%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e70983 %T Dying in Darkness: Deviations From Data Sharing Ethics in the US Public Health System and the Data Genocide of American Indian and Alaska Native Communities %A Schmit,Cason D %A O’Connell,Meghan Curry %A Shewbrooks,Sarah %A Abourezk,Charles %A Cochlin,Fallon J %A Doerr,Megan %A Kum,Hye-Chung %+ Department of Health Policy and Management, School of Public Health, Texas A&M University, 212 Adriance Lab Rd, College Station, TX, 77843, United States, 1 9794360277, schmit@tamu.edu %K ethics %K information dissemination %K indigenous peoples %K public health surveillance %K privacy %K data sharing %K deidentification %K data anonymization %K public health ethics %K data governance %D 2025 %7 26.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X Tribal governments and Tribal Epidemiology Centers face persistent challenges in obtaining the public health data that are essential to accomplishing their legal and ethical duties to promote health in American Indian and Alaska Native communities. We assessed the ethical implications of current impediments to data sharing among federal, state, and Tribal public health partners. Public health ethics obligates public health data sharing and opposes data collection without dissemination to affected communities. Privacy practices, like deidentification and data suppression, often obstruct data access, disproportionately affect American Indian and Alaska Native populations, and exacerbate health disparities. The 2020-2024 syphilis outbreak illustrates how restricted data access impedes effective public health responses. These practices represent a source of structuralized violence throughout the US public health system that contributes to the data genocide of American Indian and Alaska Native populations. Good governance practices like transparent data practices and the establishment of a social license (ie, the informal permission of a community to collect and use data) is essential to ethically balancing collective well-being with individual privacy in public health. %M 40138677 %R 10.2196/70983 %U https://www.jmir.org/2025/1/e70983 %U https://doi.org/10.2196/70983 %U http://www.ncbi.nlm.nih.gov/pubmed/40138677 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63902 %T Exploring Physicians’ Dual Perspectives on the Transition From Free Text to Structured and Standardized Documentation Practices: Interview and Participant Observational Study %A Golburean,Olga %A Pedersen,Rune %A Melby,Line %A Faxvaag,Arild %+ Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, NTNU-Norwegian University of Science and Technology, Elgeseter Gate 28, Trondheim, 7030, Norway, 47 91787389, olga.golburean@ntnu.no %K documentation %K documentation practice %K standardized documentation %K structured documentation %K secondary use of data %K interoperability %K electronic health record %K EHR %D 2025 %7 21.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Clinical documentation plays a crucial role in providing and coordinating care. Despite the widespread adoption of electronic health record (EHR) systems, many end users still document clinical data in a manner similar to traditional paper-based records. To fully leverage the benefits of EHR systems, it is necessary to adopt new documentation approaches that facilitate easy access to information at the point of care and seamless exchange of information across health care facilities. Objective: We aimed to evaluate how the transition from an older EHR system to a cross-institutional EHR system impacts physicians’ documentation practices and gain a deeper understanding of the factors influencing their choice between free text and structured and standardized documentation methods. Methods: A qualitative study was conducted between September 2023 and January 2024. It involved participant observations and individual semistructured interviews with physicians at a university hospital in Norway. Data were analyzed using reflexive thematic analysis. Results: The analysis revealed 3 main themes. First, physicians encountered challenges during the implementation phase of the new EHR system due to its complexity and their unfamiliarity with its use. However, with time, physicians gradually adopted new documentation processes. This integration or adoption primarily occurred by learning through practical experience and collaborative knowledge exchange with their peers. Second, although the implementation of the new EHR system had increased structured and standardized clinical documentation, free text remained the preferred method, with some exceptions. In addition, the fact that many physicians still relied on free-text documentation created a sense of distrust among them toward some of the standardized clinical data. Finally, the informants had mixed perceptions of Systematized Nomenclature of Medicine–Clinical Terms. Some viewed it as a more nuanced terminology system, while others found it more complex. Most informants found using templates for routine procedures beneficial as it saved time in the documentation process and ensured that all necessary parameters and documentation requirements were met. Conclusions: The study findings revealed that physicians’ acceptance of new documentation processes is influenced by various social and technological factors. These factors include previous documentation experiences, perceived benefits, familiarity with the EHR system, time constraints, and user-friendliness of the system. While physicians generally have a positive attitude toward using templates for routine procedures, they often create their own templates, and data within these templates are documented in a free-text format. To address this, health care organizations should consider implementing common standardized or semistandardized templates to reduce disparities in documentation, enhance data recording, and ensure adherence to guidelines. Furthermore, to facilitate the transition to the new documentation processes, we recommend providing physicians with customized training programs and platforms for tacit knowledge exchange. %M 40117572 %R 10.2196/63902 %U https://formative.jmir.org/2025/1/e63902 %U https://doi.org/10.2196/63902 %U http://www.ncbi.nlm.nih.gov/pubmed/40117572 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 4 %N %P e55277 %T Creation of Scientific Response Documents for Addressing Product Medical Information Inquiries: Mixed Method Approach Using Artificial Intelligence %A Lau,Jerry %A Bisht,Shivani %A Horton,Robert %A Crisan,Annamaria %A Jones,John %A Gantotti,Sandeep %A Hermes-DeSantis,Evelyn %+ phactMI, 5931 NW 1st Place, Gainesville, FL, 32607, United States, 1 2155881585, evelyn@phactmi.org %K AI %K LLM %K GPT %K biopharmaceutical %K medical information %K content generation %K artificial intelligence %K pharmaceutical %K scientific response %K documentation %K information %K clinical data %K strategy %K reference %K feasibility %K development %K machine learning %K large language model %K accuracy %K context %K traceability %K accountability %K survey %K scientific response documentation %K SRD %K benefit %K content generator %K content analysis %K Generative Pre-trained Transformer %D 2025 %7 13.3.2025 %9 Original Paper %J JMIR AI %G English %X Background: Pharmaceutical manufacturers address health care professionals’ information needs through scientific response documents (SRDs), offering evidence-based answers to medication and disease state questions. Medical information departments, staffed by medical experts, develop SRDs that provide concise summaries consisting of relevant background information, search strategies, clinical data, and balanced references. With an escalating demand for SRDs and the increasing complexity of therapies, medical information departments are exploring advanced technologies and artificial intelligence (AI) tools like large language models (LLMs) to streamline content development. While AI and LLMs show promise in generating draft responses, a synergistic approach combining an LLM with traditional machine learning classifiers in a series of human-supervised and -curated steps could help address limitations, including hallucinations. This will ensure accuracy, context, traceability, and accountability in the development of the concise clinical data summaries of an SRD. Objective: This study aims to quantify the challenges of SRD development and develop a framework exploring the feasibility and value addition of integrating AI capabilities in the process of creating concise summaries for an SRD. Methods: To measure the challenges in SRD development, a survey was conducted by phactMI, a nonprofit consortium of medical information leaders in the pharmaceutical industry, assessing aspects of SRD creation among its member companies. The survey collected data on the time and tediousness of various activities related to SRD development. Another working group, consisting of medical information professionals and data scientists, used AI to aid SRD authoring, focusing on data extraction and abstraction. They used logistic regression on semantic embedding features to train classification models and transformer-based summarization pipelines to generate concise summaries. Results: Of the 33 companies surveyed, 64% (21/33) opened the survey, and 76% (16/21) of those responded. On average, medical information departments generate 614 new documents and update 1352 documents each year. Respondents considered paraphrasing scientific articles to be the most tedious and time-intensive task. In the project’s second phase, sentence classification models showed the ability to accurately distinguish target categories with receiver operating characteristic scores ranging from 0.67 to 0.85 (all P<.001), allowing for accurate data extraction. For data abstraction, the comparison of the bilingual evaluation understudy (BLEU) score and semantic similarity in the paraphrased texts yielded different results among reviewers, with each preferring different trade-offs between these metrics. Conclusions: This study establishes a framework for integrating LLM and machine learning into SRD development, supported by a pharmaceutical company survey emphasizing the challenges of paraphrasing content. While machine learning models show potential for section identification and content usability assessment in data extraction and abstraction, further optimization and research are essential before full-scale industry implementation. The working group’s insights guide an AI-driven content analysis; address limitations; and advance efficient, precise, and responsive frameworks to assist with pharmaceutical SRD development. %M 40080808 %R 10.2196/55277 %U https://ai.jmir.org/2025/1/e55277 %U https://doi.org/10.2196/55277 %U http://www.ncbi.nlm.nih.gov/pubmed/40080808 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e65590 %T Data Interoperability in COVID-19 Vaccine Trials: Methodological Approach in the VACCELERATE Project %A Malik,Salma %A Dorothea,Zoi Pana %A Argyropoulos,Christos D %A Themistocleous,Sophia %A Macken,Alan J %A Valdenmaiier,Olena %A Scheckenbach,Frank %A Bardach,Elena %A Pfeiffer,Andrea %A Loens,Katherine %A Ochando,Jordi Cano %A Cornely,Oliver A %A Demotes-Mainard,Jacques %A Contrino,Sergio %A Felder,Gerd %+ European Clinical Research Infrastructure Network, 30 Bd Saint-Jacques, Paris, 75014, France, 33 01 80 05 86 29, salma.malik@ecrin.org %K interoperability %K metadata %K data management %K clinical trials %K protocol %K harmonization %K adult %K pediatric %K systems %K standards %D 2025 %7 7.3.2025 %9 Original Paper %J JMIR Med Inform %G English %X Background: Data standards are not only key to making data processing efficient but also fundamental to ensuring data interoperability. When clinical trial data are structured according to international standards, they become significantly easier to analyze, reducing the efforts required for data cleaning, preprocessing, and secondary use. A common language and a shared set of expectations facilitate interoperability between systems and devices. Objective: The main objectives of this study were to identify commonalities and differences in clinical trial metadata, protocols, and data collection systems/items within the VACCELERATE project. Methods: To assess the degree of interoperability achieved in the project and suggest methodological improvements, interoperable points were identified based on the core outcome areas—immunogenicity, safety, and efficacy (clinical/physiological). These points were emphasized in the development of the master protocol template and were manually compared in the following ways: (1) summaries, objectives, and end points in the protocols of 3 VACCELERATE clinical trials (EU-COVAT-1_AGED, EU-COVAT-2_BOOSTAVAC, and EU-COVPT-1_CoVacc) against the master protocol template; (2) metadata of all 3 clinical trials; and (3) evaluations from a questionnaire survey regarding differences in data management systems and structures that enabled data exchange within the VACCELERATE network. Results: The noncommonalities identified in the protocols and metadata were attributed to differences in populations, variations in protocol design, and vaccination patterns. The detailed metadata released for all 3 vaccine trials were clearly structured using internal standards, terminology, and the general approach of Clinical Data Acquisition Standards Harmonisation (CDASH) for data collection (eg, on electronic case report forms). VACCELERATE benefited significantly from the selection of the Clinical Trials Centre Cologne as the sole data management provider. With system database development coordinated by a single individual and no need for coordination among different trial units, a high degree of uniformity was achieved automatically. The harmonized transfer of data to all sites, using well-established methods, enabled quick exchanges and provided a relatively secure means of data transfer. Conclusions: This study demonstrated that using master protocols can significantly enhance trial operational efficiency and data interoperability, provided that similar infrastructure and data management procedures are adopted across multiple trials. To further improve data interoperability and facilitate interpretation and analysis, shared data should be structured, described, formatted, and stored using widely recognized data and metadata standards. Trial Registration: EudraCT 2021-004526-29; https://www.clinicaltrialsregister.eu/ctr-search/trial/2021-004526-29/DE/; 2021-004889-35; https://www.clinicaltrialsregister.eu/ctr-search/search?query=eudract_number:2021-004889-35; and 2021-004526-29; https://www.clinicaltrialsregister.eu/ctr-search/search?query=eudract_number:2021-004526-29 %M 40056469 %R 10.2196/65590 %U https://medinform.jmir.org/2025/1/e65590 %U https://doi.org/10.2196/65590 %U http://www.ncbi.nlm.nih.gov/pubmed/40056469 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e57858 %T Assessing Digital Maturity of Hospitals: Viewpoint Comparing National Approaches in Five Countries %A Cresswell,Kathrin %A Jahn,Franziska %A Silsand,Line %A Woods,Leanna %A Postema,Tim %A Logan,Marion %A Malkic,Sevala %A Ammenwerth,Elske %+ Usher Institute, The University of Edinburgh, Usher Building, 5-7 Little France Road, Edinburgh BioQuarter-Gate 3, Edinburgh, EH16 4UX, United Kingdom, 44 (0)131 651 7869, Kathrin.Cresswell@ed.ac.uk %K digital maturity %K hospitals %K assessment %K decision making %K health systems %K infancy %K Australia %K Australian %K data collection %K data %K qualitative thematic analysis %K self-reporting %K practical feasibility %K long-term tracking %K local data %K monitoring %D 2025 %7 6.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X Digital maturity assessments can inform strategic decision-making. However, national approaches to assessing the digital maturity of health systems are in their infancy, and there is limited insight into the context and processes associated with such assessments. This viewpoint article describes and compares national approaches to assessing the digital maturity of hospitals. We reviewed 5 national approaches to assessing the digital maturity of hospitals in Queensland (Australia), Germany, the Netherlands, Norway, and Scotland, exploring context, drivers, and approaches to measure digital maturity in each country. We observed a common focus on interoperability, and assessment findings were used to shape national digital health strategies. Indicators were broadly aligned, but 4 of 5 countries developed their own tailored indicator sets. Key topic areas across countries included interoperability, capabilities, leadership, governance, and infrastructure. Analysis of indicators was centralized, but data were shared with participating organizations. Only 1 setting conducted an academic evaluation. Major challenges of digital maturity assessment included the high cost and time required for data collection, questions about measurement accuracy, difficulties in consistent long-term tracking of indicators, and potential biases due to self-reporting. We also observed tensions between the practical feasibility of the process with the depth and breadth required by the complexity of the topic and tensions between national and local data needs. There are several key challenges in assessing digital maturity in hospitals nationally that influence the validity and reliability of output. These need to be explicitly acknowledged when making decisions informed by assessments and monitored over time. %M 40053724 %R 10.2196/57858 %U https://www.jmir.org/2025/1/e57858 %U https://doi.org/10.2196/57858 %U http://www.ncbi.nlm.nih.gov/pubmed/40053724 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e65549 %T Correlation Between Diagnosis-Related Group Weights and Nursing Time in the Cardiology Department: Cross-Sectional Study %A Lv,Chen %A Gong,Yi-Hong %A Wang,Xiu-Hua %A An,Jun %A Wang,Qian %A Han,Jing %A Chen,Xiao-Feng %K diagnosis-related groups %K nursing time %K workload %K human resources %K nursing %D 2025 %7 4.3.2025 %9 %J JMIR Med Inform %G English %X Background: Diagnosis-related group (DRG) payment has become the main form of medical expense settlements, and its application is becoming increasingly extensive. Objective: This study aimed to explore the correlation between DRG weights and nursing time and to develop a predictive model for nursing time in the cardiology department based on DRG weights and other factors. Methods: A convenience sampling method was used to select patients who were hospitalized in the cardiology ward of Beijing Chest Hospital between April 2023 and April 2024. Nursing time was measured by direct and indirect nursing time. To determine the distributions of nursing time based on different demographics, a Pearson correlation was used to analyze the relationship between DRG weight and nursing time, and a multiple linear regression was used to determine the influencing factors of total nursing time. Results: A total of 103 subjects were included in this study. The DRG weights were positively correlated with direct nursing time (r=0.480; P<.001), indirect nursing time (r=0.394; P<.001), and total nursing time (r=0.448; P<.001). Moreover, age was positively correlated with the 3 nursing times (direct: r=0.235; indirect: r=0.192; total: r=0.235; all P<.001). The activities of daily living (ADL) score on admission was negatively correlated with the 3 nursing times (direct: r=−0.316; indirect: r=−0.252; total: r=−0.301; all P<.001). In addition, the nursing level on the first day of admission was positively correlated with the 3 nursing times (direct: r=0.333; indirect: r=0.332; total: r=0.352; all P<.001). Furthermore, the multivariate analysis found that the nursing level on the first day of admission, complications or comorbidities, DRG weight, and ADL score on admission were the influencing factors of nursing time (R2=0.328; F5,97=69.58; P<.001). Conclusions: DRG weight showed a strong correlation with nursing time and could be used to predict nursing time, which may assist in nursing resource allocation in cardiology departments. %R 10.2196/65549 %U https://medinform.jmir.org/2025/1/e65549 %U https://doi.org/10.2196/65549 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 14 %N %P e51598 %T How to Design Electronic Case Report Form (eCRF) Questions to Maximize Semantic Interoperability in Clinical Research %A Rinaldi,Eugenia %A Stellmach,Caroline %A Thun,Sylvia %K case report form %K CRF %K interoperability %K standard data model %K data format %K metadata %K core data elements %K data quality %D 2025 %7 3.3.2025 %9 %J Interact J Med Res %G English %X Case report forms (CRFs) are the instruments used by research organizations worldwide to collect information about patients and study participants with the purpose of answering specific questions, assessing the efficacy and safety of medical products, and in general improving prevention and treatment in health care. To obtain significant research results out of the collected data, CRFs should be designed following the recommendations issued by regulatory authorities. However, we believe that semantic interoperability in CRFs has not yet been properly addressed. Within an international consortium comprising several COVID-19 cohorts, we scrutinized the questions included in the different CRFs with the purpose of establishing semantic interoperability across the different study data elements so that data could be merged and jointly analyzed. We realized that similar concepts were structured very differently across the different CRFs, making it hard to find and match the information. Based on the experience acquired, we developed 5 guiding principles on how to design CRFs to support semantic interoperability and increase data quality while also facilitating the sharing of data. Our aim in this viewpoint is to provide general suggestions that, in our opinion, should support researchers in designing CRFs. We conclude by urging authorities to establish an international coordination board for standards and interoperable clinical study data with competence in clinical data, interoperability standards, and data protection as part of a preparedness plan for future pandemics or other health threats. %R 10.2196/51598 %U https://www.i-jmr.org/2025/1/e51598 %U https://doi.org/10.2196/51598 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e67984 %T A Validation Tool (VaPCE) for Postcoordinated SNOMED CT Expressions: Development and Usability Study %A Ohlsen,Tessa %A Hofer,Viola %A Ingenerf,Josef %K SNOMED CT %K PCE %K postcoordination %K FHIR %K validation %K postcoordinated expression %K Fast Healthcare Interoperability Resource %D 2025 %7 28.2.2025 %9 %J JMIR Med Inform %G English %X Background: The digitalization of health care has increased the demand for efficient data exchange, emphasizing semantic interoperability. SNOMED Clinical Terms (SNOMED CT), a comprehensive terminology with over 360,000 medical concepts, supports this need. However, it cannot cover all medical scenarios, particularly in complex cases. To address this, SNOMED CT allows postcoordination, where users combine precoordinated concepts with new expressions. Despite SNOMED CT’s potential, the creation and validation of postcoordinated expressions (PCEs) remain challenging due to complex syntactic and semantic rules. Objective: This work aims to develop a tool that validates postcoordinated SNOMED CT expressions, focusing on providing users with detailed, automated correction instructions for syntactic and semantic errors. The goal is not just validation, but also offering user-friendly, actionable suggestions for improving PCEs. Methods: A tool was created using the Fast Healthcare Interoperability Resource (FHIR) service $validate-code and the terminology server Ontoserver to check the correctness of PCEs. When errors are detected, the tool processes the SNOMED CT Concept Model in JSON format and applies predefined error categories. For each error type, specific correction suggestions are generated and displayed to users. The key added