following four research databases: Cumulative Index of Nursing and Allied Health Literature (CINAHL), PubMed (MEDLINE), Web of Science, and Embase (Science Direct). Reviewers analyzed 57 articles, performed a narrative analysis to identify themes, and identified barriers and reports of health outcomes and quality of life indicators found in the literature. Results: Reviewers analyzed 57 studies across the following five interventions of telehealth: eHealth, mobile health (mHealth), telemonitoring, telecare (phone), and telehealth video calls, with a Cohen $\kappa$ of 0.75. Reviewers identified 14 themes for barriers. The most common of which were technical literacy (25/144 occurrences, 17\%), lack of desire (19/144 occurrences, 13\%), and cost (11/144 occurrences, 8\%). Reviewers identified 13 medical outcomes associated with telehealth interventions. The most common of which were decrease in psychological stress (21/118 occurrences, 18\%), increase in autonomy (18/118 occurrences, 15\%), and increase in cognitive ability (11/118 occurrences, 9\%). Some articles did not report medical outcomes (18/57, 32\%) and some did not report barriers (19/57, 33\%). Conclusions: The literature suggests that the elimination of barriers could increase the prevalence of telehealth use by older adults. By increasing use of telehealth, proximity to care is no longer an issue for access, and thereby care can reach populations with chronic conditions and mobility restrictions. Future research should be conducted on methods for personalizing telehealth in older adults before implementation. Trial Registration: PROSPERO CRD42020182162; https://www.crd.york.ac.uk/prospero/display\_record.php?ID=CRD42020182162. International Registered Report Identifier (IRRID): RR2-10.2196/15490 ", doi="10.2196/20359", url="http://medinform.jmir.org/2020/8/e20359/", url="http://www.ncbi.nlm.nih.gov/pubmed/32784177" } @Article{info:doi/10.2196/18599, author="Choudhury, Avishek and Asan, Onur", title="Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review", journal="JMIR Med Inform", year="2020", month="Jul", day="24", volume="8", number="7", pages="e18599", keywords="artificial intelligence", keywords="patient safety", keywords="drug safety", keywords="clinical error", keywords="report analysis", keywords="natural language processing", keywords="drug", keywords="review", abstract="Background: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings. ", doi="10.2196/18599", url="http://medinform.jmir.org/2020/7/e18599/", url="http://www.ncbi.nlm.nih.gov/pubmed/32706688" } @Article{info:doi/10.2196/18301, author="Abd-Alrazaq, Alaa and Safi, Zeineb and Alajlani, Mohannad and Warren, Jim and Househ, Mowafa and Denecke, Kerstin", title="Technical Metrics Used to Evaluate Health Care Chatbots: Scoping Review", journal="J Med Internet Res", year="2020", month="Jun", day="5", volume="22", number="6", pages="e18301", keywords="chatbots", keywords="conversational agents", keywords="health care", keywords="evaluation", keywords="metrics", abstract="Background: Dialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field. Objective: This study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots. Methods: Studies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated. Results: Of the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content). Conclusions: The technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies. ", doi="10.2196/18301", url="http://www.jmir.org/2020/6/e18301/", url="http://www.ncbi.nlm.nih.gov/pubmed/32442157" } @Article{info:doi/10.2196/16452, author="Horne, Elsie and Tibble, Holly and Sheikh, Aziz and Tsanas, Athanasios", title="Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping", journal="JMIR Med Inform", year="2020", month="May", day="28", volume="8", number="5", pages="e16452", keywords="asthma", keywords="cluster analysis", keywords="data mining", keywords="machine learning", keywords="unsupervised machine learning", abstract="Background: In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. Objective: This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. Methods: We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Results: Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75\%) studies and continuous in 12 (19\%), and the feature type was unclear in the remaining 4 (6\%) studies. A total of 23 (37\%) studies used hierarchical clustering with Ward linkage, and 22 (35\%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14\%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86\%) explained the methods used to determine the number of clusters, 24 (38\%) studies tested the quality of their cluster solution, and 11 (17\%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. Conclusions: This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings. ", doi="10.2196/16452", url="http://medinform.jmir.org/2020/5/e16452/", url="http://www.ncbi.nlm.nih.gov/pubmed/32463370" } @Article{info:doi/10.2196/17334, author="Han, Lu and Liu, Jing and Evans, Richard and Song, Yang and Ma, Jingdong", title="Factors Influencing the Adoption of Health Information Standards in Health Care Organizations: A Systematic Review Based on Best Fit Framework Synthesis", journal="JMIR Med Inform", year="2020", month="May", day="15", volume="8", number="5", pages="e17334", keywords="health information systems", keywords="health