TY - JOUR AU - Tighe, Carlos AU - Ngongalah, Lem AU - Sentís, Alexis AU - Orchard, Francisco AU - Pacurar, Gheorghe-Aurel AU - Hayes, Conor AU - Hayes, S. Jessica AU - Toader, Adrian AU - Connolly, A. Máire PY - 2025/3/5 TI - Building and Developing a Tool (PANDEM-2 Dashboard) to Strengthen Pandemic Management: Participatory Design Study JO - JMIR Public Health Surveill SP - e52119 VL - 11 KW - pandemic preparedness and response KW - COVID-19 KW - cross-border collaboration KW - surveillance KW - data collection KW - data standardization KW - data sharing KW - dashboard KW - IT system KW - IT tools N2 - Background: The COVID-19 pandemic exposed challenges in pandemic management, particularly in real-time data sharing and effective decision-making. Data protection concerns and the lack of data interoperability and standardization hindered the collection, analysis, and interpretation of critical information. Effective data visualization and customization are essential to facilitate decision-making. Objective: This study describes the development of the PANDEM-2 dashboard, a system providing a standardized and interactive platform for decision-making in pandemic management. It outlines the participatory approaches used to involve expert end users in its development and addresses key considerations of privacy, data protection, and ethical and social issues. Methods: Development was informed by a review of 25 publicly available COVID-19 dashboards, leading to the creation of a visualization catalog. User requirements were gathered through workshops and consultations with 20 experts from various health care and public health professions in 13 European Union countries. These were further refined by mapping variables and indicators required to fulfill the identified needs. Through a participatory design process, end users interacted with a preprototype platform, explored potential interface designs, and provided feedback to refine the system?s components. Potential privacy, data protection, and ethical and social risks associated with the technology, along with mitigation strategies, were identified through an iterative impact assessment. Results: Key variables incorporated into the PANDEM-2 dashboard included case rates, number of deaths, mortality rates, hospital resources, hospital admissions, testing, contact tracing, and vaccination uptake. Cases, deaths, and vaccination uptake were prioritized as the most relevant and readily available variables. However, data gaps, particularly in contact tracing and mortality rates, highlighted the need for better data collection and reporting mechanisms. User feedback emphasized the importance of diverse data visualization formats combining different data types, as well as analyzing data across various time frames. Users also expressed interest in generating custom visualizations and reports, especially on the impact of government interventions. Participants noted challenges in data reporting, such as inconsistencies in reporting levels, time intervals, the need for standardization between member states, and General Data Protection Regulation concerns for data sharing. Identified risks included ethical concerns (accessibility, user autonomy, responsible use, transparency, and accountability), privacy and data protection (security and access controls and data reidentification), and social issues (unintentional bias, data quality and accuracy, dependency on technology, and collaborative development). Mitigation measures focused on designing user-friendly interfaces, implementing robust security protocols, and promoting cross-member state collaboration. Conclusions: The PANDEM-2 dashboard provides an adaptable, user-friendly platform for pandemic preparedness and response. Our findings highlight the critical role of data interoperability, cross-border collaboration, and custom IT tools in strengthening future health crisis management. They also offer valuable insights into the challenges and opportunities in developing IT solutions to support pandemic preparedness. UR - https://publichealth.jmir.org/2025/1/e52119 UR - http://dx.doi.org/10.2196/52119 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053759 ID - info:doi/10.2196/52119 ER - TY - JOUR AU - Bartel, Christianna AU - Chen, Leeann AU - Huang, Weiyu AU - Li, Qichang AU - Li, Qingyang AU - Fedor, Jennifer AU - Durica, C. Krina AU - Low, A. Carissa PY - 2025/1/10 TI - Design and Use of Patient-Facing Electronic Patient-Reported Outcomes and Sensor Data Visualizations During Outpatient Chemotherapy JO - JMIR Cancer SP - e62711 VL - 11 KW - oncology KW - cancer KW - data visualization KW - remote monitoring KW - mobile technology KW - patients KW - outpatient KW - chemotherapy KW - symptoms KW - side effects KW - cancer treatment KW - electronic patient-reported outcome KW - online KW - monitoring KW - self-management UR - https://cancer.jmir.org/2025/1/e62711 UR - http://dx.doi.org/10.2196/62711 ID - info:doi/10.2196/62711 ER - TY - JOUR AU - Knight, Jo AU - Chandrabalan, Vardhan Vishnu AU - Emsley, A. Hedley C. PY - 2024/12/24 TI - Visualizing Patient Pathways and Identifying Data Repositories in a UK Neurosciences Center: Exploratory Study JO - JMIR Med Inform SP - e60017 VL - 12 KW - health data KW - business process monitoring notation KW - neurology KW - process monitoring KW - patient pathway KW - clinical pathway KW - patient care KW - EHR KW - electronic health record KW - dataset KW - questionnaire KW - patient data KW - NHS KW - National Health Service N2 - 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. UR - https://medinform.jmir.org/2024/1/e60017 UR - http://dx.doi.org/10.2196/60017 ID - info:doi/10.2196/60017 ER - TY - JOUR AU - Helminski, Danielle AU - Sussman, B. Jeremy AU - Pfeiffer, N. Paul AU - Kokaly, N. Alex AU - Ranusch, Allison AU - Renji, Deep Anjana AU - Damschroder, J. Laura AU - Landis-Lewis, Zach AU - Kurlander, E. Jacob PY - 2024/12/10 TI - Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review JO - JMIR Med Inform SP - e59828 VL - 12 KW - dashboard KW - medical informatics KW - quality improvement KW - electronic health record KW - scoping review KW - monitoring KW - health care system KW - patient