value of the tool is in generating specific correction suggestions for each identified error, which are displayed to the users. The tool was integrated into a web application, where users can validate individual PCEs or bulk-upload files. The tool was tested with real existing PCEs, which were used as input and validated. In the event of errors, appropriate error messages were generated as output. Results: In the validation of 136 PCEs from 304 FHIR Questionnaires, 18 (13.2%) PCEs were invalid, with the most common errors being invalid attribute values. Additionally, 868 OncoTree codes were evaluated, resulting in 161 (20.9%) PCEs containing inactive concepts, which were successfully replaced with valid alternatives. A user survey reflects a favorable evaluation of the tool’s functionality. Participants found the error categorization and correction suggestions to be precise, offering clear guidance for addressing issues. However, there is potential for enhancement, particularly regarding the level of detail in the error messages. Conclusions: The validation tool significantly improves the accuracy of postcoordinated SNOMED CT expressions by not only identifying errors but also offering detailed correction instructions. This approach supports health care professionals in ensuring that their PCEs are syntactically and semantically valid, enhancing data quality and interoperability across systems. %R 10.2196/67984 %U https://medinform.jmir.org/2025/1/e67984 %U https://doi.org/10.2196/67984 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e64422 %T Analysis of Retinal Thickness in Patients With Chronic Diseases Using Standardized Optical Coherence Tomography Data: Database Study Based on the Radiology Common Data Model %A Park,ChulHyoung %A Lee,So Hee %A Lee,Da Yun %A Choi,Seoyoon %A You,Seng Chan %A Jeon,Ja Young %A Park,Sang Jun %A Park,Rae Woong %K data standardization %K ophthalmology %K radiology %K optical coherence tomography %K retinal thickness %D 2025 %7 21.2.2025 %9 %J JMIR Med Inform %G English %X Background: The Observational Medical Outcome Partners-Common Data Model (OMOP-CDM) is an international standard for harmonizing electronic medical record (EMR) data. However, since it does not standardize unstructured data, such as medical imaging, using this data in multi-institutional collaborative research becomes challenging. To overcome this limitation, extensions such as the Radiology Common Data Model (R-CDM) have emerged to include and standardize these data types. Objective: This work aims to demonstrate that by standardizing optical coherence tomography (OCT) data into an R-CDM format, multi-institutional collaborative studies analyzing changes in retinal thickness in patients with long-standing chronic diseases can be performed efficiently. Methods: We standardized OCT images collected from two tertiary hospitals for research purposes using the R-CDM. As a proof of concept, we conducted a comparative analysis of retinal thickness between patients who have chronic diseases and those who have not. Patients diagnosed or treated for retinal and choroidal diseases, which could affect retinal thickness, were excluded from the analysis. Using the existing OMOP-CDM at each institution, we extracted cohorts of patients with chronic diseases and control groups, performing large-scale 1:2 propensity score matching (PSM). Subsequently, we linked the OMOP-CDM and R-CDM to extract the OCT image data of these cohorts and analyzed central macular thickness (CMT) and retinal nerve fiber layer (RNFL) thickness using a linear mixed model. Results: OCT data of 261,874 images from Ajou University Medical Center (AUMC) and 475,626 images from Seoul National University Bundang Hospital (SNUBH) were standardized in the R-CDM format. The R-CDM databases established at each institution were linked with the OMOP-CDM database. Following 1:2 PSM, the type 2 diabetes mellitus (T2DM) cohort included 957 patients, and the control cohort had 1603 patients. During the follow-up period, significant reductions in CMT were observed in the T2DM cohorts at AUMC (P=.04) and SNUBH (P=.007), without significant changes in RNFL thickness (AUMC: P=.56; SNUBH: P=.39). Notably, a significant reduction in CMT during the follow-up was observed only at AUMC in the hypertension cohort, compared to the control group (P=.04); no other significant differences in retinal thickness were found in the remaining analyses. Conclusions: The significance of our study lies in demonstrating the efficiency of multi-institutional collaborative research that simultaneously uses clinical data and medical imaging data by leveraging the OMOP-CDM for standardizing EMR data and the R-CDM for standardizing medical imaging data. %R 10.2196/64422 %U https://medinform.jmir.org/2025/1/e64422 %U https://doi.org/10.2196/64422 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e60017 %T Visualizing Patient Pathways and Identifying Data Repositories in a UK Neurosciences Center: Exploratory Study %A Knight,Jo %A Chandrabalan,Vishnu Vardhan %A Emsley,Hedley C A %K health data %K business process monitoring notation %K neurology %K process monitoring %K patient pathway %K clinical pathway %K patient care %K EHR %K electronic health record %K dataset %K questionnaire %K patient data %K NHS %K National Health Service %D 2024 %7 24.12.2024 %9 %J JMIR Med Inform %G English %X Background: Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Health care data are inherently complex, and their acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of health care data could lead to improvements in patient care and service delivery. However, this depends on the identification of relevant datasets. Objective: We aimed to demonstrate the application of business process modeling notation (BPMN) to represent clinical pathways at a UK neurosciences center and map the clinical activity to corresponding data flows into electronic health records and other nonstandard data repositories. Methods: We used BPMN to map and visualize a patient journey and the subsequent movement and storage of patient data. After identifying several datasets that were being held outside of the standard applications, we collected information about these datasets using a questionnaire. Results: We identified 13 standard applications where neurology clinical activity was captured as part of the patient’s electronic health record including applications and databases for managing referrals, outpatient activity, laboratory data, imaging data, and clinic letters. We also identified 22 distinct datasets not within standard applications that were created and managed within the neurosciences department, either by individuals or teams. These were being used to deliver direct patient care and included datasets for tracking patient blood results, recording home visits, and tracking triage status. Conclusions: Mapping patient data flows and repositories allowed us to identify areas wherein the current electronic health record does not fulfill the needs of day-to-day patient care. Data that are being stored outside of standard applications represent a potential duplication in the effort and risks being overlooked. Future work should identify unmet data needs to inform correct data capture and centralization within appropriate data architectures. %R 10.2196/60017 %U https://medinform.jmir.org/2024/1/e60017 %U https://doi.org/10.2196/60017 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e59113 %T Implementing Findable, Accessible, Interoperable, Reusable (FAIR) Principles in Child and Adolescent Mental Health Research: Mixed Methods Approach %A de Groot,Rowdy %A van der Graaff,Frank %A van der Doelen,Daniël %A Luijten,Michiel %A De Meyer,Ronald %A Alrouh,Hekmat %A van Oers,Hedy %A Tieskens,Jacintha %A Zijlmans,Josjan %A Bartels,Meike %A Popma,Arne %A de Keizer,Nicolette %A Cornet,Ronald %A Polderman,Tinca J C %K FAIR data %K research data management %K data interoperability %K data standardization %K OMOP CDM %K implementation %K health data %K data quality %K FAIR principles %D 2024 %7 19.12.2024 %9 %J JMIR Ment Health %G English %X Background: The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers. Objectives: To further the field of FAIR data, this study aimed to systematically identify barriers regarding implementing the FAIR principles in the area of child and adolescent mental health research, define the most challenging barriers, and provide recommendations for these barriers. Methods: Three sources were used as input to identify barriers: (1) evaluation of the implementation process of the Observational Medical Outcomes Partnership Common Data Model by 3 data managers; (2) interviews with experts on mental health research, reusable health data, and data quality; and (3) a rapid literature review. All barriers were categorized according to type as described previously, the affected FAIR principle, a category to add detail about the origin of the barrier, and whether a barrier was mental health specific. The barriers were assessed and ranked on impact with the data managers using the Delphi method. Results: Thirteen barriers were identified by the data managers, 7 were identified by the experts, and 30 barriers were extracted from the literature. This resulted in 45 unique barriers. The characteristics that were most assigned to the barriers were, respectively, external type (n=32/45; eg, organizational policy preventing the use of required software), tooling category (n=19/45; ie, software and databases), all FAIR principles (n=15/45), and not mental health specific (n=43/45). Consensus on ranking the scores of the barriers was reached after 2 rounds of the Delphi method. The most important recommendations to overcome the barriers are adding a FAIR data steward to the research team, accessible step-by-step guides, and ensuring sustainable funding for the implementation and long-term use of FAIR data. Conclusions: By systematically listing these barriers and providing recommendations, we intend to enhance the awareness of researchers and grant providers that making data FAIR demands specific expertise, available tooling, and proper investments. %R 10.2196/59113 %U https://mental.jmir.org/2024/1/e59113 %U https://doi.org/10.2196/59113 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e63710 %T Enhancing Standardized and Structured Recording by Elderly Care Physicians for Reusing Electronic Health Record Data: Interview Study %A Albers,Charlotte A W %A Wieland-Jorna,Yvonne %A de Bruijne,Martine C %A Smalbrugge,Martin %A Joling,Karlijn J %A de Boer,Marike E %+ Department of Medicine for Older People, Location Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, Amsterdam, 1081 HV, Netherlands, 31 615867548, c.a.w.albers@amsterdamumc.nl %K electronic health records %K health information interoperability %K health information exchange %K reference standards %K long-term care %K nursing homes %K medical records %K attitude of health personnel %K qualitative research %K digital health %D 2024 %7 13.12.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Elderly care physicians (ECPs) in nursing homes document patients’ health, medical conditions, and the care provided in electronic health records (EHRs). However, much of these health data currently lack structure and standardization, limiting their potential for health information exchange across care providers and reuse for quality improvement, policy development, and scientific research. Enhancing this potential requires insight into the attitudes and behaviors of ECPs toward standardized and structured recording in EHRs. Objective: This study aims to answer why and how ECPs record their findings in EHRs and what factors influence them to record in a standardized and structured manner. The findings will be used to formulate recommendations aimed at enhancing standardized and structured data recording for the reuse of EHR data. Methods: Semistructured interviews were conducted with 13 ECPs working in Dutch nursing homes. We recruited participants through purposive sampling, aiming for diversity in age, gender, health care organization, and use of EHR systems. Interviews continued until we reached data saturation. Analysis was performed using inductive thematic analysis. Results: ECPs primarily use EHRs to document daily patient care, ensure continuity of care, and fulfill their obligation to record specific information for accountability purposes. The EHR serves as a record to justify their actions in the event of a complaint. In addition, some respondents also mentioned recording information for secondary purposes, such as research and quality improvement. Several factors were found to influence standardized and structured recording. At a personal level, it is crucial to experience the added value of standardized and structured recording. At the organizational level, clear internal guidelines and a focus on their implementation can have a substantial impact. At the level of the EHR system, user-friendliness, interoperability, and guidance were most frequently mentioned as being important. At a national level, the alignment of internal guidelines with overarching standards plays a pivotal role in encouraging standardized and structured recording. Conclusions: The results of our study are similar to the findings of previous research in hospital care and general practice. Therefore, long-term care can learn from solutions regarding standardized and structured recording in other health care sectors. The main motives for ECPs to record in EHRs are the daily patient care and ensuring continuity of care. Standardized and structured recording can be improved by aligning the recording method in EHRs with the primary care process. In addition, there are incentives for motivating ECPs to record in a standardized and structured way, mainly at the personal, organizational, EHR system, and national levels. %M 39671580 %R 10.2196/63710 %U https://medinform.jmir.org/2024/1/e63710 %U https://doi.org/10.2196/63710 %U http://www.ncbi.nlm.nih.gov/pubmed/39671580 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e63834 %T Advancing Progressive Web Applications to Leverage Medical Imaging for Visualization of Digital Imaging and Communications in Medicine and Multiplanar Reconstruction: Software Development and Validation Study %A AboArab,Mohammed A. %A Potsika,Vassiliki T. %A Theodorou,Alexis %A Vagena,Sylvia %A Gravanis,Miltiadis %A Sigala,Fragiska %A Fotiadis,Dimitrios I. %+ Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR-45110, Ioannina, 45110, Greece, 30 2651005580, fotiadis@uoi.gr %K medical image visualization %K peripheral artery computed tomography imaging %K multiplanar reconstruction %K progressive web applications %D 2024 %7 9.12.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: In medical imaging, 3D visualization is vital for displaying volumetric organs, enhancing diagnosis and analysis. Multiplanar reconstruction (MPR) improves visual and diagnostic capabilities by transforming 2D images from computed tomography (CT) and magnetic resonance imaging into 3D representations. Web-based Digital Imaging and Communications in Medicine (DICOM) viewers integrated into picture archiving and communication systems facilitate access to pictures and interaction with remote data. However, the adoption of progressive web applications (PWAs) for web-based DICOM and MPR visualization remains limited. This paper addresses this gap by leveraging PWAs for their offline access and enhanced performance. Objective: This study aims to evaluate the integration of DICOM and MPR visualization into the web using PWAs, addressing challenges related to cross-platform compatibility, integration capabilities, and high-resolution image reconstruction for medical image visualization. Methods: Our paper introduces a PWA that uses a modular design for enhancing DICOM and MPR visualization in web-based medical imaging. By integrating React.js and Cornerstone.js, the application offers seamless DICOM image processing, ensures cross-browser compatibility, and delivers a responsive user experience across multiple devices. It uses advanced interpolation techniques to make volume reconstructions more accurate. This makes MPR analysis and visualization better in a web environment, thus promising a substantial advance in medical imaging analysis. Results: In our approach, the performance of DICOM- and MPR-based PWAs for medical image visualization and reconstruction was evaluated through comprehensive experiments. The application excelled in terms of loading time and volume reconstruction, particularly in Google Chrome, whereas Firefox showed superior performance in viewing slices. This study uses a dataset comprising 22 CT scans of peripheral artery patients to demonstrate the application’s robust performance, with Google Chrome outperforming other browsers in both the local area network and wide area network settings. In addition, the application’s accuracy in MPR reconstructions was validated with an error margin of <0.05 mm and outperformed the state-of-the-art methods by 84% to 98% in loading and volume rendering time. Conclusions: This paper highlights advancements in DICOM and MPR visualization using PWAs, addressing the gaps in web-based medical imaging. By exploiting PWA features such as offline access and improved performance, we have significantly advanced medical imaging technology, focusing on cross-platform compatibility, integration efficiency, and speed. Our application outperforms existing platforms for handling complex MPR analyses and accurate analysis of medical imaging as validated through peripheral artery CT imaging. %M 39652391 %R 10.2196/63834 %U https://medinform.jmir.org/2024/1/e63834 %U https://doi.org/10.2196/63834 %U http://www.ncbi.nlm.nih.gov/pubmed/39652391 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60258 %T Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions %A Chen,You %A Lehmann,Christoph U %A Malin,Bradley %+ Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Nashville, TN, 37203, United States, 1 6153431939, you.chen@vanderbilt.edu %K patient care pathway %K care journey %K care coordination %K digital information ecosystem %K digital technologies %K artificial intelligence %K information interoperability %K information silos %K workload %K information retrieval %K care transitions %K patient-reported outcome measures %K clinical workflow %K usability %K user experience workflow %K health care information systems %K networks of health care professionals %K patient information flow %D 2024 %7 2.12.2024 %9 Viewpoint %J J Med Internet Res %G English %X The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients’ desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination. %M 39622048 %R 10.2196/60258 %U https://www.jmir.org/2024/1/e60258 %U https://doi.org/10.2196/60258 %U http://www.ncbi.nlm.nih.gov/pubmed/39622048 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e64969 %T Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study %A Rosenthal,Sarah %A Adler-Milstein,Julia %A Patel,Vaishali %K public health informatics %K health information exchange %K health information technology %K data exchange %K health information %K national survey %K surveillance %K United States %K PHA %K HIO %K public health agency %K health information exchange organization %D 2024 %7 15.11.2024 %9 %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic revealed major gaps in public health agencies’ (PHAs’) data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries. Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs’ capabilities to support public health data exchange. Methods: Drawing on multiple sources, we identified all potential local, regional, and state HIOs that were operational in the United States as of March 1, 2022. We defined operational as HIOs that facilitated exchange between at least 2 independent entities. We fielded a survey among our census list of 135 HIOs in January-July 2023. The survey confirmed HIO status as well as captured organizational demographics and current and potential support for PHAs. We report descriptive statistics on HIO demographics and connectivity with PHAs. We also include results on services and data available to support PHAs, funding sources to support public health reporting, and barriers to public health reporting. Of the 135 potential HIOs that received the survey, 90 met our definition of an HIO, and 77 completed the survey, yielding an 86% response rate. Results: We found that 66 (86%) of HIOs in 45 states were electronically connected to at least 1 PHA, yielding 187 HIO-PHA connections across all HIOs. Among HIOs connected to PHAs, the most common type of public health reporting supported by HIOs was immunization registry (n=39, 64%), electronic laboratory result (n=37, 63%), and syndromic surveillance (n=34, 61%). In total, 58% (n=38) of HIOs connected to PHAs provided data to address COVID-19 information gaps, and an additional 30% (n=20) could do so. The most common types of data provided to PHAs were hospitalization information (n=54, 93%), other demographic data (n=53, 91%), health information (eg, chronic health conditions; n=51, 88%), and hospital laboratory results (n=51, 88%). A total of 64% (n=42) of HIOs provided at least 1 type of data analytic service to PHAs to support COVID-19 pandemic response efforts. Top HIO reported barriers to support PHA activities included limited PHA funding (n=21, 32%) and PHAs’ competing priorities (n=15, 23%). Conclusions: Our results show that many HIOs are already connected to PHAs and that they are assuming an emerging role to facilitate public health reporting. HIOs are well-positioned to provide value-added support for public health data exchange and address PHAs’ information gaps, as ongoing federal efforts to modernize public health data infrastructure and interoperability continue. %R 10.2196/64969 %U https://publichealth.jmir.org/2024/1/e64969 %U https://doi.org/10.2196/64969 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e52675 %T Unintended Consequences of Data Sharing Under the Meaningful Use Program %A Willcockson,Irmgard Ursula %A Valdes,Ignacio Herman %K electronic health records %K EHR %K medical record %K interoperability %K health information interoperability %K clinical burden %K Medicare %K Medicaid %K reimbursement %K data science %K data governance %K data breach %K cybersecurity %K privacy %D 2024 %7 14.11.2024 %9 %J JMIR Med Inform %G English %X Interoperability has been designed to improve the quality and efficiency of health care. It allows the Centers for Medicare and Medicaid Services to collect data on quality measures as a part of the Meaningful Use program. Covered providers who fail to provide data have lower rates of reimbursement. Unintended consequences also arise at each step of the data collection process: (1) providers are not reimbursed for the extra time required to generate data; (2) patients do not have control over when and how their data are provided to or used by the government; and (3) large datasets increase the chances of an accidental data breach or intentional hacker attack. After detailing the issues, we describe several solutions, including an appropriate data use review board, which is designed to oversee certain aspects of the process and ensure accountability and transparency. %R 10.2196/52675 %U https://medinform.jmir.org/2024/1/e52675 %U https://doi.org/10.2196/52675 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55148 %T Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study %A Brehmer,Alexander %A Sauer,Christopher Martin %A Salazar Rodríguez,Jayson %A Herrmann,Kelsey %A Kim,Moon %A Keyl,Julius %A Bahnsen,Fin Hendrik %A Frank,Benedikt %A Köhrmann,Martin %A Rassaf,Tienush %A Mahabadi,Amir-Abbas %A Hadaschik,Boris %A Darr,Christopher %A Herrmann,Ken %A Tan,Susanne %A Buer,Jan %A Brenner,Thorsten %A Reinhardt,Hans Christian %A Nensa,Felix %A Gertz,Michael %A Egger,Jan %A Kleesiek,Jens %+ Institute for Artificial Intelligence in Medicine, University Hospital Essen, Hufelandstr. 