information interoperability", keywords="adoption", keywords="health care sector", abstract="Background: Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. Objective: This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. Methods: First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles. Results: In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power. Conclusions: The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided. ", doi="10.2196/17334", url="https://medinform.jmir.org/2020/5/e17334", url="http://www.ncbi.nlm.nih.gov/pubmed/32347800" } @Article{info:doi/10.2196/17984, author="Spasic, Irena and Nenadic, Goran", title="Clinical Text Data in Machine Learning: Systematic Review", journal="JMIR Med Inform", year="2020", month="Mar", day="31", volume="8", number="3", pages="e17984", keywords="natural language processing", keywords="machine learning", keywords="medical informatics", keywords="medical informatics applications", abstract="Background: Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigated the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified 110 relevant studies and extracted information about text data used to support machine learning, NLP tasks supported, and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation, and any relevant statistics. Results: The majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents, with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing the predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable because of the sensitive nature of data considered. Besides the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The majority of studies focused on text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management, and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation. ", doi="10.2196/17984", url="http://medinform.jmir.org/2020/3/e17984/", url="http://www.ncbi.nlm.nih.gov/pubmed/32229465" } @Article{info:doi/10.2196/13042, author="Mold, Freda and Hendy, Jane and Lai, Yi-Ling and de Lusignan, Simon", title="Electronic Consultation in Primary Care Between Providers and Patients: Systematic Review", journal="JMIR Med Inform", year="2019", month="Dec", day="3", volume="7", number="4", pages="e13042", keywords="referral and consultation", keywords="health services accessibility", keywords="primary health care", keywords="general practice", keywords="patient access to records", keywords="patient portals", keywords="Web-based access", abstract="Background: Governments and health care providers are keen to find innovative ways to deliver care more efficiently. Interest in electronic consultation (e-consultation) has grown, but the evidence of benefit is uncertain. Objective: This study aimed to assess the evidence of delivering e-consultation using secure email and messaging or video links in primary care. Methods: A systematic review was conducted on the use and application of e-consultations in primary care. We searched 7 international databases (MEDLINE, EMBASE, CINAHL, Cochrane Library, PsycINFO, EconLit, and Web of Science; 1999-2017), identifying 52 relevant studies. Papers were screened against a detailed inclusion and exclusion criteria. Independent dual data extraction was conducted and assessed for quality. The resulting evidence was synthesized using thematic analysis. Results: This review included 57 studies from a range of countries, mainly the United States (n=30) and the United Kingdom (n=13). There were disparities in uptake and utilization toward more use by younger, employed adults. Patient responses to e-consultation were mixed. Patients reported satisfaction with services and improved self-care, communication, and engagement with clinicians. Evidence for the acceptability and ease of use was strong, especially for those with long-term conditions and patients located in remote regions. However, patients were concerned about the privacy and security of their data. For primary health care staff, e-consultation delivers challenges around time management, having the correct technological infrastructure, whether it offers a comparable standard of clinical quality, and whether it improves health outcomes. Conclusions: E-consultations may improve aspects of care delivery, but the small scale of many of the studies and low adoption rates leave unanswered questions about usage, quality, cost, and sustainability. We need to improve e-consultation implementation, demonstrate how e-consultations will not increase disparities in access, provide better reassurance to patients about privacy, and incorporate e-consultation as part of a manageable clinical workflow. ", doi="10.2196/13042", url="http://medinform.jmir.org/2019/4/e13042/", url="http://www.ncbi.nlm.nih.gov/pubmed/31793888" } @Article{info:doi/10.2196/12660, author="Masud, Rafia and Al-Rei, Mona and Lokker, Cynthia", title="Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review", journal="JMIR Med Inform", year="2019", month="Jul", day="18", volume="7", number="3", pages="e12660", keywords="computer-aided detection", keywords="machine learning", keywords="screening mammography", keywords="breast cancer", keywords="radiology", keywords="implementation", abstract="Background: With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked. Objective: The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use. Methods: The MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed. Results: A total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes. Conclusions: There is a gap in the literature between CAD's well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use. ", doi="10.2196/12660", url="http://medinform.jmir.org/2019/3/e12660/", url="http://www.ncbi.nlm.nih.gov/pubmed/31322128" } @Article{info:doi/10.2196/12607, author="Dobrow, J. Mark and Bytautas, P. Jessica and Tharmalingam, Sukirtha and Hagens, Simon", title="Interoperable Electronic Health Records and Health Information Exchanges: Systematic Review", journal="JMIR Med Inform", year="2019", month="Jun", day="06", volume="7", number="2", pages="e12607", keywords="health information exchange", keywords="electronic health record", keywords="interoperability", keywords="use", keywords="impact", keywords="systematic review", abstract="Background: As the availability of interoperable electronic health records (iEHRs) or health information exchanges (HIEs) continues to increase, there is greater need and opportunity to assess the current evidence base on what works and what does not regarding the adoption, use, and impact of iEHRs. Objective: The purpose of this project is to assess the international evidence base on the adoption, use, and impact of iEHRs. Methods: We conducted a systematic review, searching multiple databases---MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL)---with supplemental searches conducted in Google Scholar and grey literature sources (ie, Google, Grey Literature Report, and OpenGrey). All searches were conducted in January and February 2017. Articles were eligible for inclusion if they were published in English, were published from 2006 to 2017, and were either an original research study or a literature review. In order to be included, articles needed to focus on iEHRs and HIEs across multiple health care settings, as well as on the impact and effectiveness of iEHR adoption and use. Results: We included 130 articles in the synthesis (113 primary studies, 86.9\%; 17 reviews, 13.1\%), with the majority focused on the United States (88/130, 67.7\%). The primary studies focused on a wide range of health care settings; the three most prevalent settings studied included acute care (59/113, 52.2\%), primary care (44/113, 38.9\%), and emergency departments (34/113, 30.1\%). We identified 29 distinct measurement items in the 113 primary studies that were linked to 522 specific measurement outcomes. Productivity and quality were the two evaluation dimensions that received the most attention, accounting for 14 of 29 (48\%) measurement items and 306 of 522 (58.6\%) measurement outcomes identified. Overall, the majority of the 522 measurement outcomes were positive (298/522, 57.1\%). We also identified 17 reviews on iEHR use and impact, 6 (35\%) that focused on barriers and facilitators to adoption and implementation and 11 (65\%) that focused on benefits and impacts, with the more recent reviews finding little generalizable evidence of benefit and impact. Conclusions: This review captures the status of an evolving and active field focused on the use and impact of iEHRs. While the overall findings suggest many positive impacts, the quality of the primary studies were not evaluated systematically. When broken down by specific measurement item, the results directed attention both to measurement outcomes that were consistently positive and others that were mostly negative or equivocal. ", doi="10.2196/12607", url="http://medinform.jmir.org/2019/2/e12607/", url="http://www.ncbi.nlm.nih.gov/pubmed/31172961" } @Article{info:doi/10.2196/12239, author="Sheikhalishahi, Seyedmostafa and Miotto, Riccardo and Dudley, T. Joel and Lavelli, Alberto and Rinaldi, Fabio and Osmani, Venet", title="Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review", journal="JMIR Med Inform", year="2019", month="Apr", day="27", volume="7", number="2", pages="e12239", keywords="electronic health records", keywords="clinical notes", keywords="chronic diseases", keywords="natural language processing", keywords="machine learning", keywords="deep learning", keywords="heart disease", keywords="stroke", keywords="cancer", keywords="diabetes", keywords="lung disease", abstract="Background: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective: The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using ``clinical notes,'' ``natural language processing,'' and ``chronic disease'' and their variations as keywords to maximize coverage of the articles. Results: Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions: Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora. ", doi="10.2196/12239", url="http://medinform.jmir.org/2019/2/e12239/", url="http://www.ncbi.nlm.nih.gov/pubmed/31066697" } @Article{info:doi/10.2196/13445, author="Aboueid, Stephanie and Liu, H. Rebecca and Desta, Negussie Binyam and Chaurasia, Ashok and Ebrahim, Shanil", title="The Use of Artificially Intelligent Self-Diagnosing Digital Platforms by the General Public: Scoping Review", journal="JMIR Med Inform", year="2019", month="May", day="01", volume="7", number="2", pages="e13445", keywords="diagnosis", keywords="artificial intelligence", keywords="symptom checkers", keywords="diagnostic self evaluation", keywords="self-care", abstract="Background: Self-diagnosis is the process of diagnosing or identifying a medical condition in oneself. Artificially