care KW - clinical research KW - emergency department KW - inpatient KW - clinical management N2 - Background: Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context. Objective: This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context. Methods: MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018?2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted. Results: From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ?2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%). Conclusions: Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation?which can provide insight into ways to improve dashboard effectiveness?is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners. International Registered Report Identifier (IRRID): RR2-10.2196/34894 UR - https://medinform.jmir.org/2024/1/e59828 UR - http://dx.doi.org/10.2196/59828 ID - info:doi/10.2196/59828 ER - TY - JOUR AU - AboArab, A. Mohammed AU - Potsika, T. Vassiliki AU - Theodorou, Alexis AU - Vagena, Sylvia AU - Gravanis, Miltiadis AU - Sigala, Fragiska AU - Fotiadis, I. Dimitrios PY - 2024/12/9 TI - 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 JO - JMIR Med Inform SP - e63834 VL - 12 KW - medical image visualization KW - peripheral artery computed tomography imaging KW - multiplanar reconstruction KW - progressive web applications N2 - 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. UR - https://medinform.jmir.org/2024/1/e63834 UR - http://dx.doi.org/10.2196/63834 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63834 ER - TY - JOUR AU - Patel, Mohammed Ahmed AU - Baxter, Weston AU - Porat, Talya PY - 2024/11/20 TI - Toward Guidelines for Designing Holistic Integrated Information Visualizations for Time-Critical Contexts: Systematic Review JO - J Med Internet Res SP - e58088 VL - 26 KW - visualization KW - design KW - holistic KW - integrated KW - time-critical KW - guidelines KW - pre-attentive processing KW - gestalt theory KW - situation awareness KW - decision-making KW - mobile phone N2 - Background: With the extensive volume of information from various and diverse data sources, it is essential to present information in a way that allows for quick understanding and interpretation. This is particularly crucial in health care, where timely insights into a patient?s condition can be lifesaving. Holistic visualizations that integrate multiple data variables into a single visual representation can enhance rapid situational awareness and support informed decision-making. However, despite the existence of numerous guidelines for different types of visualizations, this study reveals that there are currently no specific guidelines or principles for designing holistic integrated information visualizations that enable quick processing and comprehensive understanding of multidimensional data in time-critical contexts. Addressing this gap is essential for enhancing decision-making in time-critical scenarios across various domains, particularly in health care. Objective: This study aims to establish a theoretical foundation supporting the argument that holistic integrated visualizations are a distinct type of visualization for time-critical contexts and identify applicable design principles and guidelines that can be used to design for such cases. Methods: We systematically searched the literature for peer-reviewed research on visualization strategies, guidelines, and taxonomies. The literature selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted across 6 databases: ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, Scopus, and Web of Science. The search was conducted up to August 2024 using the terms (?visualisations? OR ?visualizations?) AND (?guidelines? OR ?taxonomy? OR ?taxonomies?), with studies restricted to the English language. Results: Of 936 papers, 46 (4.9%) were included in the final review. In total, 48% (22/46) related to providing a holistic understanding and overview of multidimensional data; 28% (13/46) focused on integrated presentation, that is, integrating or combining multidimensional data into a single visual representation; and 35% (16/46) pertained to time and designing for rapid information processing. In total, 65% (30/46) of the papers presented general information visualization or visual communication guidelines and principles. No specific guidelines or principles were found that addressed all the characteristics of holistic, integrated visualizations in time-critical contexts. A summary of the key guidelines and principles from the 46 papers was extracted, collated, and categorized into 60 guidelines that could aid in designing holistic integrated visualizations. These were grouped according to different characteristics identified in the systematic review (eg, gestalt principles, reduction, organization, abstraction, and task complexity) and further condensed into 5 main proposed guidelines. Conclusions: Holistic integrated information visualizations in time-critical domains are a unique use case requiring a unique set of design guidelines. Our proposed 5 main guidelines, derived from existing design theories and guidelines, can serve as a starting point to enable both holistic and rapid processing of information, facilitating better-informed decisions in time-critical contexts. UR - https://www.jmir.org/2024/1/e58088 UR - http://dx.doi.org/10.2196/58088 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58088 ER - TY - JOUR AU - Bhavaraju, L. Vasudha AU - Panchanathan, Sarada AU - Willis, C. Brigham AU - Garcia-Filion, Pamela PY - 2024/11/6 TI - Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study JO - JMIR Med Educ SP - e53337 VL - 10 KW - clinical informatics KW - electronic health record KW - pediatric resident KW - COVID-19 KW - competence-based medical education KW - pediatric KW - children KW - SARS-CoV-2 KW - clinic KW - urban KW - diagnosis KW - health informatics KW - EHR KW - individualized learning plan N2 - Background: Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee?s experiences using existing tools. Objective: This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences. Methods: This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020?2022) were compared with class and