55, Essen, 45147, Germany, 49 0201723 ext 77801, jens.kleesiek@uk-essen.de %K clinical informatics %K FHIR %K real-world evidence %K medical intelligence %K interoperability %K data exchange %K clinical management %K clinical decision-making %K electronic health records %K quality of care %K quality improvement %D 2024 %7 31.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. Objective: This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making. Methods: A Python package for the use of multimodal FHIR data (FHIRPACK [FHIR Python Analysis Conversion Kit]) was developed and pioneered in 5 real-world clinical use cases, that is, myocardial infarction, stroke, diabetes, sepsis, and prostate cancer. Patients were identified based on the ICD-10 (International Classification of Diseases, Tenth Revision) codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards. Results: For 2022, a total of 1,302,988 patient encounters were analyzed. (1) Myocardial infarction: in 72.7% (261/359) of cases, medication regimens fulfilled guideline recommendations. (2) Stroke: out of 1277 patients, 165 received thrombolysis and 108 thrombectomy. (3) Diabetes: in 443,866 serum glucose and 16,180 glycated hemoglobin A1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (13,887/35,494). Among those with dysglycemia, diagnosis was coded in 44.2% (6138/13,887) of the patients. (4) Sepsis: In 1803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (773/2672, 28.9%) and piperacillin and tazobactam was the primarily prescribed antibiotic (593/1593, 37.2%). (5) PC: out of 54, three patients who received radical prostatectomy were identified as cases with prostate-specific antigen persistence or biochemical recurrence. Conclusions: Leveraging FHIR data through large-scale analytics can enhance health care quality and improve patient outcomes across 5 clinical specialties. We identified (1) patients with sepsis requiring less broad antibiotic therapy, (2) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, (3) patients who had a stroke with longer than recommended times to intervention, (4) patients with hyperglycemia who could benefit from specialist referral, and (5) patients with PC with early increases in cancer markers. %M 39240144 %R 10.2196/55148 %U https://www.jmir.org/2024/1/e55148 %U https://doi.org/10.2196/55148 %U http://www.ncbi.nlm.nih.gov/pubmed/39240144 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57569 %T A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation %A Kruse,Jesse %A Wiedekopf,Joshua %A Kock-Schoppenhauer,Ann-Kristin %A Essenwanger,Andrea %A Ingenerf,Josef %A Ulrich,Hannes %K FHIR %K StructureMaps %K FHIR mapping language %K laboratory data %K mapping %K standardization %K data science %K healthcare system %K HIS %K information system %K electronic healthcare record %K health care system %K electronic health record %K health information system %D 2024 %7 18.10.2024 %9 %J JMIR Med Inform %G English %X Background: Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). Objective: In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. Methods: FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. Results: The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians’ specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. Conclusions: The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology. %R 10.2196/57569 %U https://medinform.jmir.org/2024/1/e57569 %U https://doi.org/10.2196/57569 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58541 %T Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study %A Rosenau,Lorenz %A Gruendner,Julian %A Kiel,Alexander %A Köhler,Thomas %A Schaffer,Bastian %A Majeed,Raphael W %K feasibility %K FHIR %K CQL %K eligibility criteria %K clinical research %K intermediate query format %K healthcare interoperability %K cohort definition %K query %K queries %K interoperability %K interoperable %K informatics %K portal %K portals %K implementation %K develop %K development %K ontology %K ontologies %K JSON %D 2024 %7 14.10.2024 %9 %J JMIR Med Inform %G English %X Background: To advance research with clinical data, it is essential to make access to the available data as fast and easy as possible for researchers, which is especially challenging for data from different source systems within and across institutions. Over the years, many research repositories and data standards have been created. One of these is the Fast Healthcare Interoperability Resources (FHIR) standard, used by the German Medical Informatics Initiative (MII) to harmonize and standardize data across university hospitals in Germany. One of the first steps to make these data available is to allow researchers to create feasibility queries to determine the data availability for a specific research question. Given the heterogeneity of different query languages to access different data across and even within standards such as FHIR (eg, CQL and FHIR Search), creating an intermediate query syntax for feasibility queries reduces the complexity of query translation and improves interoperability across different research repositories and query languages. Objective: This study describes the creation and implementation of an intermediate query syntax for feasibility queries and how it integrates into the federated German health research portal (Forschungsdatenportal Gesundheit) and the MII. Methods: We analyzed the requirements for feasibility queries and the feasibility tools that are currently available in research repositories. Based on this analysis, we developed an intermediate query syntax that can be easily translated into different research repository–specific query languages. Results: The resulting Clinical Cohort Definition Language (CCDL) for feasibility queries combines inclusion criteria in a conjunctive normal form and exclusion criteria in a disjunctive normal form, allowing for additional filters like time or numerical restrictions. The inclusion and exclusion results are combined via an expression to specify feasibility queries. We defined a JSON schema for the CCDL, generated an ontology, and demonstrated the use and translatability of the CCDL across multiple studies and real-world use cases. Conclusions: We developed and evaluated a structured query syntax for feasibility queries and demonstrated its use in a real-world example as part of a research platform across 39 German university hospitals. %R 10.2196/58541 %U https://medinform.jmir.org/2024/1/e58541 %U https://doi.org/10.2196/58541 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e56735 %T Enhancing the Functionalities of Personal Health Record Systems: Empirical Study Based on the HL7 Personal Health Record System Functional Model Release 1 %A Cao,Teng %A Chen,Zhi %A Nakayama,Masaharu %K fast healthcare interoperability resources %K logical observation identifiers names and codes %K personal health record system functional model %K personal health records %D 2024 %7 9.10.2024 %9 %J JMIR Med Inform %G English %X Background: The increasing demand for personal health record (PHR) systems is driven by individuals’ desire to actively manage their health care. However, the limited functionality of current PHR systems has affected users’ willingness to adopt them, leading to lower-than-expected usage rates. The HL7 (Health Level Seven) PHR System Functional Model (PHR-S FM) was proposed to address this issue, outlining all possible functionalities in PHR systems. Although the PHR-S FM provides a comprehensive theoretical framework, its practical effectiveness and applicability have not been fully explored. Objective: This study aimed to design and develop a tethered PHR prototype in accordance with the guidelines of the PHR-S FM. It sought to explore the feasibility of applying the PHR-S FM in PHR systems by comparing the prototype with the results of previous research. Methods: The PHR-S FM profile was defined to meet broad clinical data management requirements based on previous research. We designed and developed a PHR prototype as a web application using the Fast Healthcare Interoperability Resources R4 (FHIR) and Logical Observation Identifiers Names and Codes (LOINC) coding system for interoperability and data consistency. We validated the prototype using the Synthea dataset, which provided realistic synthetic medical records. In addition, we compared the results produced by the prototype with those of previous studies to evaluate the feasibility and implementation of the PHR-S FM framework. Results: The PHR prototype was developed based on the PHR-S FM profile. We verified its functionality by demonstrating its ability to synchronize data with the FHIR server, effectively managing and displaying various health data types. Validation using the Synthea dataset confirmed the prototype’s accuracy, achieving 100% coverage across 1157 data items. A comparison with the findings of previous studies indicated the feasibility of implementing the PHR-S FM and highlighted areas for future research and improvements. Conclusions: The results of this study offer valuable insights into the potential for practical application and broad adoption of the PHR-S FM in real-world health care settings. %R 10.2196/56735 %U https://medinform.jmir.org/2024/1/e56735 %U https://doi.org/10.2196/56735 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e60293 %T Toward Better Semantic Interoperability of Data Element Repositories in Medicine: Analysis Study %A Hu,Zhengyong %A Wang,Anran %A Duan,Yifan %A Zhou,Jiayin %A Hu,Wanfei %A Wu,Sizhu %+ Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 3 Yabao Road, Chaoyang District, Beijing, 100020, China, 86 52328760, Wu.sizhu@imicams.ac.cn %K data element repository %K FAIR %K ISO/IEC 11179 %K metadata %K semantic interoperability %D 2024 %7 30.9.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Data element repositories facilitate high-quality medical data sharing by standardizing data and enhancing semantic interoperability. However, the application of repositories is confined to specific projects and institutions. Objective: This study aims to explore potential issues and promote broader application of data element repositories within the medical field by evaluating and analyzing typical repositories. Methods: Following the inclusion of 5 data element repositories through a literature review, a novel analysis framework consisting of 7 dimensions and 36 secondary indicators was constructed and used for evaluation and analysis. Results: The study’s results delineate the unique characteristics of different repositories and uncover specific issues in their construction. These issues include the absence of data reuse protocols and insufficient information regarding the application scenarios and efficacy of data elements. The repositories fully comply with only 45% (9/20) of the subprinciples for Findable and Reusable in the FAIR principle, while achieving a 90% (19/20 subprinciples) compliance rate for Accessible and 67% (10/15 subprinciples) for Interoperable. Conclusions: The recommendations proposed in this study address the issues to improve the construction and application of repositories, offering valuable insights to data managers, computer experts, and other pertinent stakeholders. %M 39348178 %R 10.2196/60293 %U https://medinform.jmir.org/2024/1/e60293 %U https://doi.org/10.2196/60293 %U http://www.ncbi.nlm.nih.gov/pubmed/39348178 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58445 %T State-of-the-Art Fast Healthcare Interoperability Resources (FHIR)–Based Data Model and Structure Implementations: Systematic Scoping Review %A Tabari,Parinaz %A Costagliola,Gennaro %A De Rosa,Mattia %A Boeker,Martin %+ Department of Informatics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Italy, 39 089 963319, ptabari@unisa.it %K data model %K Fast Healthcare Interoperability Resources %K FHIR %K interoperability %K modeling %K PRISMA %D 2024 %7 24.9.2024 %9 Review %J JMIR Med Inform %G English %X 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. %M 39316433 %R 10.2196/58445 %U https://medinform.jmir.org/2024/1/e58445 %U https://doi.org/10.2196/58445 %U http://www.ncbi.nlm.nih.gov/pubmed/39316433 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57853 %T PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources %A Ohlsen,Tessa %A Ingenerf,Josef %A Essenwanger,Andrea %A Drenkhahn,Cora %+ IT Center for Clinical Research, University of Luebeck, Ratzeburger Allee 160, Luebeck, 23562, Germany, 49 45131015623, t.ohlsen@uni-luebeck.de %K SNOMED CT %K HL7 FHIR %K TermInfo %K postcoordination %K semantic interoperability %K terminology %K OWL %K semantic similarity %D 2024 %7 17.9.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs. Objective: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model. Methods: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the “Delta.” Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources. Results: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts. Conclusions: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users. %M 39287966 %R 10.2196/57853 %U https://medinform.jmir.org/2024/1/e57853 %U https://doi.org/10.2196/57853 %U http://www.ncbi.nlm.nih.gov/pubmed/39287966 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e59651 %T Implementation of the World Health Organization Minimum Dataset for Emergency Medical Teams to Create Disaster Profiles for the Indonesian SATUSEHAT Platform Using Fast Healthcare Interoperability Resources: Development and Validation Study %A Faisal,Hiro Putra %A Nakayama,Masaharu %K WHO EMT MDS %K FHIR %K SATUSEHAT %K disaster %K implementation %K development %K validation %K emergency medical team %K disaster management %K Indonesia %K Fast Healthcare Interoperability Resources %K resources %K interoperability %K electronic medical records %K EMR %K reporting %K disaster profile %K health data %K health data collection %K World Health Organization %K EMT %K WHO %K MDS %K minimum dataset %D 2024 %7 28.8.2024 %9 %J JMIR Med Inform %G English %X Background: The National Disaster Management Agency (Badan Nasional Penanggulangan Bencana) handles disaster management in Indonesia as a health cluster by collecting, storing, and reporting information on the state of survivors and their health from various sources during disasters. Data were collected on paper and transferred to Microsoft Excel spreadsheets. These activities are challenging because there are no standards for data collection. The World Health Organization (WHO) introduced a standard for health data collection during disasters for emergency medical teams (EMTs) in the form of a minimum dataset (MDS). Meanwhile, the Ministry of Health of Indonesia launched the SATUSEHAT platform to integrate all electronic medical records in Indonesia based on Fast Healthcare Interoperability Resources (FHIR). Objective: This study aims to implement the WHO EMT MDS to create a disaster profile for the SATUSEHAT platform using FHIR. Methods: We extracted variables from 2 EMT MDS medical records—the WHO and Association of Southeast Asian Nations (ASEAN) versions—and the daily reporting form. We then performed a mapping process to match these variables with the FHIR resources and analyzed the gaps between the variables and base resources. Next, we conducted profiling to see if there were any changes in the selected resources and created extensions to fill the gap using the Forge application. Subsequently, the profile was implemented using an open-source FHIR server. Results: The total numbers of variables extracted from the WHO EMT MDS, ASEAN EMT MDS, and daily reporting forms were 30, 32, and 46, with the percentage of variables matching FHIR resources being 100% (30/30), 97% (31/32), and 85% (39/46), respectively. From the 40 resources available in the FHIR ID core, we used 10, 14, and 9 for the WHO EMT MDS, ASEAN EMT MDS, and daily reporting form, respectively. Based on the gap analysis, we found 4 variables in the daily reporting form that were not covered by the resources. Thus, we created extensions to address this gap. Conclusions: We successfully created a disaster profile that can be used as a disaster case for the SATUSEHAT platform. This profile may standardize health data collection during disasters. %R 10.2196/59651 %U https://medinform.jmir.org/2024/1/e59651 %U https://doi.org/10.2196/59651 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57153 %T Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium %A Kamdje Wabo,Gaetan %A Moorthy,Preetha %A Siegel,Fabian %A Seuchter,Susanne A %A Ganslandt,Thomas %+ Center for Preventive Medicine and Digital Health Baden-Wuerttemberg, Department of Biomedical Informatics, Medical Faculty of Mannheim, University of Heidelberg, Building 3, Level 4, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany, 49 621 383 8088, gaetankamdje.wabo@medma.uni-heidelberg.de %K data quality %K fitness-for-purpose %K secondary use %K thematic analysis %K EHR data %K electronic health record %K data integration center %K Medical Informatics Initiative %K MIRACUM consortium %K Medical Informatics for Research and Care in University Medicine %K data science %K integration %K data use %K visualization %K visualizations %K record %K records %K EHR %K EHRs %K survey %K surveys %K medical informatics %D 2024 %7 19.8.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings. Objective: The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs. Methods: A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method. Results: All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master’s degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams. Conclusions: The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings. %M 39158950 %R 10.2196/57153 %U https://medinform.jmir.org/2024/1/e57153 %U https://doi.org/10.2196/57153 %U http://www.ncbi.nlm.nih.gov/pubmed/39158950 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57005 %T Uncovering Harmonization Potential in Health Care Data Through Iterative Refinement of Fast Healthcare Interoperability Resources Profiles Based on Retrospective Discrepancy Analysis: Case Study %A Rosenau,Lorenz %A Behrend,Paul %A Wiedekopf,Joshua %A Gruendner,Julian %A Ingenerf,Josef %+ IT Center for Clinical Research, University of Lübeck, Gebäude 64, 2.OG, Raum 05, Ratzeburger Allee 160, Lübeck, 23562, Germany, 49 451 3101 5636, lorenz.rosenau@uni-luebeck.de %K Health Level 7 Fast Healthcare Interoperability Resources %K HL7 FHIR %K FHIR profiles %K interoperability %K data harmonization %K discrepancy analysis %K data quality %K cross-institutional data exchange %K Medical Informatics Initiative %K federated data access challenges %D 2024 %7 23.7.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Cross-institutional interoperability between health care providers remains a recurring challenge worldwide. The German Medical Informatics Initiative, a collaboration of 37 university hospitals in Germany, aims to enable interoperability between partner sites by defining Fast Healthcare Interoperability Resources (FHIR) profiles for the cross-institutional exchange of health care data, the Core Data Set (CDS). The current CDS and its extension modules define elements representing patients’ health care records. All university hospitals in Germany have made significant progress in providing routine data in a standardized format based on the CDS. In addition, the central research platform for health, the German Portal for Medical Research Data feasibility tool, allows medical researchers to query the available CDS data items across many participating hospitals. Objective: In this study, we aimed to evaluate a novel approach of combining the current top-down generated FHIR profiles with the bottom-up generated knowledge gained by the analysis of respective instance data. This allowed us to derive options for iteratively refining FHIR profiles using the information obtained from a discrepancy analysis. Methods: We developed an FHIR validation pipeline and opted to derive more restrictive profiles from the original CDS profiles. This decision was driven by the need to align more closely with the specific assumptions and requirements of the central feasibility platform’s search ontology. While the original CDS profiles offer a generic framework adaptable for a broad spectrum of medical informatics use cases, they lack the specificity to model the nuanced criteria essential for medical researchers. A key example of this is the necessity to represent specific laboratory codings and values interdependencies accurately. The validation results allow us to identify discrepancies between the instance data at the clinical sites and the profiles specified by the feasibility platform and addressed in the future. Results: A total of 20 university hospitals participated in this study. Historical factors, lack of harmonization, a wide range of source systems, and case sensitivity of coding are some of the causes for the discrepancies identified. While in our case study, Conditions, Procedures, and Medications have a high degree of uniformity in the coding of instance data due to legislative requirements for billing in Germany, we found that laboratory values pose a significant data harmonization challenge due to their interdependency between coding and value. Conclusions: While the CDS achieves interoperability, different challenges for federated data access arise, requiring more specificity in the profiles to make assumptions on the instance data. We further argue that further harmonization of the instance data can significantly lower required retrospective harmonization efforts. We recognize that discrepancies cannot be resolved solely at the clinical site; therefore, our findings have a wide range of implications and will require action on multiple levels and by various stakeholders. %M 39042420 %R 10.2196/57005 %U https://medinform.jmir.org/2024/1/e57005 %U https://doi.org/10.2196/57005 %U http://www.ncbi.nlm.nih.gov/pubmed/39042420 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e54590 %T Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline %A Lamer,Antoine %A Saint-Dizier,Chloé %A Paris,Nicolas %A Chazard,Emmanuel %K data reuse %K data lake %K data warehouse %K feature extraction %K datamart %K feature store %D 2024 %7 17.7.2024 %9 %J JMIR Med Inform %G English %X The growing adoption and use of health information technology has generated a wealth of clinical data in electronic format, offering opportunities for data reuse beyond direct patient care. However, as data are distributed across multiple software, it becomes challenging to cross-reference information between sources due to differences in formats, vocabularies, and technologies and the absence of common identifiers among software. To address these challenges, hospitals have adopted data warehouses to consolidate and standardize these data for research. Additionally, as a complement or alternative, data lakes store both source data and metadata in a detailed and unprocessed format, empowering exploration, manipulation, and adaptation of the data to meet specific analytical needs. Subsequently, datamarts are used to further refine data into usable information tailored to specific research questions. However, for efficient analysis, a feature store is essential to pivot and denormalize the data, simplifying queries. In conclusion, while data warehouses are crucial, data lakes, datamarts, and feature stores play essential and complementary roles in facilitating data reuse for research and analysis in health care. %R 10.2196/54590 %U https://medinform.jmir.org/2024/1/e54590 %U https://doi.org/10.2196/54590 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e59187 %T Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension %A Ji,Hyerim %A Kim,Seok %A Sunwoo,Leonard %A Jang,Sowon %A Lee,Ho-Young %A Yoo,Sooyoung %+ Office of eHealth Research and Business, Seoul National University Bundang Hospital, 172, Dolma-ro, Bundang-gu, Seongnam-si, 13605, Republic of Korea, 82 317878980, yoosoo0@snubh.org %K DICOM %K OMOP %K CDM %K lung cancer %K medical imaging %K data integration %K data quality %K Common Data Model %K Digital Imaging and Communications in Medicine %K Observational Medical Outcomes Partnership %D 2024 %7 12.7.