intelligent digital platforms for self-diagnosis are becoming widely available and are used by the general public; however, little is known about the body of knowledge surrounding this technology. Objective: The objectives of this scoping review were to (1) systematically map the extent and nature of the literature and topic areas pertaining to digital platforms that use computerized algorithms to provide users with a list of potential diagnoses and (2) identify key knowledge gaps. Methods: The following databases were searched: PubMed (Medline), Scopus, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers, Google Scholar, Open Grey, and ProQuest Dissertations and Theses. The search strategy was developed and refined with the assistance of a librarian and consisted of 3 main concepts: (1) self-diagnosis; (2) digital platforms; and (3) public or patients. The search generated 2536 articles from which 217 were duplicates. Following the Tricco et al 2018 checklist, 2 researchers screened the titles and abstracts (n=2316) and full texts (n=104), independently. A total of 19 articles were included for review, and data were retrieved following a data-charting form that was pretested by the research team. Results: The included articles were mainly conducted in the United States (n=10) or the United Kingdom (n=4). Among the articles, topic areas included accuracy or correspondence with a doctor's diagnosis (n=6), commentaries (n=2), regulation (n=3), sociological (n=2), user experience (n=2), theoretical (n=1), privacy and security (n=1), ethical (n=1), and design (n=1). Individuals who do not have access to health care and perceive to have a stigmatizing condition are more likely to use this technology. The accuracy of this technology varied substantially based on the disease examined and platform used. Women and those with higher education were more likely to choose the right diagnosis out of the potential list of diagnoses. Regulation of this technology is lacking in most parts of the world; however, they are currently under development. Conclusions: There are prominent research gaps in the literature surrounding the use of artificially intelligent self-diagnosing digital platforms. Given the variety of digital platforms and the wide array of diseases they cover, measuring accuracy is cumbersome. More research is needed to understand the user experience and inform regulations. ", doi="10.2196/13445", url="http://medinform.jmir.org/2019/2/e13445/", url="http://www.ncbi.nlm.nih.gov/pubmed/31042151" } @Article{info:doi/10.2196/11496, author="Kruse, Clemens and Pesek, Brandon and Anderson, Megan and Brennan, Kacey and Comfort, Hilary", title="Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review", journal="JMIR Med Inform", year="2019", month="Mar", day="20", volume="7", number="1", pages="e11496", keywords="telemedicine", keywords="COPD", keywords="chronic disease", abstract="Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases and its prevalence is increasing in the United States. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations. Objective: The objective of this review is to evaluate the effectiveness of telemonitoring to manage COPD. Researchers want to determine how telemonitoring has been used to observe COPD and we are hoping this will lead to more research in telemonitoring of this disease. Methods: This review was conducted in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Authors performed a systematic review of the PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to obtain relevant articles. Articles were then accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD. Results: Results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 out of 29 (45\%) articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 (38\%) indicated no improvement. Authors identified the following facilitators: reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost. Conclusions: The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations. ", doi="10.2196/11496", url="http://medinform.jmir.org/2019/1/e11496/", url="http://www.ncbi.nlm.nih.gov/pubmed/30892276" } @Article{info:doi/10.2196/medinform.7601, author="Trtovac, Dino and Lee, Joon", title="The Use of Technology in Identifying Hospital Malnutrition: Scoping Review", journal="JMIR Med Inform", year="2018", month="Jan", day="19", volume="6", number="1", pages="e4", keywords="hospital malnutrition", keywords="technology-driven health care", keywords="malnutrition detection", keywords="nutrition diagnosis", keywords="malnutrition assessment", keywords="food-intake monitoring", keywords="automated data", keywords="malnutrition", keywords="nutritional status", keywords="nutrition assessment", abstract="Background: Malnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition. Objective: Our objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes. Methods: We designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases. Results: We retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting. Conclusions: Most articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers' workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition. ", doi="10.2196/medinform.7601", url="https://medinform.jmir.org/2018/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/29351894" } @Article{info:doi/10.2196/medinform.8131, author="Syzdykova, Assel