graduated resident (classes of 2016?2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps. Results: Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals. Conclusions: This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education. UR - https://mededu.jmir.org/2024/1/e53337 UR - http://dx.doi.org/10.2196/53337 ID - info:doi/10.2196/53337 ER - TY - JOUR AU - Simblett, Sara AU - Dawe-Lane, Erin AU - Gilpin, Gina AU - Morris, Daniel AU - White, Katie AU - Erturk, Sinan AU - Devonshire, Julie AU - Lees, Simon AU - Zormpas, Spyridon AU - Polhemus, Ashley AU - Temesi, Gergely AU - Cummins, Nicholas AU - Hotopf, Matthew AU - Wykes, Til AU - PY - 2024/10/18 TI - Data Visualization Preferences in Remote Measurement Technology for Individuals Living With Depression, Epilepsy, and Multiple Sclerosis: Qualitative Study JO - J Med Internet Res SP - e43954 VL - 26 KW - mHealth KW - qualitative KW - technology KW - depression KW - epilepsy KW - multiple sclerosis KW - wearables KW - devices KW - smartphone apps KW - application KW - feedback KW - users KW - data KW - data visualization KW - mobile phone N2 - Background: Remote measurement technology (RMT) involves the use of wearable devices and smartphone apps to measure health outcomes in everyday life. RMT with feedback in the form of data visual representations can facilitate self-management of chronic health conditions, promote health care engagement, and present opportunities for intervention. Studies to date focus broadly on multiple dimensions of service users? design preferences and RMT user experiences (eg, health variables of perceived importance and perceived quality of medical advice provided) as opposed to data visualization preferences. Objective: This study aims to explore data visualization preferences and priorities in RMT, with individuals living with depression, those with epilepsy, and those with multiple sclerosis (MS). Methods: A triangulated qualitative study comparing and thematically synthesizing focus group discussions with user reviews of existing self-management apps and a systematic review of RMT data visualization preferences. A total of 45 people participated in 6 focus groups across the 3 health conditions (depression, n=17; epilepsy, n=11; and MS, n=17). Results: Thematic analysis validated a major theme around design preferences and recommendations and identified a further four minor themes: (1) data reporting, (2) impact of visualization, (3) moderators of visualization preferences, and (4) system-related factors and features. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. Easy to use and intuitive data visualization design was lauded by individuals with neurological and psychiatric conditions. Apps design needs to consider the unique requirements of service users. Overall, this study offers RMT developers a comprehensive outline of the data visualization preferences of individuals living with depression, epilepsy, and MS. UR - https://www.jmir.org/2024/1/e43954 UR - http://dx.doi.org/10.2196/43954 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/43954 ER - TY - JOUR AU - Sven?ek, Adrijana AU - Lorber, Mateja AU - Gosak, Lucija AU - Verbert, Katrien AU - Klemenc-Ketis, Zalika AU - Stiglic, Gregor PY - 2024/10/14 TI - The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review JO - JMIR Public Health Surveill SP - e60128 VL - 10 KW - cardiovascular disease prevention KW - risk factors KW - visual analytics KW - visualization KW - mobile phone KW - PRISMA N2 - Background: Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients? behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. Objective: This review aims to present the most-used visualization techniques to estimate CVD risk. Methods: In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. Results: We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing?related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). Conclusions: On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health?s effectiveness in improving CVD outcomes is limited. UR - https://publichealth.jmir.org/2024/1/e60128 UR - http://dx.doi.org/10.2196/60128 UR - http://www.ncbi.nlm.nih.gov/pubmed/39401079 ID - info:doi/10.2196/60128 ER - TY - JOUR AU - Wu, Zhiyue AU - Peng, Suyuan AU - Zhou, Liang PY - 2023/4/21 TI - Visualization of Traditional Chinese Medicine Formulas: Development and Usability Study JO - JMIR Form Res SP - e40805 VL - 7 KW - visualization KW - Chinese medicine formulas KW - interactive data analysis KW - traditional Chinese medicine KW - multifaceted data visualization KW - five elements N2 - Background: Traditional Chinese medicine (TCM) formulas are combinations of Chinese herbal medicines. Knowledge of classic medicine formulas is the basis of TCM diagnosis and treatment and is the core of TCM inheritance. The large number and flexibility of medicine formulas make memorization difficult, and understanding their composition rules is even more difficult. The multifaceted and multidimensional properties of herbal medicines are important for understanding the formula; however, these are usually separated from the formula information. Furthermore, these data are presented as text and cannot be analyzed jointly and interactively. Objective: We aimed to devise a visualization method for TCM formulas that shows the composition of medicine formulas and the multidimensional properties of herbal medicines involved and supports the comparison of medicine formulas. Methods: A TCM formula visualization method with multiple linked views is proposed and implemented as a web-based tool after close collaboration between visualization and TCM experts. The composition of medicine formulas is visualized in a formula view with a similarity-based layout supporting the comparison of compositing herbs; a shared herb view complements the formula view by showing all overlaps of pair-wise formulas; and a dimensionality-reduction plot of herbs enables the visualization of multidimensional herb properties. The usefulness of the tool was evaluated through a usability study with TCM experts. Results: Our method was applied to 2 typical categories of medicine formulas, namely tonic formulas and heat-clearing