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Methods: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables—IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH—to standardize various imaging-related data and link to clinical data. Results: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. Conclusions: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions. %M 38996330 %R 10.2196/59187 %U https://medinform.jmir.org/2024/1/e59187 %U https://doi.org/10.2196/59187 %U http://www.ncbi.nlm.nih.gov/pubmed/38996330 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e49613 %T Dermoscopy Differential Diagnosis Explorer (D3X) Ontology to Aggregate and Link Dermoscopic Patterns to Differential Diagnoses: Development and Usability Study %A Lin,Rebecca Z %A Amith,Muhammad Tuan %A Wang,Cynthia X %A Strickley,John %A Tao,Cui %+ Department of Artificial Intelligence and Informatics, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, United States, 1 9049530255, Tao.Cui@mayo.edu %K medical informatics %K biomedical ontology %K ontology %K ontologies %K vocabulary %K OWL %K web ontology language %K skin %K semiotic %K web app %K web application %K visual %K visualization %K dermoscopic %K diagnosis %K diagnoses %K diagnostic %K information storage %K information retrieval %K skin lesion %K skin diseases %K dermoscopy differential diagnosis explorer %K dermatology %K dermoscopy %K differential diagnosis %K information storage and retrieval %D 2024 %7 21.6.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand. Objective: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses. Methods: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers. Results: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory–driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain. Conclusions: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice. %M 38904996 %R 10.2196/49613 %U https://medinform.jmir.org/2024/1/e49613 %U https://doi.org/10.2196/49613 %U http://www.ncbi.nlm.nih.gov/pubmed/38904996 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e53535 %T Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability %A Palojoki,Sari %A Lehtonen,Lasse %A Vuokko,Riikka %K electronic health record %K health records %K EHR %K EHRs %K semantic %K health care data %K semantic interoperability %K interoperability %K standardize %K standardized %K standardization %K cross-border data exchange %K systematic review %K synthesis %K syntheses %K review methods %K review methodology %K search %K searches %K searching %K systematic %K data exchange %K information sharing %K ontology %K ontologies %K terminology %K terminologies %K standard %K standards %K classification %K PRISMA %K data sharing %K Preferred Reporting Items for Systematic Reviews and Meta-Analyses %D 2024 %7 25.4.2024 %9 %J JMIR Med Inform %G English %X 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. %R 10.2196/53535 %U https://medinform.jmir.org/2024/1/e53535 %U https://doi.org/10.2196/53535 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e51560 %T Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews %A Declerck,Jens %A Kalra,Dipak %A Vander Stichele,Robert %A Coorevits,Pascal %+ Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Campus UZ-Ghent, Entrance 42, 6th Floor, Corneel Heymanslaan 10, Ghent, 9000, Belgium, 32 93323628, jens.declerck@ugent.be %K data quality %K data quality dimensions %K data quality assessment %K secondary use %K data quality framework %K fit for purpose %D 2024 %7 6.3.2024 %9 Review %J JMIR Med Inform %G English %X 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. %M 38446534 %R 10.2196/51560 %U https://medinform.jmir.org/2024/1/e51560 %U https://doi.org/10.2196/51560 %U http://www.ncbi.nlm.nih.gov/pubmed/38446534 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e52967 %T Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review %A Peng,Yuan %A Bathelt,Franziska %A Gebler,Richard %A Gött,Robert %A Heidenreich,Andreas %A Henke,Elisa %A Kadioglu,Dennis %A Lorenz,Stephan %A Vengadeswaran,Abishaa %A Sedlmayr,Martin %+ Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Fetscherstraße 74, Dresden, 01307, Germany, 49 3514583648, yuan.peng@tu-dresden.de %K ETL %K ELT %K Extract-Load-Transform %K Extract-Transform-Load %K interoperability %K metadata-driven %K medical domain %K data harmonization %D 2024 %7 14.2.2024 %9 Review %J JMIR Med Inform %G English %X Background: Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. Objective: In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. Methods: We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. Results: Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. Conclusions: Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data. %M 38354027 %R 10.2196/52967 %U https://medinform.jmir.org/2024/1/e52967 %U https://doi.org/10.2196/52967 %U http://www.ncbi.nlm.nih.gov/pubmed/38354027 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e45209 %T Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review %A Amar,Fouzia %A April,Alain %A Abran,Alain %+ École de technologie supérieure - ETS, 1100 Notre Dame Ouest, Montreal, QC, H3C 1K3, Canada, 1 514 396 8800, famar2022@gmail.com %K electronic health record %K EHR %K Health Level Seven International Fast Healthcare Interoperability Resources %K HL7 FHIR %K interoperability, web ontology language %K OWL %K ontology %K semantic %K terminology %K resource description framework %K RDF %K machine learning %K ML %K natural language processing %K NLP %D 2024 %7 30.1.2024 %9 Review %J J Med Internet Res %G English %X Background: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard. Objective: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies. Methods: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard. Results: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language–based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability. Conclusions: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction. %M 38289660 %R 10.2196/45209 %U https://www.jmir.org/2024/1/e45209 %U https://doi.org/10.2196/45209 %U http://www.ncbi.nlm.nih.gov/pubmed/38289660 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e45413 %T Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study %A Guinez-Molinos,Sergio %A Espinoza,Sonia %A Andrade,Jose %A Medina,Alejandro %+ School of Medicine, Universidad de Talca, Avenida San Miguel #3748, Talca, 3460000, Chile, 56 996195268, sguinez@utalca.cl %K interoperability %K health information system %K Health Level Seven International %K HL7 %K Fast Healthcare Interoperability Resource %K FHIR %K certification %K training %K interoperable %K e-learning %K application programming interface %K API %D 2024 %7 29.1.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: Interoperability between health information systems is a fundamental requirement to guarantee the continuity of health care for the population. The Fast Healthcare Interoperability Resource (FHIR) is the standard that enables the design and development of interoperable systems with broad adoption worldwide. However, FHIR training curriculums need an easily administered web-based self-learning platform with modules to create scenarios and questions that the learner answers. This paper proposes a system for teaching FHIR that automatically evaluates the answers, providing the learner with continuous feedback and progress. Objective: We are designing and developing a learning management system for creating, applying, deploying, and automatically assessing FHIR web-based courses. Methods: The system requirements for teaching FHIR were collected through interviews with experts involved in academic and professional FHIR activities (universities and health institutions). The interviews were semistructured, recording and documenting each meeting. In addition, we used an ad hoc instrument to register and analyze all the needs to elicit the requirements. Finally, the information obtained was triangulated with the available evidence. This analysis was carried out with Atlas-ti software. For design purposes, the requirements were divided into functional and nonfunctional. The functional requirements were (1) a test and question manager, (2) an application programming interface (API) to orchestrate components, (3) a test evaluator that automatically evaluates the responses, and (4) a client application for students. Security and usability are essential nonfunctional requirements to design functional and secure interfaces. The software development methodology was based on the traditional spiral model. The end users of the proposed system are (1) the system administrator for all technical aspects of the server, (2) the teacher designing the courses, and (3) the students interested in learning FHIR. Results: The main result described in this work is Huemul, a learning management system for training on FHIR, which includes the following components: (1) Huemul Admin: a web application to create users, tests, and questions and define scores; (2) Huemul API: module for communication between different software components (FHIR server, client, and engine); (3) Huemul Engine: component for answers evaluation to identify differences and validate the content; and (4) Huemul Client: the web application for users to show the test and questions. Huemul was successfully implemented with 416 students associated with the 10 active courses on the platform. In addition, the teachers have created 60 tests and 695 questions. Overall, the 416 students who completed their courses rated Huemul highly. Conclusions: Huemul is the first platform that allows the creation of courses, tests, and questions that enable the automatic evaluation and feedback of FHIR operations. Huemul has been implemented in multiple FHIR teaching scenarios for health care professionals. Professionals trained on FHIR with Huemul are leading successful national and international initiatives. %M 38285492 %R 10.2196/45413 %U https://mededu.jmir.org/2024/1/e45413 %U https://doi.org/10.2196/45413 %U http://www.ncbi.nlm.nih.gov/pubmed/38285492 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e49301 %T The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices %A Schwab,Julian D %A Werle,Silke D %A Hühne,Rolf %A Spohn,Hannah %A Kaisers,Udo X %A Kestler,Hans A %+ Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany, 49 731 500 24500, hans.kestler@uni-ulm.de %K semantic terminology %K semantic %K terminology %K terminologies %K data linkage %K interoperability %K data exchange %K SNOMED CT %K LOINC %K eHealth %K patient-reported outcome questionnaires %K requirement for standards %K standard %K standards %K PRO %K PROM %K patient reported %D 2023 %7 22.12.2023 %9 Viewpoint %J JMIR Med Inform %G English %X Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient’s feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them. %M 38133917 %R 10.2196/49301 %U https://medinform.jmir.org/2023/1/e49301 %U https://doi.org/10.2196/49301 %U http://www.ncbi.nlm.nih.gov/pubmed/38133917 %0 Journal Article %@ 2291-9694 %I %V 11 %N %P e47959 %T Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study %A Henke,Elisa %A Zoch,Michéle %A Kallfelz,Michael %A Ruhnke,Thomas %A Leutner,Liz Annika %A Spoden,Melissa %A Günster,Christian %A Sedlmayr,Martin %A Bathelt,Franziska %K OMOP CDM %K interoperability %K vocabularies %K claims data %K OHDSI %K Observational Medical Outcomes Partnership %K common data model %K Observational Health Data Sciences and Informatics %D 2023 %7 7.11.2023 %9 %J JMIR Med Inform %G English %X Background: National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project “Hybrid Quality Indicators Using Machine Learning Methods” (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP CDM. Objective: This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM–based research. Methods: To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health Data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP CDM. Results: Our vocabulary preparation approach was able to map 3 missing German vocabularies to standard concepts and prepare 8 vocabularies as new 2-billion concepts. The completeness evaluation showed that the prepared vocabularies cover 44.3% (3288/7417) of the source codes contained in German claims data. The correctness evaluation revealed that the specified validity periods in the OMOP CDM are compliant for the majority (705,531/706,032, 99.9%) of source codes and associated dates in German claims data. The calculation of the vocabulary coverage showed a noticeable decrease of missing vocabularies from 55% (11/20) to 10% (2/20) due to our preparation approach. Conclusions: By preparing 10 vocabularies, we showed that our approach is applicable to any type of vocabulary used in a source data set. The prepared vocabularies are currently limited to German vocabularies, which can only be used in national OMOP CDM research projects, because the mapping of new 2-billion concepts to standard concepts is missing. To participate in international OHDSI network studies with German claims data, future work is required to map the prepared 2-billion concepts to standard concepts. %R 10.2196/47959 %U https://medinform.jmir.org/2023/1/e47959 %U https://doi.org/10.2196/47959 %0 Journal Article %@ 2291-9694 %I %V 11 %N %P e45315 %T Computer-Supported Collaborative Design of Standardized Clinical Cases: Algorithm Development and Validation %A Guinez-Molinos,Sergio %A Buendía-García,Félix %A Sierra-Rodríguez,José-Luis %A Gayoso-Cabada,Joaquín %A González-Díaz,Jaime %K collaborative learning %K interoperability %K case-based learning %K standards %K clinical cases %K collaborative clinical cases %D 2023 %7 19.9.2023 %9 %J JMIR Med Inform %G English %X Background: The creation of computer-supported collaborative clinical cases is an area of educational research that has been widely studied. However, the reuse of cases and their sharing with other platforms is a problem, as it encapsulates knowledge in isolated platforms without interoperability. This paper proposed a workflow ecosystem for the collaborative design and distribution of clinical cases through web-based computing platforms that (1) allow medical students to create clinical cases collaboratively in a dedicated environment; (2) make it possible to export these clinical cases in terms of the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) interoperability standard; (3) provide support to transform imported cases into learning object repositories; and (4) use e-learning standards (eg, Instructional Management Systems Content Packaging [IMS-CP] or Sharable Content Object Reference Model [SCORM]) to incorporate this content into widely-used learning management systems (LMSs), letting medical students democratize a valuable knowledge that would otherwise be confined within proprietary platforms. Objective: This study aimed to demonstrate the feasibility of developing a workflow ecosystem based on IT platforms to enable the collaborative creation, export, and deployment of clinical cases. Methods: The ecosystem infrastructure for computer-supported collaborative design of standardized clinical cases consists of three platforms: (1) Mosaico, a platform used in the design of clinical cases; (2) Clavy, a tool for the flexible management of learning object repositories, which is used to orchestrate the transformation and processing of these clinical cases; and (3) Moodle, an LMS that is geared toward publishing the processed clinical cases and delivering their course deployment stages in IMS-CP or SCORM format. The generation of cases in Mosaico is exported in the HL7 FHIR interoperability standard to Clavy, which is then responsible for creating and deploying a learning object in Moodle. Results: The main result was an interoperable ecosystem that demonstrates the feasibility of automating the stages of collaborative clinical case creation, export through HL7 FHIR standards, and deployment in an LMS. This ecosystem enables the generation of IMS-CPs associated with the original Mosaico clinical cases that can be deployed in conventional third-party LMSs, thus allowing the democratization and sharing of clinical cases to different platforms in standard and interoperable formats. Conclusions: In this paper, we proposed, implemented, and demonstrated the feasibility of developing a standards-based workflow that interoperates multiple platforms with heterogeneous technologies to create, transform, and deploy clinical cases on the web. This achieves the objective of transforming the created cases into a platform for web-based deployment in an LMS. %R 10.2196/45315 %U https://medinform.jmir.org/2023/1/e45315 %U https://doi.org/10.2196/45315 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45013 %T Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review %A Inau,Esther Thea %A Sack,Jean %A Waltemath,Dagmar %A Zeleke,Atinkut Alamirrew %+ Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str 48, Greifswald, D-17475, Germany, 49 3834867548, inaue@uni-greifswald.de %K data stewardship %K findable, accessible, interoperable, and reusable data principles %K FAIR data principles %K health research %K Preferred Reporting Items for Systematic Reviews and Meta-Analyses %K PRISMA %K qualitative analysis %K scoping review %K information retrieval %K health information exchange %D 2023 %7 28.8.2023 %9 Review %J J Med Internet Res %G English %X 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 %M 37639292 %R 10.2196/45013 %U https://www.jmir.org/2023/1/e45013 %U https://doi.org/10.2196/45013 %U http://www.ncbi.nlm.nih.gov/pubmed/37639292 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e44842 %T Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review %A Nan,Jingwen %A Xu,Li-Qun %+ Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Unit 2, Block C1, AI Innovation Center, Hele Second Street, Gaoxin District, Chengdu, 610213, China, 86 (28) 60103585, xuliqun@chinamobile.com %K Health level 7 Fast Healthcare Interoperability Resources %K HL7 FHIR %K interoperability %K literature review %K practice guideline %K mobile phone %D 2023 %7 21.8.2023 %9 Review %J JMIR Med Inform %G English %X 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. %M 37603388 %R 10.2196/44842 %U https://medinform.jmir.org/2023/1/e44842 %U https://doi.org/10.2196/44842 %U http://www.ncbi.nlm.nih.gov/pubmed/37603388 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e48363 %T Challenges and Solutions in Implementing eSource Technology for Real-World Studies in China: Qualitative Study Among Different Stakeholders %A Wang,Bin %A Lai,Junkai %A Liao,Xiwen %A Jin,Feifei %A Yao,Chen %+ Peking University Clinical Research Institute, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing, 100191, China, 86 01066551053, yaochen@hsc.pku.edu.cn %K electronic medical record %K electronic source %K eSource %K challenge %K real-world data %K interoperability %D 2023 %7 10.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: eSources consist of data that were initially documented in an electronic structure. Typically, an eSource encompasses the direct acquisition, compilation, and retention of electronic information (such as electronic health records [EHRs] or wearable devices), which serves to streamline clinical research. eSources have the potential to enhance the accuracy of data, promote patient safety, and minimize expenses associated with clinical trials. An opinion study published in September 2020 by TransCelerate outlined a road map for the future application of eSource technology and identified 5 key areas of challenges. The background of this study concerns the use of eSource technology in clinical research. Objective: The aim of this study was to present challenges and possible solutions for the implementation of eSource technology in real-world studies by summarizing team experiences and lessons learned from an eSource record (ESR) project. Methods: After initially developing a simple prototype of the ESR software that can be demonstrated systematically, the researchers conducted in-depth interviews and interacted with different stakeholders to obtain guidance and suggestions. The researchers selected 5 different roles for interviewees: regulatory authorities, pharmaceutical company representatives, hospital information department employees, medical system providers, and clinicians. Results: After screening all consultants, the researchers concluded that there were 25 representative consultants. The hospital information department needs to implement many demands from various stakeholders, which makes the existing EHR system unable to meet all the demands of eSources. The emergence of an ESR is intended to divert the burden of the hospital information department from the enormous functional requirements of the outdated EHR system. The entire research process emphasizes multidisciplinary and multibackground expert opinions and considers the complexity of the knowledge backgrounds of personnel involved in the chain of clinical source data collection, processing, quality control, and application in real-world scenarios. To increase the readability of the results, the researchers classified the main results in accordance with the paragraph titles in “Use of Electronic Health Record Data in Clinical Investigations,” a guide released by the US Food and Drug Administration. Conclusions: This study introduces the requirement dependencies of different stakeholders and the challenges and recommendations for designing ESR software when implementing eSource technology in China. Experiences based on ESR projects will provide new insights into the disciplines that advance the eSource research field. Future studies should engage patients directly to understand their experiences, concerns, and preferences regarding the implementation of eSource technology. Moreover, involving additional stakeholders, including community health care providers and social workers, will provide valuable insights into the challenges and potential solutions across various health care settings. %M 37561551 %R 10.2196/48363 %U https://formative.jmir.org/2023/1/e48363 %U https://doi.org/10.2196/48363 %U http://www.ncbi.nlm.nih.gov/pubmed/37561551 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e46477 %T Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions %A Rossander,Anna %A Karlsson,Daniel %+ Department of Applied IT, University of Gothenburg, Department of Applied Information Technology, Division of Informatics, Box 100, Gothenburg, 405 30, Sweden, 46 735989141, anna.rossander@gu.se %K informatics %K health care %K information model %K terminology %K terminologies %K interoperability %K competency question %K interoperable %K competency %K EHR %K electronic health record %K guideline %K standard %K recommendation %K information system %D 2023 %7 31.7.