and Malta, Andr{\'e} and Zolfo, Maria and Diro, Ermias and Oliveira, Luis Jos{\'e}", title="Open-Source Electronic Health Record Systems for Low-Resource Settings: Systematic Review", journal="JMIR Med Inform", year="2017", month="Nov", day="13", volume="5", number="4", pages="e44", keywords="electronic health record", keywords="EHR", keywords="software", keywords="eHealth", keywords="open source", abstract="Background: Despite the great impact of information and communication technologies on clinical practice and on the quality of health services, this trend has been almost exclusive to developed countries, whereas countries with poor resources suffer from many economic and social issues that have hindered the real benefits of electronic health (eHealth) tools. As a component of eHealth systems, electronic health records (EHRs) play a fundamental role in patient management and effective medical care services. Thus, the adoption of EHRs in regions with a lack of infrastructure, untrained staff, and ill-equipped health care providers is an important task. However, the main barrier to adopting EHR software in low- and middle-income countries is the cost of its purchase and maintenance, which highlights the open-source approach as a good solution for these underserved areas. Objective: The aim of this study was to conduct a systematic review of open-source EHR systems based on the requirements and limitations of low-resource settings. Methods: First, we reviewed existing literature on the comparison of available open-source solutions. In close collaboration with the University of Gondar Hospital, Ethiopia, we identified common limitations in poor resource environments and also the main requirements that EHRs should support. Then, we extensively evaluated the current open-source EHR solutions, discussing their strengths and weaknesses, and their appropriateness to fulfill a predefined set of features relevant for low-resource settings. Results: The evaluation methodology allowed assessment of several key aspects of available solutions that are as follows: (1) integrated applications, (2) configurable reports, (3) custom reports, (4) custom forms, (5) interoperability, (6) coding systems, (7) authentication methods, (8) patient portal, (9) access control model, (10) cryptographic features, (11) flexible data model, (12) offline support, (13) native client, (14) Web client,(15) other clients, (16) code-based language, (17) development activity, (18) modularity, (19) user interface, (20) community support, and (21) customization. The quality of each feature is discussed for each of the evaluated solutions and a final comparison is presented. Conclusions: There is a clear demand for open-source, reliable, and flexible EHR systems in low-resource settings. In this study, we have evaluated and compared five open-source EHR systems following a multidimensional methodology that can provide informed recommendations to other implementers, developers, and health care professionals. We hope that the results of this comparison can guide decision making when needing to adopt, install, and maintain an open-source EHR solution in low-resource settings. ", doi="10.2196/medinform.8131", url="http://medinform.jmir.org/2017/4/e44/", url="http://www.ncbi.nlm.nih.gov/pubmed/29133283" } @Article{info:doi/10.2196/medinform.6405, author="Staszewska, Anna and Zaki, Pearl and Lee, Joon", title="Computerized Decision Aids for Shared Decision Making in Serious Illness: Systematic Review", journal="JMIR Med Inform", year="2017", month="Oct", day="06", volume="5", number="4", pages="e36", keywords="decision making", keywords="decision aids", keywords="evidence-based medicine", keywords="user-computer interface", keywords="chronic disease", abstract="Background: Shared decision making (SDM) is important in achieving patient-centered care. SDM tools such as decision aids are intended to inform the patient. When used to assist in decision making between treatments, decision aids have been shown to reduce decisional conflict, increase ease of decision making, and increase modification of previous decisions. Objective: The purpose of this systematic review is to assess the impact of computerized decision aids on patient-centered outcomes related to SDM for seriously ill patients. Methods: PubMed and Scopus databases were searched to identify randomized controlled trials (RCTs) that assessed the impact of computerized decision aids on patient-centered outcomes and SDM in serious illness. Six RCTs were identified and data were extracted on study population, design, and results. Risk of bias was assessed by a modified Cochrane Risk of Bias Tool for Quality Assessment of Randomized Controlled Trials. Results: Six RCTs tested decision tools in varying serious illnesses. Three studies compared different computerized decision aids against each other and a control. All but one study demonstrated improvement in at least one patient-centered outcome. Computerized decision tools may reduce unnecessary treatment in patients with low disease severity in comparison with informational pamphlets. Additionally, electronic health record (EHR) portals may provide the opportunity to manage care from the home for individuals affected by illness. The quality of decision aids is of great importance. Furthermore, satisfaction with the use of tools is associated with increased