formulas, which contain 20 and 26 formulas composed of 58 and 73 herbal medicines, respectively. Each herbal medicine has a 23-dimensional characterizing attribute. In the usability study, TCM experts explored the 2 data sets with our web-based tool and quickly gained insight into formulas and herbs of interest, as well as the overall features of the formula groups that are difficult to identify with the traditional text-based method. Moreover, feedback from the experts indicated the usefulness of the proposed method. Conclusions: Our TCM formula visualization method is able to visualize and compare complex medicine formulas and the multidimensional attributes of herbal medicines using a web-based tool. TCM experts gained insights into 2 typical medicine formula categories using our method. Overall, the new method is a promising first step toward new TCM formula education and analysis methodologies. UR - https://formative.jmir.org/2023/1/e40805 UR - http://dx.doi.org/10.2196/40805 UR - http://www.ncbi.nlm.nih.gov/pubmed/37083631 ID - info:doi/10.2196/40805 ER - TY - JOUR AU - Costello, Jeremy AU - Kaur, Manpreet AU - Reformat, Z. Marek AU - Bolduc, V. Francois PY - 2023/4/17 TI - Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study JO - J Med Internet Res SP - e45268 VL - 25 KW - knowledge graph KW - natural language processing KW - neurodevelopmental disorders KW - autism spectrum disorder KW - intellectual disability KW - attention deficit hyperactivity disorder KW - named entity recognition KW - topic modeling KW - aggregation operator N2 - Background: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources. Objective: We aimed to develop a natural language?based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph. Methods: Using a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus. Results: We developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query. Conclusions: We developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence?based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine. UR - https://www.jmir.org/2023/1/e45268 UR - http://dx.doi.org/10.2196/45268 UR - http://www.ncbi.nlm.nih.gov/pubmed/37067865 ID - info:doi/10.2196/45268 ER - TY - JOUR AU - Scheer, Jan AU - Volkert, Alisa AU - Brich, Nicolas AU - Weinert, Lina AU - Santhanam, Nandhini AU - Krone, Michael AU - Ganslandt, Thomas AU - Boeker, Martin AU - Nagel, Till PY - 2022/10/24 TI - Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review JO - J Med Internet Res SP - e38041 VL - 24 IS - 10 KW - patient data KW - comparison KW - visualization systems KW - visual analytics KW - information visualization KW - cohorts KW - multiple patients KW - single patients KW - time-oriented data N2 - Background: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. Objective: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. Methods: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. Results: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. Conclusions: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space. UR - https://www.jmir.org/2022/10/e38041 UR - http://dx.doi.org/10.2196/38041 UR - http://www.ncbi.nlm.nih.gov/pubmed/36279164 ID - info:doi/10.2196/38041 ER - TY - JOUR AU - Gisladottir, Undina AU - Nakikj, Drashko AU - Jhunjhunwala, Rashi AU - Panton, Jasmine AU - Brat, Gabriel AU - Gehlenborg, Nils PY - 2022/4/29 TI - Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery JO - JMIR Hum Factors SP - e29118 VL - 9 IS - 2 KW - data visualization KW - surgical informed consent KW - shared decision-making KW - biomedical informatics N2 - Background: There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks. Objective: Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4). Methods: We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool. Results: In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators. Conclusions: We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization. UR - https://humanfactors.jmir.org/2022/2/e29118 UR - http://dx.doi.org/10.2196/29118 UR - http://www.ncbi.nlm.nih.gov/pubmed/35486432 ID - info:doi/10.2196/29118 ER - TY - JOUR AU - Polhemus, Ashley AU - Novak, Jan AU - Majid, Shazmin AU - Simblett, Sara AU - Morris, Daniel AU - Bruce, Stuart AU - Burke, Patrick AU - Dockendorf, F. Marissa AU - Temesi, Gergely AU - Wykes, Til PY - 2022/4/28 TI - Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives JO - JMIR Ment Health SP - e25249 VL - 9 IS - 4 KW - digital health KW - remote measurement technology KW - neurology KW - mental health KW - data visualization KW - user-centered design N2 - Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users? experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not ?one-size-fits-all,? and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health. UR - https://mental.jmir.org/2022/4/e25249 UR - http://dx.doi.org/10.2196/25249 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482368 ID - info:doi/10.2196/25249 ER - TY - JOUR AU - Davidson, Brittany AU - Ferrer Portillo, Mara Katiuska AU - Wac, Marceli AU - McWilliams, Chris AU - Bourdeaux, Christopher AU - Craddock, Ian PY - 2022/4/13 TI - Requirements for a Bespoke Intensive Care Unit Dashboard in Response to the COVID-19 Pandemic: Semistructured Interview Study JO - JMIR Hum Factors SP - e30523 VL - 9 IS - 2 KW - intensive care KW - critical care KW - COVID-19 KW - human-centered design KW - dashboard KW - eHealth KW - disease monitoring KW - monitoring KW - ICU KW - design KW - development KW - interview N2 - Background: Intensive care units (ICUs) around the world are in high demand due to patients with COVID-19 requiring hospitalization. As researchers at the University of Bristol, we were approached to develop a bespoke data visualization dashboard to assist two local ICUs during the pandemic that will centralize disparate data sources in the ICU to help reduce the cognitive load on busy ICU staff in the ever-evolving pandemic. Objective: The aim of this study was to conduct interviews with ICU staff in University Hospitals Bristol and Weston National Health Service Foundation Trust to elicit requirements for a bespoke dashboard to monitor the high volume of patients, particularly during the