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: There is a flora of health care information models but no consensus on which to use. This leads to poor information sharing and duplicate modelling work. The amount and type of differences between models has, to our knowledge, not been evaluated. Objective: This work aims to explore how information structured with various information models differ in practice. Our hypothesis is that differences between information models are overestimated. This work will also assess the usability of competency questions as a method for evaluation of information models within health care. Methods: In this study, 4 information standards, 2 standards for secondary use, and 2 electronic health record systems were included as material. Competency questions were developed for a random selection of recommendations from a clinical guideline. The information needed to answer the competency questions was modelled according to each included information model, and the results were analyzed. Differences in structure and terminology were quantified for each combination of standards. Results: In this study, 36 competency questions were developed and answered. In general, similarities between the included information models were larger than the differences. The demarcation between information model and terminology was overall similar; on average, 45% of the included structures were identical between models. Choices of terminology differed within and between models; on average, 11% was usable in interaction with each other. The information models included in this study were able to represent most information required for answering the competency questions. Conclusions: Different but same same; in practice, different information models structure much information in a similar fashion. To increase interoperability within and between systems, it is more important to move toward structuring information with any information model rather than finding or developing a perfect information model. Competency questions are a feasible way of evaluating how information models perform in practice. %M 37523221 %R 10.2196/46477 %U https://medinform.jmir.org/2023/1/e46477 %U https://doi.org/10.2196/46477 %U http://www.ncbi.nlm.nih.gov/pubmed/37523221 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e41225 %T Validation of an Interoperability Framework for Linking mHealth Apps to Electronic Record Systems in Botswana: Expert Survey Study %A Ndlovu,Kagiso %A Mars,Maurice %A Scott,Richard E %+ Department of Telehealth, School of Nursing & Public Health, College of Health Sciences, University of KwaZulu-Natal, Mazisi Kunene Road, Glenwood, Durban, 4041, South Africa, 27 0312601075, ndlovuk@ub.ac.bw %K mobile health %K mHealth %K electronic record systems %K eRecord systems %K mHealth-eRecord Interoperability Framework %K mHeRIF %K interoperability architecture %K validation %K Botswana %K mobile phone %D 2023 %7 2.5.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Electronic record (eRecord) systems and mobile health (mHealth) apps have documented potential to improve health service delivery, resulting in increased global uptake. However, their interoperability remains a global challenge hindering diagnosis, monitoring of health conditions, and data access irrespective of geographic location. Given the widespread use of mobile devices by patients and health care providers, linking mHealth apps and eRecord systems could result in a comprehensive and seamless data exchange within a health care community. The Botswana National eHealth Strategy recognizes interoperability as an issue and mHealth as a potential solution for some health care needs but is silent on how to make mHealth apps interoperable with existing eRecord systems. A literature review and analysis of existing mHealth interoperability frameworks found none suitable for Botswana. As such, it was critical to conceptualize, design, and develop an mHealth-eRecord Interoperability Framework (mHeRIF) to enhance the interoperability pillar of the Botswana National eHealth Strategy and leverage the full benefits of linking mHealth apps with other health information systems. Objective: This study aimed to validate the developed mHeRIF and determine whether it requires further refinement before consideration towards enhancing the National eHealth Strategy. Methods: Published framework validation approaches guided the development of a survey administered to 12 purposively selected local and international eHealth experts. In total, 25% (3/12) of the experts were drawn from Botswana, 25% (3/12) were drawn from low- and middle-income countries in sub-Saharan Africa, 25% (3/12) were drawn from low- and middle-income countries outside Africa, and 25% (3/12) were drawn from high-income countries. Quantitative responses were collated in a Microsoft Excel (Microsoft Corp) spreadsheet for descriptive analysis, and the NVivo software (version 11; QSR International) was used to aid the thematic analysis of the qualitative open-ended questions. Results: The analysis of responses showed overall support for the content and format of the proposed mHeRIF. However, some experts’ suggestions led to 4 modest revisions of the mHeRIF. Conclusions: Overall, the experts’ reviews showed that the mHeRIF could contribute to the National eHealth Strategy by guiding the linking of mHealth apps to existing eRecord systems in Botswana. Similarly, the experts validated an architectural model derived from the mHeRIF in support of the first mobile telemedicine initiative considered for national rollout in Botswana. The mHeRIF helps identify key components to consider before and after linking mHealth apps to eRecord systems and is being considered for use as the foundation of such interoperability in Botswana. %M 37129939 %R 10.2196/41225 %U https://formative.jmir.org/2023/1/e41225 %U https://doi.org/10.2196/41225 %U http://www.ncbi.nlm.nih.gov/pubmed/37129939 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e46127 %T A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study %A Sung,Sumi %A Park,Hyeoun-Ae %A Jung,Hyesil %A Kang,Hannah %+ College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 2 740 8827, hapark@snu.ac.kr %K semantic interoperability %K Systematized Nomenclature of Medicine–Clinical Terms %K mapping guideline %K local terms %K mapping %K guideline %K SNOMED %K nomenclature %K interoperable %K interoperability %K terminology %K medical term %K health term %K terminologies %K ontologies %D 2023 %7 18.4.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: South Korea joined SNOMED International as the 39th member country. To ensure semantic interoperability, South Korea introduced SNOMED CT (Systemized Nomenclature of Medicine–Clinical Terms) in 2020. However, there is no methodology to map local Korean terms to SNOMED CT. Instead, this is performed sporadically and independently at each local medical institution. The quality of the mapping, therefore, cannot be guaranteed. Objective: This study aimed to develop and introduce a guideline to map local Korean terms to the SNOMED CT used to document clinical findings and procedures in electronic health records at health care institutions in South Korea. Methods: The guidelines were developed from December 2020 to December 2022. An extensive literature review was conducted. The overall structures and contents of the guidelines with diverse use cases were developed by referencing the existing SNOMED CT mapping guidelines, previous studies related to SNOMED CT mapping, and the experiences of the committee members. The developed guidelines were validated by a guideline review panel. Results: The SNOMED CT mapping guidelines developed in this study recommended the following 9 steps: define the purpose and scope of the map, extract terms, preprocess source terms, preprocess source terms using clinical context, select a search term, use search strategies to find SNOMED CT concepts using a browser, classify mapping correlations, validate the map, and build the final map format. Conclusions: The guidelines developed in this study can support the standardized mapping of local Korean terms into SNOMED CT. Mapping specialists can use this guideline to improve the mapping quality performed at individual local medical institutions. %M 37071456 %R 10.2196/46127 %U https://medinform.jmir.org/2023/1/e46127 %U https://doi.org/10.2196/46127 %U http://www.ncbi.nlm.nih.gov/pubmed/37071456 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43127 %T Asymmetric Interoperability as a Strategy Among Provider Group Health Information Exchange: Directional Analysis %A Shah,Rohin Rathin %A Bailey,Joseph Peter %+ The Robert H. Smith School of Business, University of Maryland, 7699 Mowatt Ln, College Park, MD, 20742-1815, United States, 1 301 405 2174, jpbailey@umd.edu %K health information exchange %K quality payment program %K electronic health records %K electronic referrals %K medical informatics %K technology adoption %K health information interoperability %D 2023 %7 6.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: High levels of seamless, bidirectional health information exchange continue to be broadly limited among provider groups despite the vast array of benefits that interoperability entails for patient care and the many persistent efforts across the health care ecosystem directed at advancing interoperability. As provider groups seek to act in their strategic best interests, they are often interoperable and exchange information in certain directions but not others, leading to the formation of asymmetries. Objective: We aimed to examine the correlation at the provider group level between the distinct directions of interoperability with regard to sending health information and receiving health information, to describe how this correlation varies across provider group types and provider group sizes, and to analyze the symmetries and asymmetries that arise in the exchange of patient health information across the health care ecosystem as a result. Methods: We used data from the Centers for Medicare & Medicaid Services (CMS), which included interoperability performance information for 2033 provider groups within the Quality Payment Program Merit-based Incentive Payment System and maintained distinct performance measures for sending health information and receiving health information. In addition to compiling descriptive statistics, we also conducted a cluster analysis to identify differences among provider groups—particularly with respect to symmetric versus asymmetric interoperability. Results: We found that the examined directions of interoperability—sending health information and receiving health information—have relatively low bivariate correlation (0.4147) with a significant number of observations exhibiting asymmetric interoperability (42.5%). Primary care providers are generally more likely to exchange information asymmetrically than specialty providers, being more inclined to receive health information than to send health information. Finally, we found that larger provider groups are significantly less likely to be bidirectionally interoperable than smaller groups, although both are asymmetrically interoperable at similar rates. Conclusions: The adoption of interoperability by provider groups is more nuanced than traditionally considered and should not be seen as a binary determination (ie, to be interoperable or not). Asymmetric interoperability—and its pervasive presence among provider groups—reiterates how the manner in which provider groups exchange patient health information is a strategic choice and may pose similar implications and potential harms as the practice of information blocking has in the past. Differences in the operational paradigms among provider groups of varying types and sizes may explain their varying extents of health information exchange for sending and receiving health information. There continues to remain substantial room for improvement on the path to achieving a fully interoperable health care ecosystem, and future policy efforts directed at advancing interoperability should consider the practice of being asymmetrically interoperable among provider groups. %M 37023418 %R 10.2196/43127 %U https://www.jmir.org/2023/1/e43127 %U https://doi.org/10.2196/43127 %U http://www.ncbi.nlm.nih.gov/pubmed/37023418 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42615 %T Digital Health Data Quality Issues: Systematic Review %A Syed,Rehan %A Eden,Rebekah %A Makasi,Tendai %A Chukwudi,Ignatius %A Mamudu,Azumah %A Kamalpour,Mostafa %A Kapugama Geeganage,Dakshi %A Sadeghianasl,Sareh %A Leemans,Sander J J %A Goel,Kanika %A Andrews,Robert %A Wynn,Moe Thandar %A ter Hofstede,Arthur %A Myers,Trina %+ School of Information Systems, Faculty of Science, Queensland University of Technology, 2 George Street, Brisbane, 4000, Australia, 61 7 3138 9360, r.syed@qut.edu.au %K data quality %K digital health %K electronic health record %K eHealth %K systematic reviews %D 2023 %7 31.3.2023 %9 Review %J J Med Internet Res %G English %X Background: The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective: The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. Results: The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. Conclusions: The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first. %M 37000497 %R 10.2196/42615 %U https://www.jmir.org/2023/1/e42615 %U https://doi.org/10.2196/42615 %U http://www.ncbi.nlm.nih.gov/pubmed/37000497 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e43847 %T A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study %A Williams,Elena %A Kienast,Manuel %A Medawar,Evelyn %A Reinelt,Janis %A Merola,Alberto %A Klopfenstein,Sophie Anne Ines %A Flint,Anne Rike %A Heeren,Patrick %A Poncette,Akira-Sebastian %A Balzer,Felix %A Beimes,Julian %A von Bünau,Paul %A Chromik,Jonas %A Arnrich,Bert %A Scherf,Nico %A Niehaus,Sebastian %+ AICURA Medical GmbH, Bessemerstr 22, Berlin, 12103, Germany, 49 173 9449677, evelyn.medawar@aicura-medical.com %K data interoperability %K fast healthcare interoperability resources %K FHIR %K data standardization pipeline %K medical information mart for intensive care %K MIMIC IV %K artificial intelligence %K AI application %K AI %K deployment %K data %K usability %K care unit %K diagnosis %K cooperation %K patient care %K care %K medical research %D 2023 %7 21.3.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. Objective: In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. Methods: We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. Results: We present the FHIR-DHP workflow in respect of the transformation of “raw” hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. Conclusions: Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research. %M 36943344 %R 10.2196/43847 %U https://medinform.jmir.org/2023/1/e43847 %U https://doi.org/10.2196/43847 %U http://www.ncbi.nlm.nih.gov/pubmed/36943344 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42822 %T A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study %A Sinaci,A Anil %A Gencturk,Mert %A Teoman,Huseyin Alper %A Laleci Erturkmen,Gokce Banu %A Alvarez-Romero,Celia %A Martinez-Garcia,Alicia %A Poblador-Plou,Beatriz %A Carmona-Pírez,Jonás %A Löbe,Matthias %A Parra-Calderon,Carlos Luis %+ Software Research & Development and Consultancy Corporation (SRDC), Orta Dogu Teknik Universitesi Teknokent K1-16, Cankaya, 06800, Turkey, 90 3122101763, anil@srdc.com.tr %K Health Level 7 Fast Healthcare Interoperability Resources %K HL7 FHIR %K Findable, Accessible, Interoperable, and Reusable principles %K FAIR principles %K health data sharing %K health data transformation %K secondary use %D 2023 %7 8.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. Objective: Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. Methods: Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. Results: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. Conclusions: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks. %M 36884270 %R 10.2196/42822 %U https://www.jmir.org/2023/1/e42822 %U https://doi.org/10.2196/42822 %U http://www.ncbi.nlm.nih.gov/pubmed/36884270 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e43848 %T Perspectives on Challenges and Opportunities for Interoperability: Findings From Key Informant Interviews With Stakeholders in Ohio %A Walker,Daniel M %A Tarver,Willi L %A Jonnalagadda,Pallavi %A Ranbom,Lorin %A Ford,Eric W %A Rahurkar,Saurabh %+ Department of Family and Community Medicine, College of Medicine, The Ohio State University, Suite 5000, 700 Ackerman Rd, Columbus, OH, 43202, United States, 1 203 988 1800, daniel.walker@osumc.edu %K interoperability %K health information exchange %K health information technology %K electronic health record %K usability %D 2023 %7 24.2.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: Interoperability—the exchange and integration of data across the health care system—remains a challenge despite ongoing policy efforts aimed at promoting interoperability. Objective: This study aimed to identify current challenges and opportunities to advancing interoperability across stakeholders. Methods: Primary data were collected through qualitative, semistructured interviews with stakeholders (n=24) in Ohio from July to October 2021. Interviewees were sampled using a stratified purposive sample of key informants from 4 representative groups as follows: acute care and children’s hospital leaders, primary care providers, behavioral health providers, and regional health information exchange networks. Interviews focused on key informant perspectives on electronic health record implementation, the alignment of public policy with organizational strategy, interoperability implementation challenges, and opportunities for health information technology. The interviews were transcribed verbatim followed by rigorous qualitative analysis using directed content analysis. Results: The findings illuminate themes related to challenges and opportunities for interoperability that align with technological (ie, implementation challenges, mismatches in interoperability capabilities across stakeholders, and opportunities to leverage new technology and integrate social determinants of health data), organizational (ie, facilitators of interoperability and strategic alignment of participation in value-based payment programs with interoperability), and environmental (ie, policy) domains. Conclusions: Interoperability, although technically feasible for most providers, remains challenging for technological, organizational, and environmental reasons. Our findings suggest that the incorporation of end user considerations into health information technology development, implementation, policy, and standard deployment may support interoperability advancement. %M 36826979 %R 10.2196/43848 %U https://medinform.jmir.org/2023/1/e43848 %U https://doi.org/10.2196/43848 %U http://www.ncbi.nlm.nih.gov/pubmed/36826979 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e43750 %T Systematized Nomenclature of Medicine–Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review %A Vuokko,Riikka %A Vakkuri,Anne %A Palojoki,Sari %+ Unit for Digitalization and Management, Ministry of Social Affairs and Health, PO Box 33, Helsinki, FI-00023 Government, Finland, 358 50 453 6377, riikka.vuokko@gov.fi %K clinical %K electronic health record %K EHR %K review method %K literature review %K SNOMED CT %K Systematized Nomenclature for Medicine %K use case %K terminology %K terminologies %K SNOMED %D 2023 %7 6.2.2023 %9 Review %J JMIR Med Inform %G English %X 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. %M 36745498 %R 10.2196/43750 %U https://medinform.jmir.org/2023/1/e43750 %U https://doi.org/10.2196/43750 %U http://www.ncbi.nlm.nih.gov/pubmed/36745498 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e37545 %T Digital Maturity Consulting and Strategizing to Optimize Services: Overview %A Phiri,Peter %A Cavalini,Heitor %A Shetty,Suchith %A Delanerolle,Gayathri %+ Research & Innovation Department, Southern Health NHS Foundation Trust, Clinical Trials Facility, Tom Rudd Unit, Moorgreen Hospital, Botley Road, West End, Southampton, SO30 3JB, United Kingdom, 44 2380475112, P.Phiri@soton.ac.uk %K digital maturity model %K health care system %K electronic medical records %K health record %K information %K UK %K medical service %K care provider %K integration %K interoperability %K digital health %K digital record %K workflow %D 2023 %7 17.1.2023 %9 Viewpoint %J J Med Internet Res %G English %X The National Health Service (NHS), the health care system of the United Kingdom, is one of the largest health care entities in the world and has been successfully serving the UK population for decades. The NHS is also the fourth-largest employer globally. True to its reputation, some of the most modern and technically advanced medical services are available in the United Kingdom. However, between the acute, primary, secondary, and tertiary care providers of the NHS, there needs to be seamless integration and interoperability to provide timely holistic care to patients at a national level. Various efforts have been taken and programs launched since 2002 to achieve digital transformation in the NHS but with partial success rates. As it is important to understand a problem before trying to solve it, in this paper, we focus on tools used to assess the digital maturity of NHS trusts and organizations. Additionally, we aim to present the impact of ongoing transformation attempts on secondary services, particularly mental health. This paper considered the literature on digital maturity and performed a rapid review of currently available tools to measure digital maturity. We have performed a multivocal literature review that included white papers and web-based documents in addition to peer-reviewed literature. Further, the paper also provides a perspective of the ground reality from a mental health service provider’s point of view. Assessment tools adopted from the global market, later modified and tailor-made to suit local preferences, are currently being used. However, there is a need for a robust framework that assesses status, allows target setting, and tracks progress across diverse providers. %M 36649060 %R 10.2196/37545 %U https://www.jmir.org/2023/1/e37545 %U https://doi.org/10.2196/37545 %U http://www.ncbi.nlm.nih.gov/pubmed/36649060 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 11 %P e40338 %T Shared Interoperable Clinical Decision Support Service for Drug-Allergy Interaction Checks: Implementation Study %A Jung,Sungwon %A Bae,Sungchul %A Seong,Donghyeong %A Oh,Ock Hee %A Kim,Yoomi %A Yi,Byoung-Kee %+ Department of Artificial Intelligent Convergence, Kangwon National University, 1 Ganwondaehakgil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea, 82 33 250 7672, byoungkeeyi@gmail.com %K clinical decision support %K drug-allergy interaction %K Health Level 7 %K Fast Healthcare Interoperability Resources %K interoperability %K CDS Hooks %D 2022 %7 10.11.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Clinical decision support (CDS) can improve health care with respect to the quality of care, patient safety, efficiency, and effectiveness. Establishing a CDS system in a health care setting remains a challenge. A few hospitals have used self-developed in-house CDS systems or commercial CDS solutions. Since these in-house CDS systems tend to be tightly coupled with a specific electronic health record system, the functionality and knowledge base are not easily shareable. A shared interoperable CDS system facilitates the sharing of the knowledge base and extension of CDS services. Objective: The study focuses on developing and deploying the national CDS service for the drug-allergy interaction (DAI) check for health care providers in Korea that need to introduce the service but lack the budget and expertise. Methods: To provide the shared interoperable CDS service, we designed and implemented the system based on the CDS Hooks specification and Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The study describes the CDS development process. The system development went through requirement analysis, design, implementation, and deployment. In particular, the concept architecture was designed based on the CDS Hooks structure. The MedicationRequest and AllergyIntolerance resources were profiled to exchange data using the FHIR standard. The discovery and DAI check application programming interfaces and rule engine were developed. Results: The CDS service was deployed on G-Cloud, a government cloud service. In March 2021, the CDS service was launched, and 67 health care providers participated in the CDS service. The health care providers participated in the service with 1,008,357 DAI checks for 114,694 patients, of which 33,054 (3.32%) cases resulted in a “warning.” Conclusions: Korea’s Ministry of Health and Welfare has been trying to build an HL7 FHIR-based ecosystem in Korea. As one of these efforts, the CDS service initiative has been conducted. To promote the rapid adoption of the HL7 FHIR standard, it is necessary to accelerate practical service development and to appeal to policy makers regarding the benefits of FHIR standardization. With the development of various case-specific implementation guides using the Korea Core implementation guide, the FHIR standards will be distributed nationwide, and more shared interoperable health care services will be introduced in Korea. %M 36355401 %R 10.2196/40338 %U https://medinform.jmir.org/2022/11/e40338 %U https://doi.org/10.2196/40338 %U http://www.ncbi.nlm.nih.gov/pubmed/36355401 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 10 %P e41503 %T Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics’ Common Data Model %A Bae,Woo Kyung %A Cho,Jihoon %A Kim,Seok %A Kim,Borham %A Baek,Hyunyoung %A Song,Wongeun %A Yoo,Sooyoung %+ Healthcare Information and Communication Technology Research Center, Office of eHealth Research and Business, Seoul National University Bundang Hospital, Republic of Korea, 172, Dolma-ro, Bundang-gu, Seongnam-si, 13605, Republic of Korea, 82 010 9053 7094, yoosoo0@snubh.org %K cardiovascular diseases %K coronary artery computed tomography angiography %K observational study %K common data model %K population level estimation %K cardiology %K vascular disease %K medical informatics %K computed tomography %K angiography %K electronic health record %K risk score %K health data science %K data modeling %D 2022 %7 13.10.