patient satisfaction and reduced decisional conflict. Finally, patients may benefit from computerized decision tools without the need for increased physician involvement. Conclusions: Most computerized decision aids improved at least one patient-centered outcome. All RCTs identified were at a High Risk of Bias or Unclear Risk of Bias. Effort should be made to improve the quality of RCTs testing SDM aids in serious illness. ", doi="10.2196/medinform.6405", url="https://medinform.jmir.org/2017/4/e36/", url="http://www.ncbi.nlm.nih.gov/pubmed/28986341" } @Article{info:doi/10.2196/medinform.6323, author="Woldaregay, Zebene Ashenafi and Walderhaug, St{\aa}le and Hartvigsen, Gunnar", title="Telemedicine Services for the Arctic: A Systematic Review", journal="JMIR Med Inform", year="2017", month="Jun", day="28", volume="5", number="2", pages="e16", keywords="telemedicine", keywords="telehealth", keywords="health services accessibility", keywords="extreme cold", keywords="arctic regions", keywords="accidents", abstract="Background: Telemedicine services have been successfully used in areas where there are adequate infrastructures such as reliable power and communication lines. However, despite the increasing number of merchants and seafarers, maritime and Arctic telemedicine have had limited success. This might be linked with various factors such as lack of good infrastructure, lack of trained onboard personnel, lack of Arctic-enhanced telemedicine equipment, extreme weather conditions, remoteness, and other geographical challenges. Objective: The purpose of this review was to assess and analyze the current status of telemedicine services in the context of maritime conditions, extreme weather (ie, Arctic weather), and remote accidents and emergencies. Moreover, the paper aimed to identify successfully implemented telemedicine services in the Arctic region and in maritime settings and remote emergency situations and present state of the art systems for these areas. Finally, we identified the status quo of telemedicine services in the context of search and rescue (SAR) scenarios in these extreme conditions. Methods: A rigorous literature search was conducted between September 7 and October 28, 2015, through various online databases. Peer reviewed journals and articles were considered. Relevant articles were first identified by reviewing the title, keywords, and abstract for a preliminary filter with our selection criteria, and then we reviewed full-text articles that seemed relevant. Information from the selected literature was extracted based on some predefined categories, which were defined based on previous research and further elaborated upon via iterative brainstorming. Results: The initial hits were vetted using the title, abstract, and keywords, and we retrieved a total of 471 papers. After removing duplicates from the list, 422 records remained. Then, we did an independent assessment of the articles and screening based on the inclusion and exclusion criteria, which eliminated another 219 papers, leaving 203 relevant papers. After a full-text assessment, 36 articles were left, which were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: Despite the increasing number of fishermen and other seafarers, Arctic and maritime working conditions are mainly characterized by an absence of access to health care facilities. The condition is further aggravated for fishermen and seafarers who are working in the Arctic regions. In spite of the existing barriers and challenges, some telemedicine services have recently been successfully delivered in these areas. These services include teleconsultation (9/37, 24\%), teleradiology (8/37, 22\%), teledermatology and tele-education (3/37, 8\%), telemonitoring and telecardiology (telesonography) (1/37, 3\%), and others (10/37, 27\%). However, the use of telemedicine in relation to search and rescue (SAR) services is not yet fully exploited. Therefore, we foresee that these implemented and evaluated telemedicine services will serve as underlying models for the successful implementation of future search and rescue (SAR) services. ", doi="10.2196/medinform.6323", url="http://medinform.jmir.org/2017/2/e16/", url="http://www.ncbi.nlm.nih.gov/pubmed/28659257" } @Article{info:doi/10.2196/medinform.6959, author="Balikuddembe, S. Michael and Tumwesigye, M. Nazarius and Wakholi, K. Peter and Tyllesk{\"a}r, Thorkild", title="Computerized Childbirth Monitoring Tools for Health Care Providers Managing Labor: A Scoping Review", journal="JMIR Med Inform", year="2017", month="Jun", day="15", volume="5", number="2", pages="e14", keywords="childbirth", keywords="obstetric labor", keywords="fetal monitoring", keywords="medical informatics applications", keywords="systematic review", abstract="Background: Proper monitoring of labor and childbirth prevents many pregnancy-related complications. However, monitoring is still poor in many places partly due to the usability concerns of support tools such as the partograph. In 2011, the World Health Organization (WHO) called for the development and evaluation of context-adaptable electronic health solutions to health challenges. Computerized tools have penetrated many areas of health care, but their influence in supporting health staff with childbirth seems limited. Objective: The objective of this scoping review was to determine the scope and trends of research