COVID-19 pandemic. Methods: We conducted six semistructured interviews with clinical staff to obtain an overview of their requirements for the dashboard and to ensure its ultimate suitability for end users. Interview questions aimed to understand the job roles undertaken in the ICU, potential uses of the dashboard, specific issues associated with managing COVID-19 patients, key data of interest, and any concerns about the introduction of a dashboard into the ICU. Results: From our interviews, we found the following design requirements: (1) a flexible dashboard, where the functionality can be updated quickly and effectively to respond to emerging information about the management of this new disease; (2) a mobile dashboard, which allows staff to move around on wards with a dashboard, thus potentially replacing paper forms to enable detailed and consistent data entry; (3) a customizable and intuitive dashboard, where individual users would be able to customize the appearance of the dashboard to suit their role; (4) real-time data and trend analysis via informative data visualizations that help busy ICU staff to understand a patient?s clinical trajectory; and (5) the ability to manage tasks and staff, tracking both staff and patient movements, handovers, and task monitoring to ensure the highest quality of care. Conclusions: The findings of this study confirm that digital solutions for ICU use would potentially reduce the cognitive load of ICU staff and reduce clinical errors at a time of notably high demand of intensive health care. UR - https://humanfactors.jmir.org/2022/2/e30523 UR - http://dx.doi.org/10.2196/30523 UR - http://www.ncbi.nlm.nih.gov/pubmed/35038301 ID - info:doi/10.2196/30523 ER - TY - JOUR AU - Chishtie, Jawad AU - Bielska, Anna Iwona AU - Barrera, Aldo AU - Marchand, Jean-Sebastien AU - Imran, Muhammad AU - Tirmizi, Ali Syed Farhan AU - Turcotte, A. Luke AU - Munce, Sarah AU - Shepherd, John AU - Senthinathan, Arrani AU - Cepoiu-Martin, Monica AU - Irvine, Michael AU - Babineau, Jessica AU - Abudiab, Sally AU - Bjelica, Marko AU - Collins, Christopher AU - Craven, Catharine B. AU - Guilcher, Sara AU - Jeji, Tara AU - Naraei, Parisa AU - Jaglal, Susan PY - 2022/2/18 TI - Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review JO - J Med Internet Res SP - e27534 VL - 24 IS - 2 KW - interactive visualization KW - data visualization KW - secondary health care data KW - public health informatics KW - population health KW - health services research N2 - Background: Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. Objective: The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications. Methods: We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations. Results: In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods. Conclusions: Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support. International Registered Report Identifier (IRRID): RR2-10.2196/14019 UR - https://www.jmir.org/2022/2/e27534 UR - http://dx.doi.org/10.2196/27534 UR - http://www.ncbi.nlm.nih.gov/pubmed/35179499 ID - info:doi/10.2196/27534 ER - TY - JOUR AU - Warin, Thierry PY - 2021/11/30 TI - Global Research on Coronaviruses: Metadata-Based Analysis for Public Health Policies JO - JMIR Med Inform SP - e31510 VL - 9 IS - 11 KW - COVID-19 KW - SARS-CoV-2 KW - natural language processing KW - coronavirus KW - unstructured data KW - data science KW - health 4.0 N2 - Background: Within the context of the COVID-19 pandemic, this paper suggests a data science strategy for analyzing global research on coronaviruses. The application of reproducible research principles founded on text-as-data information, open science, the dissemination of scientific data, and easy access to scientific production may aid public health in the fight against the virus. Objective: The primary goal of this paper was to use global research on coronaviruses to identify critical elements that can help inform public health policy decisions. We present a data science framework to assist policy makers in implementing cutting-edge data science techniques for the purpose of developing evidence-based public health policies. Methods: We used the EpiBibR (epidemiology-based bibliography for R) package to gain access to coronavirus research documents worldwide (N=121,231) and their associated metadata. To analyze these data, we first employed a theoretical framework to group the findings into three categories: conceptual, intellectual, and social. Second, we mapped the results of our analysis in these three dimensions using machine learning techniques (ie, natural language processing) and social network analysis. Results: Our findings, firstly, were methodological in nature. They demonstrated the potential for the proposed data science framework to be applied to public health policies. Additionally, our findings indicated that the United States and China were the primary contributors to global coronavirus research during the study period. They also demonstrated that India and Europe were significant contributors, albeit in a secondary position. University collaborations in this domain were strong between the United States, Canada, and the United Kingdom, confirming the country-level findings. Conclusions: Our findings argue for a data-driven approach to public health policy, particularly when efficient and relevant research is required. Text mining techniques can assist policy makers in calculating evidence-based indices and informing their decision-making process regarding specific actions necessary for effective health responses. UR - https://medinform.jmir.org/2021/11/e31510 UR - http://dx.doi.org/10.2196/31510 UR - http://www.ncbi.nlm.nih.gov/pubmed/34596570 ID - info:doi/10.2196/31510 ER - TY - JOUR AU - Gong, Jianxia AU - Sihag, Vikrant AU - Kong, Qingxia AU - Zhao, Lindu PY - 2021/11/1 TI - Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis JO - JMIR Med Inform SP - e31142 VL - 9 IS - 11 KW - knowledge evolution trends KW - research hotspots KW - personal health data KW - bibliometrics N2 - Background: The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective: The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods: We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results: The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions: Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including ?allocative value,? ?technology value,? and ?personalized value.? UR - https://medinform.jmir.org/2021/11/e31142 UR - http://dx.doi.org/10.2196/31142 UR - http://www.ncbi.nlm.nih.gov/pubmed/34723823 ID - info:doi/10.2196/31142 ER - TY - JOUR AU - Engelhard, M. Matthew AU - D'Arcy, Joshua AU - Oliver, A. Jason AU - Kozink, Rachel AU - McClernon, Joseph F. PY - 2021/11/1 TI - Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation JO - J Med Internet Res SP - e27875 VL - 23 IS - 11 KW - smoking KW - smoking cessation KW - machine learning KW - computer vision KW - digital health KW - eHealth KW - behavior KW - CNN KW - neural network KW - artificial intelligence KW - AI KW - images KW - environment KW - ecological momentary assessment KW - mobile health KW - mHealth KW - mobile phone N2 - Background: Viewing their habitual smoking environments increases smokers? craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers? daily environments. Objective: In this study, we aim to predict environment-associated risk from continuously acquired images of smokers? daily environments. We also aim to understand how model performance varies by location type, as reported by participants. Methods: Smokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network?based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants? daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app. Results: A total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ?=0.48; P=.001). Conclusions: Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions. UR - https://www.jmir.org/2021/11/e27875 UR - http://dx.doi.org/10.2196/27875 UR - http://www.ncbi.nlm.nih.gov/pubmed/34723819 ID - info:doi/10.2196/27875 ER - TY - JOUR AU - Ivankovi?, Damir AU - Barbazza, Erica AU - Bos, Véronique AU - Brito Fernandes, Óscar AU - Jamieson Gilmore, Kendall AU - Jansen, Tessa AU - Kara, Pinar AU - Larrain, Nicolas AU - Lu, Shan AU - Meza-Torres, Bernardo AU - Mulyanto, Joko AU - Poldrugovac, Mircha AU - Rotar, Alexandru AU - Wang, Sophie AU - Willmington, Claire AU - Yang, Yuanhang AU - Yelgezekova, Zhamin AU - Allin, Sara AU - Klazinga, Niek AU - Kringos, Dionne PY - 2021/2/24 TI - Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards JO - J Med Internet Res SP - e25682 VL - 23 IS - 2 KW - COVID-19 KW - pandemic KW - internet KW - performance measures KW - public reporting of health care data KW - public health KW - surveillance KW - health information management KW - dashboard KW - accessibility KW - online tool KW - communication KW - feature KW - expert N2 - Background: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. Objective: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (?why?), content and data (?what?), and analyses and displays (?how? they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. Methods: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. Results: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are ?close to home?; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. Conclusions: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified. UR - https://www.jmir.org/2021/2/e25682 UR - http://dx.doi.org/10.2196/25682 UR - http://www.ncbi.nlm.nih.gov/pubmed/33577467 ID - info:doi/10.2196/25682 ER - TY - JOUR AU - Roche, Raoul Tadzio AU - Said, Sadiq AU - Rössler, Julian AU - Gozdzik, Malgorzata AU - Meybohm, Patrick AU - Zacharowski, Kai AU - Spahn, R. Donat AU - Nöthiger, B. Christoph AU - Tscholl, W. David PY - 2020/12/4 TI - Physicians? Perceptions of a Situation Awareness?Oriented Visualization Technology for Viscoelastic Blood Coagulation Management (Visual Clot): Mixed Methods Study JO - JMIR Serious Games SP - e19036 VL - 8 IS - 4 KW - blood coagulation KW - hemostasis KW - blood coagulation test KW - point of care KW - rotational thromboelastometry KW - Visual Clot KW - decision making KW - survey and questionnaires KW - situation awareness KW - user-centered design KW - qualitative research KW - visualization KW - avatar N2 - Background: Viscoelastic tests enable a time-efficient analysis of coagulation properties. An important limitation of viscoelastic tests is the complicated presentation of their results in the form of abstract graphs with a multitude of numbers. We developed Visual Clot to simplify the interpretation of presented clotting information. This visualization technology applies user-centered design principles to create an animated model of a blood clot during the hemostatic cascade. In a previous simulation study, we found Visual Clot to double diagnostic accuracy, reduce time to decision making and perceived workload, and improve care providers? confidence. Objective: This study aimed to investigate the opinions of physicians on Visual Clot technology. It further aimed to assess its strengths, limitations, and clinical applicability as a support tool for coagulation management. Methods: This was a researcher-initiated, international, double-center, mixed qualitative-quantitative study that included the anesthesiologists and intensive care physicians who participated in the previous Visual Clot study. After the participants solved six coagulation scenarios using Visual Clot, we questioned them about the perceived pros and cons of this new tool. Employing qualitative research methods, we identified recurring answer patterns, and derived major topics and subthemes through inductive coding. Based on them, we defined six statements. The study participants later rated their agreement to these statements on five-point Likert scales in an online survey, which represented the quantitative part of this study. Results: A total of 60 physicians participated in the primary Visual Clot study. Among these, 36 gave an interview and 42 completed the online survey. In total, eight different major topics were derived from the interview field note responses. The three most common topics were ?positive design features? (29/36, 81%), ?facilitates decision making? (17/36, 47%), and ?quantification not made? (17/36, 47%). In the online survey, 93% (39/42) agreed to the statement that