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure. Objective: The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) data and the population-level estimation method. Methods: Using electronic health record–based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses. Results: The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02). Conclusions: Although we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years. %M 36227638 %R 10.2196/41503 %U https://medinform.jmir.org/2022/10/e41503 %U https://doi.org/10.2196/41503 %U http://www.ncbi.nlm.nih.gov/pubmed/36227638 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 7 %P e35724 %T Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review %A Vorisek,Carina Nina %A Lehne,Moritz %A Klopfenstein,Sophie Anne Ines %A Mayer,Paula Josephine %A Bartschke,Alexander %A Haese,Thomas %A Thun,Sylvia %+ Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str. 2, Berlin, 10178, Germany, 49 30 450 543049, carina-nina.vorisek@bih-charite.de %K Fast Healthcare Interoperability Resources %K FHIR %K interoperability %K health research %K health care %K health information technology %K research %K clinical research %K public health %K epidemiology %D 2022 %7 19.7.2022 %9 Review %J JMIR Med Inform %G English %X 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. %M 35852842 %R 10.2196/35724 %U https://medinform.jmir.org/2022/7/e35724 %U https://doi.org/10.2196/35724 %U http://www.ncbi.nlm.nih.gov/pubmed/35852842 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e29930 %T Digital Health Solutions and State of Interoperability: Landscape Analysis of Sierra Leone %A Chukwu,Emeka %A Garg,Lalit %A Foday,Edward %A Konomanyi,Abdul %A Wright,Royston %A Smart,Francis %+ Department of Computer Information Systems, Faculty of Information and Communication Technology, University of Malta, PG room A24, Level 0, Msida, MSD2080, Malta, 356 99330888, nnaemeka_ec@hotmail.com %K digital health %K mHealth %K mobile health %K eHealth %K Health information and communication technologies %K Sierra Leone %K big data %K HIE %K interoperability %D 2022 %7 10.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The government and partners have invested heavily in the health information system (HIS) for service delivery, surveillance, reporting, and monitoring. Sierra Leone’s government launched its first digital health strategy in 2018. In 2019, a broader national innovation and digital strategy was launched. The health pillar direction will use big data and artificial intelligence (AI) to improve health care in general and maternal and child health in particular. Understanding the number, distribution, and interoperability of digital health solutions is crucial for successful implementation strategies. Objective: This paper presents the state of digital health solutions in Sierra Leone and how these solutions currently interoperate. This study further presents opportunities for big data and AI applications. Methods: All the district health management teams, all digital health implementing organizations, and a stratified sample of 72 (out of 1284) health facilities were purposefully selected from all health districts and surveyed. Results: The National Health Management Information System’s (NHMIS’s) aggregate reporting solution populated by health facility forms HF1 to HF9 was, by far, the most used tool. A health facility–based weekly aggregate electronic integrated disease surveillance and response solution was also widely used. Half of the health facilities had more than 2 digital health solutions in use. The different digital health software solutions do not share data among one another, though aggregate reporting data were sent as necessary. None of the respondents use any of the health care registries for patient, provider, health facility, or terminology identification. Conclusions: Many digital health solutions are currently used at health facilities in Sierra Leone. The government can leverage current investment in HIS from surveillance and reporting for using big data and AI for care. The vision of using big data for health care is achievable if stakeholders prioritize individualized and longitudinal patient data exchange using agreed use cases from national strategies. This study has shown evidence of distribution, types, and scale of digital health solutions in health facilities and opportunities for leveraging big data to fill critical gaps necessary to achieve the national digital health vision. %M 35687406 %R 10.2196/29930 %U https://formative.jmir.org/2022/6/e29930 %U https://doi.org/10.2196/29930 %U http://www.ncbi.nlm.nih.gov/pubmed/35687406 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 5 %P e36709 %T The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study %A Gruendner,Julian %A Deppenwiese,Noemi %A Folz,Michael %A Köhler,Thomas %A Kroll,Björn %A Prokosch,Hans-Ulrich %A Rosenau,Lorenz %A Rühle,Mathias %A Scheidl,Marc-Anton %A Schüttler,Christina %A Sedlmayr,Brita %A Twrdik,Alexander %A Kiel,Alexander %A Majeed,Raphael W %+ Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Wetterkreuz 15, Erlangen, 91058, Germany, 49 9131 8567787, julian.gruendner@fau.de %K federated feasibility queries %K FHIR %K distributed analysis %K feasibility study %K HL7 FHIR %K FHIR Search %K CQL %K COVID-19 %K pandemic %K health data %K query %K patient data %K consensus data set %K medical informatics %K Fast Healthcare Interoperability Resources %D 2022 %7 25.5.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing. Objective: This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data. Methods: We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service. Results: We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative. Conclusions: We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible. %M 35486893 %R 10.2196/36709 %U https://medinform.jmir.org/2022/5/e36709 %U https://doi.org/10.2196/36709 %U http://www.ncbi.nlm.nih.gov/pubmed/35486893 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e31825 %T Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study %A Sharma,Videha %A Eleftheriou,Iliada %A van der Veer,Sabine N %A Brass,Andrew %A Augustine,Titus %A Ainsworth,John %+ Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Vaughan House, Portsmouth Street, Manchester, M13 9GV, United Kingdom, 44 7735360958, videha.sharma@postgrad.manchester.ac.uk %K digital transformation %K health information exchange %K interoperability %K medical informatics %K data journey modelling %K kidney transplantation %D 2022 %7 21.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. Objective: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. Methods: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. Results: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. Conclusions: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation. %M 35451983 %R 10.2196/31825 %U https://www.jmir.org/2022/4/e31825 %U https://doi.org/10.2196/31825 %U http://www.ncbi.nlm.nih.gov/pubmed/35451983 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e32776 %T Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study %A Yuan,Junyi %A Wang,Sufen %A Pan,Changqing %+ Hospital’s Office, Shanghai Chest Hospital, Shanghai Jiao Tong University, No 241 West Huaihai Road, Xuhui District, Shanghai, 200030, China, 86 21 62805080, panchangqing@shchest.org %K medical collaborative networks %K big data resources %K transaction efficiency %D 2022 %7 21.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. Objective: This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. Methods: This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. Results: Sharing of diagnosis and treatment data (β=.222; P=.03) and sharing of medical research data (β=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (β=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. Conclusions: This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs. %M 35318187 %R 10.2196/32776 %U https://www.jmir.org/2022/4/e32776 %U https://doi.org/10.2196/32776 %U http://www.ncbi.nlm.nih.gov/pubmed/35318187 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 4 %P e36481 %T Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application %A Wang,Miye %A Li,Sheyu %A Zheng,Tao %A Li,Nan %A Shi,Qingke %A Zhuo,Xuejun %A Ding,Renxin %A Huang,Yong %+ Engineering Research Center of Medical Information Technology, West China Hospital of Sichuan University, Ministry of Education, Information Center, West China Hospital, No. 37, Guoxue Road, Wuhou District, Chengdu, Sichuan Province, 610041, China, 86 18980601030, huangyong@wchscu.cn %K big data platform in health care %K multisource %K heterogeneous %K data integration %K data governance %K data application %K data security %K data quality control %K big data %K data science %K medical informatics %K health care %D 2022 %7 13.4.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: With the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care. Objective: We aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security. Methods: The project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master–slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established. Results: After 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model—from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month). Conclusions: The platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research. %M 35416792 %R 10.2196/36481 %U https://medinform.jmir.org/2022/4/e36481 %U https://doi.org/10.2196/36481 %U http://www.ncbi.nlm.nih.gov/pubmed/35416792 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e31021 %T Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users %A Almowil,Zahra %A Zhou,Shang-Ming %A Brophy,Sinead %A Croxall,Jodie %+ Data Science Building, Medical School, Swansea University, Sketty, Swansea, Wales, SA2 8PP, United Kingdom, 44 07552894384, 934467@swansea.ac.uk %K electronic health records %K record linkage %K reproducible research %K clinical codes %K concept libraries %D 2022 %7 15.3.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it difficult to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This study aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, in the development of a data portal for phenotypes (a concept library). Methods: This was a qualitative study using interviews and focus group discussion. One-to-one interviews were conducted with researchers, clinicians, machine learning experts, and senior research managers in health data science (N=6) to explore their specific needs in the development of a concept library. In addition, a focus group discussion with researchers (N=14) working with the Secured Anonymized Information Linkage databank, a national eHealth data linkage infrastructure, was held to perform a SWOT (strengths, weaknesses, opportunities, and threats) analysis for the phenotyping system and the proposed concept library. The interviews and focus group discussion were transcribed verbatim, and 2 thematic analyses were performed. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would stimulate them to share their work and reuse the work of others, and they pointed out several barriers that could inhibit them from sharing their work and reusing the work of others. The participants suggested some developments that they would like to see to improve reproducible research output using routine data. Conclusions: The study indicated that most interviewees valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform. Analysis of interviews and the focus group discussion revealed that different stakeholders have different requirements, facilitators, barriers, and concerns about a prototype concept library. %M 35289755 %R 10.2196/31021 %U https://humanfactors.jmir.org/2022/1/e31021 %U https://doi.org/10.2196/31021 %U http://www.ncbi.nlm.nih.gov/pubmed/35289755 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e32392 %T Digital Health Policy and Programs for Hospital Care in Vietnam: Scoping Review %A Tran,Duc Minh %A Thwaites,C Louise %A Van Nuil,Jennifer Ilo %A McKnight,Jacob %A Luu,An Phuoc %A Paton,Chris %A , %+ Oxford University Clinical Research Unit, 764 Vo Van Kiet, Ward 1, District 5, Ho Chi Minh City, 700000, Vietnam, 84 356574593, ductm@oucru.org %K digital health %K eHealth %K policy %K Vietnam %K hospital care %K data %K health %K electronic medical records %K standards %K compulsory %K patient ID %K administrative information %K health insurance ID %K mobile phone %D 2022 %7 9.2.2022 %9 Review %J J Med Internet Res %G English %X Background: There are a host of emergent technologies with the potential to improve hospital care in low- and middle-income countries such as Vietnam. Wearable monitors and artificial intelligence–based decision support systems could be integrated with hospital-based digital health systems such as electronic health records (EHRs) to provide higher level care at a relatively low cost. However, the appropriate and sustainable application of these innovations in low- and middle-income countries requires an understanding of the local government’s requirements and regulations such as technology specifications, cybersecurity, data-sharing protocols, and interoperability. Objective: This scoping review aims to explore the current state of digital health research and the policies that govern the adoption of digital health systems in Vietnamese hospitals. Methods: We conducted a scoping review using a modification of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed and Web of Science were searched for academic publications, and Thư Viện Pháp Luật, a proprietary database of Vietnamese government documents, and the Vietnam Electronic Health Administration website were searched for government documents. Google Scholar and Google Search were used for snowballing searches. The sources were assessed against predefined eligibility criteria through title, abstract, and full-text screening. Relevant information from the included sources was charted and summarized. The review process was primarily undertaken by one researcher and reviewed by another researcher during each step. Results: In total, 11 academic publications and 20 government documents were included in this review. Among the academic studies, 5 reported engineering solutions for information systems in hospitals, 2 assessed readiness for EHR implementation, 1 tested physicians’ performance before and after using clinical decision support software, 1 reported a national laboratory information management system, and 2 reviewed the health system’s capability to implement eHealth and artificial intelligence. Of the 20 government documents, 19 were promulgated from 2013 to 2020. These regulations and guidance cover a wide range of digital health domains, including hospital information management systems, general and interoperability standards, cybersecurity in health organizations, conditions for the provision of health information technology (HIT), electronic health insurance claims, laboratory information systems, HIT maturity, digital health strategies, electronic medical records, EHRs, and eHealth architectural frameworks. Conclusions: Research about hospital-based digital health systems in Vietnam is very limited, particularly implementation studies. Government regulations and guidance for HIT in health care organizations have been released with increasing frequency since 2013, targeting a variety of information systems such as electronic medical records, EHRs, and laboratory information systems. In general, these policies were focused on the basic specifications and standards that digital health systems need to meet. More research is needed in the future to guide the implementation of digital health care systems in the Vietnam hospital setting. %M 35138264 %R 10.2196/32392 %U https://www.jmir.org/2022/2/e32392 %U https://doi.org/10.2196/32392 %U http://www.ncbi.nlm.nih.gov/pubmed/35138264 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e20702 %T Factors Associated With Willingness to Share Health Information: Rapid Review %A Naeem,Iffat %A Quan,Hude %A Singh,Shaminder %A Chowdhury,Nashit %A Chowdhury,Mohammad %A Saini,Vineet %A TC,Turin %+ O'Brien Institute of Public Health, University of Calgary, GD01, TRW Building, 3280 Hospital Drive NW, Calgary, AB, T2N4N1, Canada, 1 5877787866, Iffat.naeem1@ucalgary.ca %K health information %K information sharing %K health data %K EMR %K PHR %K mobile phone %D 2022 %7 9.2.2022 %9 Review %J JMIR Hum Factors %G English %X Background: To expand research and strategies to prevent disease, comprehensive and real-time data are essential. Health data are increasingly available from platforms such as pharmaceuticals, genomics, health care imaging, medical procedures, wearable devices, and internet activity. Further, health data are integrated with an individual’s sociodemographic information, medical conditions, genetics, treatments, and health care. Ultimately, health information generation and flow are controlled by the patient or participant; however, there is a lack of understanding about the factors that influence willingness to share health information. A synthesis of the current literature on the multifactorial nature of health information sharing preferences is required to understand health information exchange. Objective: The objectives of this review are to identify peer-reviewed literature that reported factors associated with health information sharing and to organize factors into cohesive themes and present a narrative synthesis of factors related to willingness to share health information. Methods: This review uses a rapid review methodology to gather literature regarding willingness to share health information within the context of eHealth, which includes electronic health records, personal health records, mobile health information, general health information, or information on social determinants of health. MEDLINE and Google Scholar were searched using keywords such as electronic health records AND data sharing OR sharing preference OR willingness to share. The search was limited to any population that excluded health care workers or practitioners, and the participants aged ≥18 years within the US or Canadian context. The data abstraction process using thematic analysis where any factors associated with sharing health information were highlighted and coded inductively within each article. On the basis of shared meaning, the coded factors were collated into major themes. Results: A total of 26 research articles met our inclusion criteria and were included in the qualitative analysis. The inductive thematic coding process revealed multiple major themes related to sharing health information. Conclusions: This review emphasized the importance of data generators’ viewpoints and the complex systems of factors that shape their decision to share health information. The themes explored in this study emphasize the importance of trust at multiple levels to develop effective information exchange partnerships. In the case of improving precision health care, addressing the factors presented here that influence willingness to share information can improve sharing capacity for individuals and allow researchers to reorient their methods to address hesitation in sharing health information. %M 35138263 %R 10.2196/20702 %U https://humanfactors.jmir.org/2022/1/e20702 %U https://doi.org/10.2196/20702 %U http://www.ncbi.nlm.nih.gov/pubmed/35138263 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e30363 %T Use of Clinical Data Interchange Standards Consortium (CDISC) Standards for Real-world Data: Expert Perspectives From a Qualitative Delphi Survey %A Facile,Rhonda %A Muhlbradt,Erin Elizabeth %A Gong,Mengchun %A Li,Qingna %A Popat,Vaishali %A Pétavy,Frank %A Cornet,Ronald %A Ruan,Yaoping %A Koide,Daisuke %A Saito,Toshiki I %A Hume,Sam %A Rockhold,Frank %A Bao,Wenjun %A Dubman,Sue %A Jauregui Wurst,Barbara %+ Clinical Data Interchange Standards Consortium, 401 W 15th Street, Austin, TX, 78738, United States, 1 512 363 5825, rfacile@cdisc.org %K real-world data %K real-world evidence %K clinical trials %K Delphi survey %K clinical data standards %K regulatory submission %K academic research %K public health data %K registry data %K electronic health records %K observational data %K data integration %K FAIR principles %D 2022 %7 27.1.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Real-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them. Objective: The aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose. Methods: We conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review. Results: Overall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources. Conclusions: There are many ongoing data standardization efforts around human health data–related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science. %M 35084343 %R 10.2196/30363 %U https://medinform.jmir.org/2022/1/e30363 %U https://doi.org/10.2196/30363 %U http://www.ncbi.nlm.nih.gov/pubmed/35084343 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e25440 %T Understanding the Nature of Metadata: Systematic Review %A Ulrich,Hannes %A Kock-Schoppenhauer,Ann-Kristin %A Deppenwiese,Noemi %A Gött,Robert %A Kern,Jori %A Lablans,Martin %A Majeed,Raphael W %A Stöhr,Mark R %A Stausberg,Jürgen %A Varghese,Julian %A Dugas,Martin %A Ingenerf,Josef %+ IT Center for Clinical Research, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23564, Germany, 49 45131015607, h.ulrich@uni-luebeck.de %K metadata %K metadata definition %K systematic review %K data integration %K data identification %K data classification %D 2022 %7 11.1.2022 %9 Review %J J Med Internet Res %G English %X Background: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous. Objective: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. Methods: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. Results: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. Conclusions: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context. %M 35014967 %R 10.2196/25440 %U https://www.jmir.org/2022/1/e25440 %U https://doi.org/10.2196/25440 %U http://www.ncbi.nlm.nih.gov/pubmed/35014967 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e31246 %T Key Challenges and Opportunities for Cloud Technology in Health Care: Semistructured Interview Study %A Cresswell,Kathrin %A Domínguez Hernández,Andrés %A Williams,Robin %A Sheikh,Aziz %+ Usher Institute, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, United Kingdom, 44 131 651 4151, Kathrin.Cresswell@ed.ac.uk %K cloud technology %K qualitative %K adoption %K implementation %K digital health %K data processing %K health care %K risk assessment %K user engagement %D 2022 %7 6.1.