on computerized labor monitoring tools that could be used by health care providers in childbirth management. Methods: We used key terms to search the Web for eligible peer-reviewed and gray literature. Eligibility criteria were a computerized labor monitoring tool for maternity service providers and dated 2006 to mid-2016. Retrieved papers were screened to eliminate ineligible papers, and consensus was reached on the papers included in the final analysis. Results: We started with about 380,000 papers, of which 14 papers qualified for the final analysis. Most tools were at the design and implementation stages of development. Three papers addressed post-implementation evaluations of two tools. No documentation on clinical outcome studies was retrieved. The parameters targeted with the tools varied, but they included fetal heart (10 of 11 tools), labor progress (8 of 11), and maternal status (7 of 11). Most tools were designed for use in personal computers in low-resource settings and could be customized for different user needs. Conclusions: Research on computerized labor monitoring tools is inadequate. Compared with other labor parameters, there was preponderance to fetal heart monitoring and hardly any summative evaluation of the available tools. More research, including clinical outcomes evaluation of computerized childbirth monitoring tools, is needed. ", doi="10.2196/medinform.6959", url="http://medinform.jmir.org/2017/2/e14/", url="http://www.ncbi.nlm.nih.gov/pubmed/28619702" } @Article{info:doi/10.2196/medinform.6730, author="Sharafoddini, Anis and Dubin, A. Joel and Lee, Joon", title="Patient Similarity in Prediction Models Based on Health Data: A Scoping Review", journal="JMIR Med Inform", year="2017", month="Mar", day="03", volume="5", number="1", pages="e7", keywords="patient similarity", keywords="predictive modeling", keywords="health data", keywords="medical records", keywords="electronic health records", keywords="personalized medicine", keywords="data-driven prediction", keywords="review", abstract="Background: Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective: The aim is to summarize and review published studies describing computer-based approaches for predicting patients' future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods: The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results: After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions: Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes. ", doi="10.2196/medinform.6730", url="http://medinform.jmir.org/2017/1/e7/", url="http://www.ncbi.nlm.nih.gov/pubmed/28258046" } @Article{info:doi/10.2196/medinform.4514, author="Sawesi, Suhila and Rashrash, Mohamed and Phalakornkule, Kanitha and Carpenter, S. Janet and Jones, F. Josette", title="The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature", journal="JMIR Med Inform", year="2016", month="Jan", day="21", volume="4", number="1", pages="e1", keywords="patient engagement", keywords="patient behavior", keywords="technology", keywords="Internet", keywords="web-based", keywords="cell phone", keywords="social media", abstract="Background: Advancements in information technology (IT) and its increasingly ubiquitous nature expand the ability to engage patients in the health care process and motivate health behavior change. Objective: Our aim was to systematically review the (1) impact of IT platforms used to promote patients' engagement and to effect change in health behaviors and health outcomes, (2) behavior theories or models applied as bases for developing these interventions and their impact on health outcomes, (3) different ways of measuring health outcomes, (4) usability, feasibility, and acceptability of these technologies among patients, and (5) challenges and research directions for implementing IT platforms to meaningfully impact patient engagement and health outcomes. Methods: PubMed, Web of Science, PsycINFO, and Google Scholar were searched for studies published from 2000 to December 2014. Two reviewers assessed the quality of the included papers, and potentially relevant studies were retrieved and assessed for eligibility based on predetermined inclusion criteria. Results: A total of 170 articles met the inclusion criteria and were reviewed in detail. Overall, 88.8\% (151/170) of studies showed positive impact on patient behavior and 82.9\% (141/170) reported high levels of improvement in patient engagement. Only 47.1\% (80/170) referenced specific behavior theories and only 33.5\% (57/170) assessed the usability of IT platforms. The majority of studies used indirect ways to measure health outcomes (65.9\%, 112/170). Conclusions: In general, the review has shown that IT platforms can enhance patient engagement and improve health outcomes. Few studies addressed usability of these interventions, and the reason for not using specific behavior theories remains unclear. Further research is needed to clarify these important questions. In addition, an assessment of these types of interventions should be conducted based on a common framework using a large variety of measurements; these measurements should include those related to motivation for health behavior change, long-standing adherence, expenditure, satisfaction, and health outcomes. ", doi="10.2196/medinform.4514", url="http://medinform.jmir.org/2016/1/e1/", url="http://www.ncbi.nlm.nih.gov/pubmed/26795082" }