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind. Objective: We aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed. Methods: We conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the data based on a sociotechnical coding framework developed in related work. Results: We interviewed 23 individuals between September 2020 and November 2020, including professionals working across major cloud providers, health care provider organizations, innovators, small and medium-sized software vendors, and academic institutions. The participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. The identified barriers to progress included the cost of data migration and skill gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations. Conclusions: Implementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment. %M 34989688 %R 10.2196/31246 %U https://humanfactors.jmir.org/2022/1/e31246 %U https://doi.org/10.2196/31246 %U http://www.ncbi.nlm.nih.gov/pubmed/34989688 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e30308 %T The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation %A Stöhr,Mark R %A Günther,Andreas %A Majeed,Raphael W %+ Justus-Liebig-University Giessen, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Klinikstraße 36, Gießen, 35392, Germany, 49 641 985 42117, mark.stoehr@innere.med.uni-giessen.de %K usability %K metadata %K data visualization %K semantic web %K data management %K data warehousing %K communication barriers %K quality improvement %K biological ontologies %K data curation %D 2021 %7 29.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data. Objective: This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues. Methods: We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories. Results: A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app. Conclusions: A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond. %M 34847059 %R 10.2196/30308 %U https://medinform.jmir.org/2021/11/e30308 %U https://doi.org/10.2196/30308 %U http://www.ncbi.nlm.nih.gov/pubmed/34847059 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e29532 %T Introduction of Systematized Nomenclature of Medicine–Clinical Terms Coding Into an Electronic Health Record and Evaluation of its Impact: Qualitative and Quantitative Study %A Pankhurst,Tanya %A Evison,Felicity %A Atia,Jolene %A Gallier,Suzy %A Coleman,Jamie %A Ball,Simon %A McKee,Deborah %A Ryan,Steven %A Black,Ruth %+ NHS Foundation Trust, University Hospitals Birmingham, Mindelsohn Way, Birmingham, B15 2TG, United Kingdom, 44 7811357984, pankhurst.tanya@gmail.com %K coding standards %K clinical decision support %K Clinician led design %K clinician reported experience %K clinical usability %K data sharing %K diagnoses %K electronic health records %K electronic health record standards %K health data exchange %K health data research %K International Classification of Diseases version 10 (ICD-10) %K National Health Service Blueprint %K patient diagnoses %K population health %K problem list %K research %K Systematized Nomenclature Of Medicine–Clinical Terms (SNOMED-CT) %K use of electronic health data %K user-led design %D 2021 %7 23.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: This study describes the conversion within an existing electronic health record (EHR) from the International Classification of Diseases, Tenth Revision coding system to the SNOMED-CT (Systematized Nomenclature of Medicine–Clinical Terms) for the collection of patient histories and diagnoses. The setting is a large acute hospital that is designing and building its own EHR. Well-designed EHRs create opportunities for continuous data collection, which can be used in clinical decision support rules to drive patient safety. Collected data can be exchanged across health care systems to support patients in all health care settings. Data can be used for research to prevent diseases and protect future populations. Objective: The aim of this study was to migrate a current EHR, with all relevant patient data, to the SNOMED-CT coding system to optimize clinical use and clinical decision support, facilitate data sharing across organizational boundaries for national programs, and enable remodeling of medical pathways. Methods: The study used qualitative and quantitative data to understand the successes and gaps in the project, clinician attitudes toward the new tool, and the future use of the tool. Results: The new coding system (tool) was well received and immediately widely used in all specialties. This resulted in increased, accurate, and clinically relevant data collection. Clinicians appreciated the increased depth and detail of the new coding, welcomed the potential for both data sharing and research, and provided extensive feedback for further development. Conclusions: Successful implementation of the new system aligned the University Hospitals Birmingham NHS Foundation Trust with national strategy and can be used as a blueprint for similar projects in other health care settings. %M 34817387 %R 10.2196/29532 %U https://medinform.jmir.org/2021/11/e29532 %U https://doi.org/10.2196/29532 %U http://www.ncbi.nlm.nih.gov/pubmed/34817387 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e29259 %T Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study %A Lamer,Antoine %A Abou-Arab,Osama %A Bourgeois,Alexandre %A Parrot,Adrien %A Popoff,Benjamin %A Beuscart,Jean-Baptiste %A Tavernier,Benoît %A Moussa,Mouhamed Djahoum %+ Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, 2 place de Verdun, Lille, F-59000, France, 33 320626969, antoine.lamer@chru-lille.fr %K data reuse %K common data model %K Observational Medical Outcomes Partnership %K anesthesia %K data warehouse %K reproducible research %D 2021 %7 29.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective: The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods: Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results: We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions: Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse. %M 34714250 %R 10.2196/29259 %U https://www.jmir.org/2021/10/e29259 %U https://doi.org/10.2196/29259 %U http://www.ncbi.nlm.nih.gov/pubmed/34714250 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 10 %P e31288 %T Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries %A Min,Lingtong %A Atalag,Koray %A Tian,Qi %A Chen,Yani %A Lu,Xudong %+ College of Biomedical Engineering & Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, 38 Zheda Road, Hangzhou, China, 86 13957118891, lvxd@zju.edu.cn %K semantic interoperability %K openEHR %K archetype %K registry %K acute coronary syndrome %D 2021 %7 19.10.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: The semantic interoperability of health care information has been a critical challenge in medical informatics and has influenced the integration, sharing, analysis, and use of medical big data. International standard organizations have developed standards, approaches, and models to improve and implement semantic interoperability. The openEHR approach—one of the standout semantic interoperability approaches—has been implemented worldwide to improve semantic interoperability based on reused archetypes. Objective: This study aimed to verify the feasibility of implementing semantic interoperability in different countries by comparing the openEHR-based information models of 2 acute coronary syndrome (ACS) registries from China and New Zealand. Methods: A semantic archetype comparison method was proposed to determine the semantics reuse degree of reused archetypes in 2 ACS-related clinical registries from 2 countries. This method involved (1) determining the scope of reused archetypes; (2) identifying corresponding data items within corresponding archetypes; (3) comparing the semantics of corresponding data items; and (4) calculating the number of mappings in corresponding data items and analyzing results. Results: Among the related archetypes in the two ACS-related, openEHR-based clinical registries from China and New Zealand, there were 8 pairs of reusable archetypes, which included 89 pairs of corresponding data items and 120 noncorresponding data items. Of the 89 corresponding data item pairs, 87 pairs (98%) were mappable and therefore supported semantic interoperability, and 71 pairs (80%) were labeled as “direct mapping” data items. Of the 120 noncorresponding data items, 114 (95%) data items were generated via archetype evolution, and 6 (5%) data items were generated via archetype localization. Conclusions: The results of the semantic comparison between the two ACS-related clinical registries prove the feasibility of establishing the semantic interoperability of health care data from different countries based on the openEHR approach. Archetype reuse provides data on the degree to which semantic interoperability exists when using the openEHR approach. Although the openEHR community has effectively promoted archetype reuse and semantic interoperability by providing archetype modeling methods, tools, model repositories, and archetype design patterns, the uncontrolled evolution of archetypes and inconsistent localization have resulted in major challenges for achieving higher levels of semantic interoperability. %M 34665150 %R 10.2196/31288 %U https://medinform.jmir.org/2021/10/e31288 %U https://doi.org/10.2196/31288 %U http://www.ncbi.nlm.nih.gov/pubmed/34665150 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 10 %P e27396 %T Common Data Elements for Meaningful Stroke Documentation in Routine Care and Clinical Research: Retrospective Data Analysis %A Berenspöhler,Sarah %A Minnerup,Jens %A Dugas,Martin %A Varghese,Julian %+ Institute of Medical Informatics, Westfälische Wilhelms-University Münster, Albert Schweitzer Campus 1, Building A11, Münster, 48149, Germany, 49 251 83 55262, s.berenspoehler@t-online.de %K common data elements %K stroke %K documentation %D 2021 %7 12.10.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Medical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. Objective: This study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. Methods: Medical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. Results: In total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. Conclusions: Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research. %M 34636733 %R 10.2196/27396 %U https://medinform.jmir.org/2021/10/e27396 %U https://doi.org/10.2196/27396 %U http://www.ncbi.nlm.nih.gov/pubmed/34636733 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 10 %P e31980 %T Expressiveness of an International Semantic Standard for Wound Care: Mapping a Standardized Item Set for Leg Ulcers to the Systematized Nomenclature of Medicine–Clinical Terms %A Hüsers,Jens %A Przysucha,Mareike %A Esdar,Moritz %A John,Swen Malte %A Hübner,Ursula Hertha %+ University of Applied Sciences Osnabrück, Albrechtstr 30, Osnabrück, 49076, Germany, 49 5419692012, u.huebner@hs-osnabrueck.de %K wound care %K chronic wound %K chronic leg ulcer %K SNOMED CT %K health information exchange %K semantic interoperability %K terminology mapping %D 2021 %7 6.10.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Chronic health conditions are on the rise and are putting high economic pressure on health systems, as they require well-coordinated prevention and treatment. Among chronic conditions, chronic wounds such as cardiovascular leg ulcers have a high prevalence. Their treatment is highly interdisciplinary and regularly spans multiple care settings and organizations; this places particularly high demands on interoperable information exchange that can be achieved using international semantic standards, such as Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT). Objective: This study aims to investigate the expressiveness of SNOMED CT in the domain of wound care, and thereby its clinical usefulness and the potential need for extensions. Methods: A clinically consented and profession-independent wound care item set, the German National Consensus for the Documentation of Leg Wounds (NKDUC), was mapped onto the precoordinated concepts of the international reference terminology SNOMED CT. Before the mapping took place, the NKDUC was transformed into an information model that served to systematically identify relevant items. The mapping process was carried out in accordance with the ISO/TR 12300 formalism. As a result, the reliability, equivalence, and coverage rate were determined for all NKDUC items and sections. Results: The developed information model revealed 268 items to be mapped. Conducted by 3 health care professionals, the mapping resulted in moderate reliability (κ=0.512). Regarding the two best equivalence categories (symmetrical equivalence of meaning), the coverage rate of SNOMED CT was 67.2% (180/268) overall and 64.3% (108/168) specifically for wounds. The sections general medical condition (55/66, 83%), wound assessment (18/24, 75%), and wound status (37/57, 65%), showed higher coverage rates compared with the sections therapy (45/73, 62%), wound diagnostics (8/14, 57%), and patient demographics (17/34, 50%). Conclusions: The results yielded acceptable reliability values for the mapping procedure. The overall coverage rate shows that two-thirds of the items could be mapped symmetrically, which is a substantial portion of the source item set. Some wound care sections, such as general medical conditions and wound assessment, were covered better than other sections (wound status, diagnostics, and therapy). These deficiencies can be mitigated either by postcoordination or by the inclusion of new concepts in SNOMED CT. This study contributes to pushing interoperability in the domain of wound care, thereby responding to the high demand for information exchange in this field. Overall, this study adds another puzzle piece to the general knowledge about SNOMED CT in terms of its clinical usefulness and its need for further extensions. %M 34428171 %R 10.2196/31980 %U https://medinform.jmir.org/2021/10/e31980 %U https://doi.org/10.2196/31980 %U http://www.ncbi.nlm.nih.gov/pubmed/34428171 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 7 %P e26770 %T Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)–Based Data Model: Development and Topic Modeling Study %A De,Amrita %A Huang,Ming %A Feng,Tinghao %A Yue,Xiaomeng %A Yao,Lixia %+ Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 1st St SW, Rochester, MN, United States, 1 5072842511, lixia.cn.yao@gmail.com %K patient secure messages %K patient portal %K data model %K FHIR %K annotated corpus %K topic modeling %D 2021 %7 30.7.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. Objective: This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. Methods: We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. Results: The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. Conclusions: Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care. %M 34328444 %R 10.2196/26770 %U https://www.jmir.org/2021/7/e26770 %U https://doi.org/10.2196/26770 %U http://www.ncbi.nlm.nih.gov/pubmed/34328444 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 7 %P e21929 %T The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities %A Ayaz,Muhammad %A Pasha,Muhammad F %A Alzahrani,Mohammed Y %A Budiarto,Rahmat %A Stiawan,Deris %+ Malaysia School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia, 60 0355146224, Muhammad.ayaz@monash.edu %K Fast Health Interoperability Resources %K FHIR %K electronic health record %K EHR %K clinical document architecture %K CDA %K Substitutable Medical Applications Reusable Technologies %K SMART %K HL7 %K health standard %K systematic literature review %D 2021 %7 30.7.2021 %9 Review %J JMIR Med Inform %G English %X Background: Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain. Objective: The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications. Methods: In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration. Results: The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR. Conclusions: The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future. %M 34328424 %R 10.2196/21929 %U https://medinform.jmir.org/2021/7/e21929 %U https://doi.org/10.2196/21929 %U http://www.ncbi.nlm.nih.gov/pubmed/34328424 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 6 %P e27591 %T A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study %A Gaudet-Blavignac,Christophe %A Raisaro,Jean Louis %A Touré,Vasundra %A Österle,Sabine %A Crameri,Katrin %A Lovis,Christian %+ Division of Medical Information Sciences, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1205, Switzerland, 41 223726201, christophe.gaudet-blavignac@hcuge.ch %K interoperability %K clinical data reuse %K personalized medicine %D 2021 %7 24.6.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Interoperability is a well-known challenge in medical informatics. Current trends in interoperability have moved from a data model technocentric approach to sustainable semantics, formal descriptive languages, and processes. Despite many initiatives and investments for decades, the interoperability challenge remains crucial. The need for data sharing for most purposes ranging from patient care to secondary uses, such as public health, research, and quality assessment, faces unmet problems. Objective: This work was performed in the context of a large Swiss Federal initiative aiming at building a national infrastructure for reusing consented data acquired in the health care and research system to enable research in the field of personalized medicine in Switzerland. The initiative is the Swiss Personalized Health Network (SPHN). This initiative is providing funding to foster use and exchange of health-related data for research. As part of the initiative, a national strategy to enable a semantically interoperable clinical data landscape was developed and implemented. Methods: A deep analysis of various approaches to address interoperability was performed at the start, including large frameworks in health care, such as Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE), and in several domains, such as regulatory agencies (eg, Clinical Data Interchange Standards Consortium [CDISC]) and research communities (eg, Observational Medical Outcome Partnership [OMOP]), to identify bottlenecks and assess sustainability. Based on this research, a strategy composed of three pillars was designed. It has strong multidimensional semantics, descriptive formal language for exchanges, and as many data models as needed to comply with the needs of various communities. Results: This strategy has been implemented stepwise in Switzerland since the middle of 2019 and has been adopted by all university hospitals and high research organizations. The initiative is coordinated by a central organization, the SPHN Data Coordination Center of the SIB Swiss Institute of Bioinformatics. The semantics is mapped by domain experts on various existing standards, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and International Classification of Diseases (ICD). The resource description framework (RDF) is used for storing and transporting data, and to integrate information from different sources and standards. Data transformers based on SPARQL query language are implemented to convert RDF representations to the numerous data models required by the research community or bridge with other systems, such as electronic case report forms. Conclusions: The SPHN strategy successfully implemented existing standards in a pragmatic and applicable way. It did not try to build any new standards but used existing ones in a nondogmatic way. It has now been funded for another 4 years, bringing the Swiss landscape into a new dimension to support research in the field of personalized medicine and large interoperable clinical data. %M 34185008 %R 10.2196/27591 %U https://medinform.jmir.org/2021/6/e27591/ %U https://doi.org/10.2196/27591 %U http://www.ncbi.nlm.nih.gov/pubmed/34185008 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 4 %P e25645 %T A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study %A Gruendner,Julian %A Gulden,Christian %A Kampf,Marvin %A Mate,Sebastian %A Prokosch,Hans-Ulrich %A Zierk,Jakob %+ Chair of Medical Informatics, Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Wetterkreuz 15, Erlangen-Tennenlohe, Germany, 49 91318526785, julian.gruendner@fau.de %K data analysis %K data science %K data standardization %K digital medical information %K eHealth %K Fast Healthcare Interoperability Resources %K data harmonization %K medical information %K patient privacy %K data repositories %K HL7 FHIR %D 2021 %7 1.4.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: The harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. Objective: This study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. Methods: We propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. Results: We imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including “Patient,” “Encounter,” “Condition” (diagnoses specified using International Classification of Diseases codes), “Procedure,” and “Observation” (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). Conclusions: This study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients’ privacy while being integrated with existing open source data analysis tools currently being developed across Germany. %M 33792554 %R 10.2196/25645 %U https://medinform.jmir.org/2021/4/e25645 %U https://doi.org/10.2196/25645 %U http://www.ncbi.nlm.nih.gov/pubmed/33792554 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24570 %T Digital Medicine Community Perspectives and Challenges: Survey Study %A Bent,Brinnae %A Sim,Ida %A Dunn,Jessilyn P %+ Department of Biostatistics & Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Durham, NC, 27705, United States, 1 9196689798, jessilyn.dunn@duke.edu %K digital medicine %K digital health %K interoperability %K mHealth %K wearables %K sensors %D 2021 %7 3.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating digital medicine into practice. In order to understand how to mitigate challenges in digital medicine research and practice, this community must understand the landscape of digital medicine professionals, which digital medicine tools are being used and how, and user perspectives on current challenges in the field of digital medicine. Objective: The primary objective of this study is to provide information to the digital medicine community that is working to establish frameworks and best practices for interoperability in digital medicine. We sought to learn about the background of digital medicine professionals and determine which sensors and file types are being used most commonly in digital medicine research. We also sought to understand perspectives on digital medicine interoperability. Methods: We used a web-based survey to query a total of 56 digital medicine professionals from May 1, 2020, to July 10, 2020, on their educational and work experience, the sensors, file types, and toolkits they use professionally, and their perspectives on interoperability in digital medicine. Results: We determined that the digital medicine community comes from diverse educational backgrounds and uses a variety of sensors and file types. Sensors measuring physical activity and the cardiovascular system are the most frequently used, and smartphones continue to be the dominant source of digital health information collection in the digital medicine community. We show that there is not a general consensus on file types in digital medicine, and data are currently handled in multiple ways. There is consensus that interoperability is a critical impediment in digital medicine, with 93% (52) of survey respondents in agreement. However, only 36% (20) of respondents currently use tools for interoperability in digital medicine. We identified three key interoperability needs to be met: integration with electronic health records, implementation of standard data schemas, and standard and verifiable methods for digital medicine research. We show that digital medicine professionals are eager to adopt new tools to solve interoperability problems, and we suggest tools to support digital medicine interoperability. Conclusions: Understanding the digital medicine community, the sensors and file types they use, and their perspectives on interoperability will enable the development and implementation of solutions that fill critical interoperability gaps in digital medicine. The challenges to interoperability outlined by this study will drive the next steps in creating an interoperable digital medicine community. Establishing best practices to address these challenges and employing platforms for digital medicine interoperability will be essential to furthering the field of digital medicine. %M 33533721 %R 10.2196/24570 %U http://mhealth.jmir.org/2021/2/e24570/ %U https://doi.org/10.2196/24570 %U http://www.ncbi.nlm.nih.gov/pubmed/33533721 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e24594 %T Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review %A Gaudet-Blavignac,Christophe %A Foufi,Vasiliki %A Bjelogrlic,Mina %A Lovis,Christian %+ Division of Medical Information Sciences, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1205, Switzerland, 41 22 372 62 01, christophe.gaudet-blavignac@hcuge.ch %K SNOMED CT %K natural language processing %K scoping review %K terminology %D 2021 %7 26.1.2021 %9 Review %J J Med Internet Res %G English %X Background: Interoperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the universal language of health care and presents characteristics of a natural language. Its use to represent clinical free text could constitute a solution to improve interoperability. Objective: Although the use of SNOMED and SNOMED CT has already been reviewed, its specific use in processing and representing unstructured data such as clinical free text has not. This review aims to better understand SNOMED CT's use for representing free text in medicine. Methods: A scoping review was performed on the topic by searching MEDLINE, Embase, and Web of Science for publications featuring free-text processing and SNOMED CT. A recursive reference review was conducted to broaden the scope of research. The review covered the type of processed data, the targeted language, the goal of the terminology binding, the method used and, when appropriate, the specific software used. Results: In total, 76 publications were selected for an extensive study. The language targeted by publications was 91% (n=69) English. The most frequent types of documents for which the terminology was used are complementary exam reports (n=18, 24%) and narrative notes (n=16, 21%). Mapping to SNOMED CT was the final goal of the research in 21% (n=16) of publications and a part of the final goal in 33% (n=25). The main objectives of mapping are information extraction (n=44, 39%), feature in a classification task (n=26, 23%), and data normalization (n=23, 20%). The method used was rule-based in 70% (n=53) of publications, hybrid in 11% (n=8), and machine learning in 5% (n=4). In total, 12 different software packages were used to map text to SNOMED CT concepts, the most frequent being Medtex, Mayo Clinic Vocabulary Server, and Medical Text Extraction Reasoning and Mapping System. Full terminology was used in 64% (n=49) of publications, whereas only a subset was used in 30% (n=23) of publications. Postcoordination was proposed in 17% (n=13) of publications, and only 5% (n=4) of publications specifically mentioned the use of the compositional grammar. Conclusions: SNOMED CT has been largely used to represent free-text data, most frequently with rule-based approaches, in English. However, currently, there is no easy solution for mapping free text to this terminology and to perform automatic postcoordination. Most solutions conceive SNOMED CT as a simple terminology rather than as a compositional bag of ontologies. Since 2012, the number of publications on this subject per year has decreased. However, the need for formal semantic representation of free text in health care is high, and automatic encoding into a compositional ontology could be a solution. %M 33496673 %R 10.2196/24594 %U http://www.jmir.org/2021/1/e24594/ %U https://doi.org/10.2196/24594 %U http://www.ncbi.nlm.nih.gov/pubmed/33496673 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 1 %P e25149 %T Interoperable Platform to Report Polymerase Chain Reaction SARS-CoV-2 Tests From Laboratories to the Chilean Government: Development and Implementation Study %A Guinez-Molinos,Sergio %A Andrade,José María %A Medina Negrete,Alejandro %A Espinoza Vidal,Sonia %A Rios,Elvis %+ Laboratory of Biomedical Informatics, School of Medicine, Universidad de Talca, Campus San Miguel, Avda. San Miguel S/N, Talca, 3460000, Chile, 56 71 2418820, sguinez@utalca.cl %K COVID-19 %K SARS-CoV-2 %K interoperability %K laboratory information system %K HL7 FHIR %K PCR %D 2021 %7 20.1.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Testing, traceability, and isolation actions are a central strategy defined by the World Health Organization to contain the COVID-19 pandemic. In this sense, the countries have had difficulties in counting the number of people infected with SARS-CoV-2. Errors in reporting results are a common factor, as well as the lack of interoperability between laboratories and governments. Approaches aimed at sending spreadsheets via email expose patients’ privacy and have increased the probability of errors due to retyping, which generates a delay in the notification of results. Objective: This study aims to design and develop an interoperable platform to report polymerase chain reaction (PCR) SARS-CoV-2 tests from laboratories to the Chilean government. Methods: The methodology to design and develop the interoperable platform was comprised of six well-structured stages: (1) creation of a minimum data set for PCR SARS-CoV-2 tests, (2) modeling processes and end points where institutions interchange information, (3) standards and interoperability design, (4) software development, (5) software testing, and (6) software implementation. Results: The interoperable Fast Healthcare Interoperability Resources (FHIR) platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government was successfully implemented. The platform was designed, developed, tested, and implemented following a structured methodology. The platform’s performance to 1000 requests resulted in a response time of 240 milliseconds, throughput of 28.3 requests per second, and process management time of 131 milliseconds. The security was assured through a private network exclusive to the Ministry of Health to ensure confidentiality and integrity. The authorization and authentication of laboratories were implemented with a JavaScript Object Notation Web Token. All the PCR SARS-CoV-2 tests were accessible through an application programming interface gateway with valid credentials and the right access control list. Conclusions: The platform was implemented and is currently being used by UC Christus Laboratory. The platform is secure. It was tested adequately for confidentiality, secure authorization, authentication, and message integrity. This platform simplifies the reporting of PCR SARS-CoV-2 tests and reduces the time and probability of mistakes in counting positive cases. The interoperable solution with FHIR is working successfully and is open for the community, laboratories, and any institution that needs to report PCR SARS-CoV-2 tests. %M 33417587 %R 10.2196/25149 %U http://medinform.jmir.org/2021/1/e25149/ %U https://doi.org/10.2196/25149 %U http://www.ncbi.nlm.nih.gov/pubmed/33417587 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 1 %P e20470 %T Prototypical Clinical Trial Registry Based on Fast Healthcare Interoperability Resources (FHIR): Design and Implementation Study %A Gulden,Christian %A Blasini,Romina %A Nassirian,Azadeh %A Stein,Alexandra %A Altun,Fatma Betül %A Kirchner,Melanie %A Prokosch,Hans-Ulrich %A Boeker,Martin %+ Chair of Medical Informatics, Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Wetterkreuz 15, Erlangen, 91058, Germany, 49 9131 85 47291, christian.gulden@fau.de %K clinical trials %K trials registry %K health information interoperability %K data sharing %K HL7 FHIR %D 2021 %7 12.1.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Clinical trial registries increase transparency in medical research by making information and results of planned, ongoing, and completed studies publicly available. However, the registration of clinical trials remains a time-consuming manual task complicated by the fact that the same studies often need to be registered in different registries with different data entry requirements and interfaces. Objective: This study investigates how Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) may be used as a standardized format for exchanging and storing clinical trial records. Methods: We designed and prototypically implemented an open-source central trial registry containing records from university hospitals, which are automatically exported and updated by local study management systems. Results: We provided an architecture and implementation of a multisite clinical trials registry based on HL7 FHIR as a data storage and exchange format. Conclusions: The results show that FHIR resources establish a harmonized view of study information from heterogeneous sources by enabling automated data exchange between trial centers and central study registries. %M 33433393 %R 10.2196/20470 %U https://medinform.jmir.org/2021/1/e20470 %U https://doi.org/10.2196/20470 %U http://www.ncbi.nlm.nih.gov/pubmed/33433393 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 12 %P e22000 %T ISO/IEEE 11073 Treadmill Interoperability Framework and its Test Method: Design and Implementation %A Huang,Zhi Yong %A Wang,Yujie %A Wang,Linling %+ School of Microelectronics and Communication Engineering, Chongqing University, No 174 Shazhengjie, Shapingba, Chongqing, 400044, China, 86 02365103544, zyhuang@cqu.edu.cn %K ISO/IEEE 11073-PHD %K treadmill %K standard frame model %K test standard %K sports health data %D 2020 %7 9.12.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Regular physical activity is proven to help prevent and treat noncommunicable diseases such as heart disease, stroke, diabetes, and breast and colon cancer. The exercise data generated by health and fitness devices (eg, treadmill, exercise bike) are very important for health management service providers to develop personalized training programs. However, at present, there is little research on a unified interoperability framework in the health and fitness domain, and there are not many solutions; besides, the privatized treadmill data transmission scheme is not conducive to data integration and analysis. Objective: This article will expand the IEEE 11073-PHD standard protocol family, develop standards for health and fitness device (using treadmill as an example) based on the latest version of the 11073-20601 optimized exchange protocol, and design protocol standards compliance testing process and inspection software, which can automatically detect whether the instantiated object of the treadmill meets the standard. Methods: The study includes the following steps: (1) Map the data transmitted by the treadmill to the 11073-PHD objects; (2) Construct a programming language structure corresponding to the 11073-PHD application protocol data unit (APDU) to complete the coding and decoding part of the test software; and (3) Transmit the instantiated simulated treadmill data to the gateway test software through transmission control protocol for standard compliance testing. Results: According to the characteristics of the treadmill, a data exchange framework conforming to 11073-PHD is constructed, and a corresponding testing framework is developed; a treadmill agent simulation is implemented, and the interoperability test is performed. Through the designed testing process, the corresponding testing software was developed to complete the standard compliance testing of the treadmill. Conclusions: The extended research of IEEE 11073-PHD in the field of health and fitness provides a potential new idea for the data transmission framework of sports equipment such as treadmills, which may also provide some help for the development of sports health equipment interoperability standards. %M 33295293 %R 10.2196/22000 %U http://medinform.jmir.org/2020/12/e22000/ %U https://doi.org/10.2196/22000 %U http://www.ncbi.nlm.nih.gov/pubmed/33295293 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 7 %P e17176 %T Data Integration in the Brazilian Public Health System for Tuberculosis: Use of the Semantic Web to Establish Interoperability %A Pellison,Felipe Carvalho %A Rijo,Rui Pedro Charters Lopes %A Lima,Vinicius Costa %A Crepaldi,Nathalia Yukie %A Bernardi,Filipe Andrade %A Galliez,Rafael Mello %A Kritski,Afrânio %A Abhishek,Kumar %A Alves,Domingos %+ Bioengineering Postgraduate Program of the São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-Carlense, 400, São Carlos, 13566-590, Brazil, 55 16 3373 9586, felipecp@alumni.usp.br %K health information systems %K tuberculosis %K ontology %K interoperability %K electronic health records %K semantic web %D 2020 %7 6.7.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Interoperability of health information systems is a challenge due to the heterogeneity of existing systems at both the technological and semantic levels of their data. The lack of existing data about interoperability disrupts intra-unit and inter-unit medical operations as well as creates challenges in conducting studies on existing data. The goal is to exchange data while providing the same meaning for data from different sources. Objective: To find ways to solve this challenge, this research paper proposes an interoperability solution for the tuberculosis treatment and follow-up scenario in Brazil using Semantic Web technology supported by an ontology. Methods: The entities of the ontology were allocated under the definitions of Basic Formal Ontology. Brazilian tuberculosis applications were tagged with entities from the resulting ontology. Results: An interoperability layer was developed to retrieve data with the same meaning and in a structured way enabling semantic and functional interoperability. Conclusions: Health professionals could use the data gathered from several data sources to enhance the effectiveness of their actions and decisions, as shown in a practical use case to integrate tuberculosis data in the State of São Paulo. %M 32628611 %R 10.2196/17176 %U https://medinform.jmir.org/2020/7/e17176 %U https://doi.org/10.2196/17176 %U http://www.ncbi.nlm.nih.gov/pubmed/32628611 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 4 %P e15199 %T Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study %A Pfaff,Emily Rose %A Champion,James %A Bradford,Robert Louis %A Clark,Marshall %A Xu,Hao %A Fecho,Karamarie %A Krishnamurthy,Ashok %A Cox,Steven %A Chute,Christopher G %A Overby Taylor,Casey %A Ahalt,Stan %+ North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, 160 N Medical Drive, Chapel Hill, NC, 27599, United States, 1 919 843 4712, epfaff@email.unc.edu %K health information interoperability %K electronic health records %K data sharing %K controlled vocabularies %D 2019 %7 16.10.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: In a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data. Objective: In this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. Methods: Mapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. Results: We have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. Conclusions: We believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research. %M 31621639 %R 10.2196/15199 %U https://medinform.jmir.org/2019/4/e15199 %U https://doi.org/10.2196/15199 %U http://www.ncbi.nlm.nih.gov/pubmed/31621639 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 5 %P e13504 %T Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR %A Yang,Lin %A Huang,Xiaoshuo %A Li,Jiao %+ Institute of Medical Information / Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, No 3 Yabao Road, Chaoyang District, Beijing, 100020, China, 86 18618461596, li.jiao@imicams.ac.cn %K openEHR %K clinical information model %K health information interoperability %K information retrieval %K probabilistic graphical model %D 2019 %7 28.05.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important. Objective: This study aimed to develop a retrieval method to identify CIMs online to represent EHR data. Methods: We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics. Results: We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering “diagnostic reports,” “problem list,” “patients background,” “clinical decision,” etc, as well as models that other retrieval methods could not find, such as “problems and diagnoses.” Conclusions: The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable. %M 31140433 %R 10.2196/13504 %U http://www.jmir.org/2019/5/e13504/ %U https://doi.org/10.2196/13504 %U http://www.ncbi.nlm.nih.gov/pubmed/31140433 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 1 %P e12630 %T An Assessment of the Interoperability of Electronic Health Record Exchanges Among Hospitals and Clinics in Taiwan %A Wen,Hsyien-Chia %A Chang,Wei-Pin %A Hsu,Min-Huei %A Ho,Cheng-Hsun %A Chu,Chi-Ming %+ School of Healthcare Administration, College of Management, Taipei Medical University, 10F., No 172-1, Section 2, Keelung Road, Xinyi District, Taipei, 106, Taiwan, 886 6638 2736 ext 1019, hcwenn@tmu.edu.tw %K electronic health records %K interoperability %K data exchange %K hospitals %K clinics %D 2019 %7 28.03.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: The rapid aging of the Taiwanese population in recent years has led to high medical needs for the elderly and increasing medical costs. Integrating patient information through electronic health records (EHRs) to reduce unnecessary medications and tests and enhance the quality of care has currently become an important issue. Although electronic data interchanges among hospitals and clinics have been implemented for many years in Taiwan, the interoperability of EHRs has not adequately been assessed. Objective: The study aimed to analyze the efficiency of data exchanges and provide suggestions for future improvements. Methods: We obtained 30 months of uploaded and downloaded data of EHRs among hospitals and clinics. The research objects of this study comprised 19 medical centers, 57 regional hospitals, 95 district hospitals, and 5520 clinics. We examined 4 exchange EHR forms: laboratory test reports, medical images, discharge summaries, and outpatient medical records. We used MySQL (Oracle Corporation) software (to save our data) and phpMyAdmin, which is a Personal Home Page program, to manage the database and then analyzed the data using SPSS 19.0 statistical software. Results: The quarterly mean uploaded volume of EHRs among hospitals was 52,790,721 (SD 580,643). The quarterly mean downloaded volume of EHRs among hospitals and clinics was 650,323 (SD 215,099). The ratio of uploaded to downloaded EHRs was about 81:1. The total volume of EHRs was mainly downloaded by medical centers and clinics, which accounted for 53.82% (mean 318,717.80) and 45.41% (mean 269,082.10), respectively, and the statistical test was significant among different hospital accreditation levels (F2=7.63; P<.001). A comparison of EHR download volumes among the 6 National Health Insurance (NHI) branches showed that the central NHI branch downloaded 11,366,431 records (21.53%), which was the highest, and the eastern branch downloaded 1,615,391 records (3.06%), which was the lowest. The statistical test among the 6 NHI branches was significant (F5=8.82; P<.001). The download volumes of laboratory tests reports and outpatient medical records were 26,980,425 (50.3%) and 21,747,588 records (40.9%), respectively, and were much higher than medical images and discharge summaries. The statistical test was also significant (F=17.72; P<.001). Finally, the download time showed that the average for x-rays was 32.05 seconds, which was the longest, and was 9.92 seconds for electrocardiogram, which was the shortest, but there was no statistically significant difference among download times for various medical images. Conclusions: After years of operation, the Electronic Medical Record Exchange Center has achieved the initial goal of EHR interoperability, and data exchanges are running quite stably in Taiwan. However, the meaningful use of EHRs among hospitals and clinics still needs further encouragement and promotion. We suggest that the government’s leading role and collective collaboration with health care organizations are important for providing effective health information exchanges. %M 30920376 %R 10.2196/12630 %U http://medinform.jmir.org/2019/1/e12630/ %U https://doi.org/10.2196/12630 %U http://www.ncbi.nlm.nih.gov/pubmed/30920376 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 4 %P e10870 %T Validation and Testing of Fast Healthcare Interoperability Resources Standards Compliance: Data Analysis %A Walonoski,Jason %A Scanlon,Robert %A Dowling,Conor %A Hyland,Mario %A Ettema,Richard %A Posnack,Steven %+ The MITRE Corporation, 202 Burlington Road, Bedford, MA, 01730, United States, 1 781 271 2021, jwalonoski@mitre.org %K electronic health records %K health data interoperability %K test-driven development %K practice makes perfect %D 2018 %7 23.10.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: There is wide recognition that the lack of health data interoperability has significant impacts. Traditionally, health data standards are complex and test-driven methods played important roles in achieving interoperability. The Health Level Seven International (HL7) standard Fast Healthcare Interoperability Resources (FHIR) may be a technical solution that aligns with policy, but systems need to be validated and tested. Objective: Our objective is to explore the question of whether or not the regular use of validation and testing tools improves server compliance with the HL7 FHIR specification. Methods: We used two independent validation and testing tools, Crucible and Touchstone, and analyzed the usage and result data to determine their impact on server compliance with the HL7 FHIR specification. Results: The use of validation and testing tools such as Crucible and Touchstone are strongly correlated with increased compliance and “practice makes perfect.” Frequent and thorough testing has clear implications for health data interoperability. Additional data analysis reveals trends over time with respect to vendors, use cases, and FHIR versions. Conclusions: Validation and testing tools can aid in the transition to an interoperable health care infrastructure. Developers that use testing and validation tools tend to produce more compliant FHIR implementations. When it comes to health data interoperability, “practice makes perfect.” %M 30355549 %R 10.2196/10870 %U http://medinform.jmir.org/2018/4/e10870/ %U https://doi.org/10.2196/10870 %U http://www.ncbi.nlm.nih.gov/pubmed/30355549 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4395 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, ELR messages from two states were analyzed. Less than 20% of incoming messages contained any form of standardized vocabulary, indicating that semantic interoperability remains a challenge. %R 10.5210/ojphi.v5i1.4395 %U %U https://doi.org/10.5210/ojphi.v5i1.4395 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 5 %N 1 %P e4451 %T Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review %D 2013 %7 ..2013 %9 %J Online J Public Health Inform %G English %X Join us as we explore the impact of ONC''s Standards & Interoperability Framework Public Health Reporting Initiative (PHRI). PHRI is working to simplify public health reporting and ensure that EHRs are interoperable with public health information systems. PHRI hopes to create a new public health Meaningful Use Stage 3 objective that is common across all program objectives - laying the ground work for public health reporting in the future. This panel will outline progress, challenges, and next steps of PHRI and describe how PHRI may affect the future of a standard language for biosurveillance. %R 10.5210/ojphi.v5i1.4451 %U %U https://doi.org/10.5210/ojphi.v5i1.4451