Visual Clot is intuitive and easy to learn. Moreover, 90% (38/42) of the participants agreed that they would like the standard result and Visual Clot displayed on the screen side by side. Furthermore, 86% (36/42) indicated that Visual Clot allows them to deal with complex coagulation situations more quickly. Conclusions: A group of anesthesia and intensive care physicians from two university hospitals in central Europe considered Visual Clot technology to be intuitive, easy to learn, and useful for decision making in situations of active bleeding. From the responses of these possible future users, Visual Clot appears to constitute an efficient and well-accepted way to streamline the decision-making process in viscoelastic test?based coagulation management. UR - http://games.jmir.org/2020/4/e19036/ UR - http://dx.doi.org/10.2196/19036 UR - http://www.ncbi.nlm.nih.gov/pubmed/33172834 ID - info:doi/10.2196/19036 ER - TY - JOUR AU - Chishtie, Ahmed Jawad AU - Marchand, Jean-Sebastien AU - Turcotte, A. Luke AU - Bielska, Anna Iwona AU - Babineau, Jessica AU - Cepoiu-Martin, Monica AU - Irvine, Michael AU - Munce, Sarah AU - Abudiab, Sally AU - Bjelica, Marko AU - Hossain, Saima AU - Imran, Muhammad AU - Jeji, Tara AU - Jaglal, Susan PY - 2020/12/3 TI - Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review JO - J Med Internet Res SP - e17892 VL - 22 IS - 12 KW - visual analytics KW - machine learning KW - data visualization KW - data mining KW - population health KW - health services research KW - mobile phone N2 - Background: Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. Objective: This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). Methods: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. Results: After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. Conclusions: With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID): RR2-10.2196/14019 UR - https://www.jmir.org/2020/12/e17892 UR - http://dx.doi.org/10.2196/17892 UR - http://www.ncbi.nlm.nih.gov/pubmed/33270029 ID - info:doi/10.2196/17892 ER - TY - JOUR AU - Cox, Steven AU - Ahalt, C. Stanley AU - Balhoff, James AU - Bizon, Chris AU - Fecho, Karamarie AU - Kebede, Yaphet AU - Morton, Kenneth AU - Tropsha, Alexander AU - Wang, Patrick AU - Xu, Hao PY - 2020/11/23 TI - Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface JO - JMIR Med Inform SP - e17964 VL - 8 IS - 11 KW - knowledge graphs KW - clinical data KW - biomedical data KW - federation KW - ontologies KW - semantic harmonization KW - visualization KW - application programming interface KW - translational science KW - clinical practice N2 - Background: Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. Objective: We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. Methods: We developed a biomedical query language and web application interphase?termed as Translator Query Language (TranQL)?to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. Results: We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as ?gene,? ?chemical substance,? and ?disease,? that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. Conclusions: TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice. UR - http://medinform.jmir.org/2020/11/e17964/ UR - http://dx.doi.org/10.2196/17964 UR - http://www.ncbi.nlm.nih.gov/pubmed/33226347 ID - info:doi/10.2196/17964 ER - TY - JOUR AU - Bhavnani, K. Suresh AU - Dang, Bryant AU - Penton, Rebekah AU - Visweswaran, Shyam AU - Bassler, E. Kevin AU - Chen, Tianlong AU - Raji, Mukaila AU - Divekar, Rohit AU - Zuhour, Raed AU - Karmarkar, Amol AU - Kuo, Yong-Fang AU - Ottenbacher, J. Kenneth PY - 2020/10/26 TI - How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach JO - JMIR Med Inform SP - e13567 VL - 8 IS - 10 KW - unplanned hospital readmission KW - visual analytics KW - bipartite networks KW - precision medicine N2 - Background: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. Results: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. Conclusions: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups. UR - https://medinform.jmir.org/2020/10/e13567 UR - http://dx.doi.org/10.2196/13567 UR - http://www.ncbi.nlm.nih.gov/pubmed/33103657 ID - info:doi/10.2196/13567 ER - TY - JOUR AU - Aida, Azusa AU - Svensson, Thomas AU - Svensson, Kishi Akiko AU - Urushiyama, Hirokazu AU - Okushin, Kazuya AU - Oguri, Gaku AU - Kubota, Naoto AU - Koike, Kazuhiko AU - Nangaku, Masaomi AU - Kadowaki, Takashi AU - Yamauchi, Toshimasa AU - Chung, Ung-Il PY - 2020/10/21 TI - Using mHealth to Provide Mobile App Users With Visualization of Health Checkup Data and Educational Videos on Lifestyle-Related Diseases: Methodological Framework for Content Development JO - JMIR Mhealth Uhealth SP - e20982 VL - 8 IS - 10 KW - apps KW - educational videos KW - health checkup KW - lifestyle-related disease KW - mHealth, prevention KW - telehealth KW - visualization N2 - Background: The number of people with lifestyle-related diseases continues to increase worldwide. Improving lifestyle behavior with health literacy may be the key to address lifestyle-related diseases. The delivery of educational videos using mobile health (mHealth) services can replace the conventional way of educating individuals, and visualization can replace the provision of health checkup data. Objective: This paper aimed to describe the development of educational content for MIRAMED, a mobile app aimed at improving users? lifestyle behaviors and health literacy for lifestyle-related diseases. Methods: All videos were based on a single unified framework to provide users with a consistent flow of information. The framework was later turned into a storyboard. The final video contents were created based on this storyboard and further discussions with leading experts and specialist physicians on effective communication with app users about lifestyle-related diseases. Results: The app uses visualization of personal health checkup data and educational videos on lifestyle-related diseases based on the current health guidelines, scientific evidence, and expert opinions of leading specialist physicians in the respective fields. A total of 8 videos were created for specific lifestyle-related diseases affecting 8 organs: (1) brain?cerebrovascular disorder, (2) eyes?diabetic retinopathy, (3) lungs?chronic obstructive pulmonary disease, (4) heart?ischemic heart disease, (5) liver?fatty liver, (6) kidneys?chronic kidney disease (diabetic kidney disease), (7) blood vessels?peripheral arterial disease, and (8) nerves?diabetic neuropathy. Conclusions: Providing enhanced mHealth education using novel digital technologies to visualize conventional health checkup data and lifestyle-related diseases is an innovative strategy. Future studies to evaluate the efficacy of the developed content are planned. UR - http://mhealth.jmir.org/2020/10/e20982/ UR - http://dx.doi.org/10.2196/20982 UR - http://www.ncbi.nlm.nih.gov/pubmed/33084586 ID - info:doi/10.2196/20982 ER - TY - JOUR AU - Senathirajah, Yalini AU - Kaufman, R. David AU - Cato, D. Kenrick AU - Borycki, M. Elizabeth AU - Fawcett, Allen Jaime AU - Kushniruk, W. Andre PY - 2020/10/21 TI - Characterizing and Visualizing Display and Task Fragmentation in the Electronic Health Record: Mixed Methods Design JO - JMIR Hum Factors SP - e18484 VL - 7 IS - 4 KW - electronic health record KW - electronic medical record KW - medical informatics KW - information technology KW - data visualization KW - user computer interface N2 - Background: The complexity of health care data and workflow presents challenges to the study of usability in electronic health records (EHRs). Display fragmentation refers to the distribution of relevant data across different screens or otherwise far apart, requiring complex navigation for the user?s workflow. Task and information fragmentation also contribute to cognitive burden. Objective: This study aims to define and analyze some of the main sources of fragmentation in EHR user interfaces (UIs); discuss relevant theoretical, historical, and practical considerations; and use granular microanalytic methods and visualization techniques to help us understand the nature of fragmentation and opportunities for EHR optimization or redesign. Methods: Sunburst visualizations capture the EHR navigation structure, showing levels and sublevels of the navigation tree, allowing calculation of a new measure, the Display Fragmentation Index. Time belt visualizations present the sequences of subtasks and allow calculation of proportion per instance, a measure that quantifies task fragmentation. These measures can be used separately or in conjunction to compare EHRs as well as tasks and subtasks in workflows and identify opportunities for reductions in steps and fragmentation. We present an example use of the methods for comparison of 2 different EHR interfaces (commercial and composable) in which subjects apprehend the same patient case. Results: Screen transitions were substantially reduced for the composable interface (from 43 to 14), whereas clicks (including scrolling) remained similar. Conclusions: These methods can aid in our understanding of UI needs under complex conditions and tasks to optimize EHR workflows and redesign. UR - https://humanfactors.jmir.org/2020/4/e18484 UR - http://dx.doi.org/10.2196/18484 UR - http://www.ncbi.nlm.nih.gov/pubmed/33084580 ID - info:doi/10.2196/18484 ER - TY - JOUR AU - Cecchetti, A. Alfred AU - Bhardwaj, Niharika AU - Murughiyan, Usha AU - Kothakapu, Gouthami AU - Sundaram, Uma PY - 2020/10/14 TI - Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach JO - JMIR Med Inform SP - e17962 VL - 8 IS - 10 KW - Appalachian region KW - medical informatics KW - health care disparities KW - electronic health records KW - data warehousing KW - data mining KW - data visualization KW - machine learning KW - data science N2 - Background: The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. Objective: This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. Methods: The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute?s Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. Results: The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. Conclusions: The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population. UR - http://medinform.jmir.org/2020/10/e17962/ UR - http://dx.doi.org/10.2196/17962 UR - http://www.ncbi.nlm.nih.gov/pubmed/33052114 ID - info:doi/10.2196/17962 ER - TY - JOUR AU - Ahmed, Kamran AU - Bukhari, Arish Muhammad AU - Mlanda, Tamayi AU - Kimenyi, Paul Jean AU - Wallace, Polly AU - Okot Lukoya, Charles AU - Hamblion, L. Esther AU - Impouma, Benido PY - 2020/10/14 TI - Novel Approach to Support Rapid Data Collection, Management, and Visualization During the COVID-19 Outbreak Response in the World Health Organization African Region: Development of a Data Summarization and Visualization Tool JO - JMIR Public Health Surveill SP - e20355 VL - 6 IS - 4 KW - COVID-19 KW - health information management KW - data collection KW - visualization KW - EWARS KW - WHO African region KW - Go.Data KW - outbreak KW - pandemic KW - health emergencies N2 - Background: The COVID-19 pandemic has created unprecedented challenges to the systematic and timely sharing of COVID-19 field data collection and management. The World Health Organization (WHO) is working with health partners on the rollout and implementation of a robust electronic field data collection platform. The delay in the deployment and rollout of this electronic platform in the WHO African Region, as a consequence of the application of large-scale public health and social measures including movement restrictions and geographical area quarantine, left a gap between data collection and management. This lead to the need to develop interim data management solutions to accurately monitor the evolution of the pandemic and support the deployment of appropriate public health interventions. Objective: The aim of this study is to review the design, development, and implementation of the COVID-19 Data Summarization and Visualization (DSV) tool as a rapidly deployable solution to fill this critical data collection gap as an interim solution. Methods: This paper reviews the processes undertaken to research and develop a tool to bridge the data collection gap between the onset of a COVID-19 outbreak and the start of data collection using a prioritized electronic platform such as Go.Data in the WHO African Region. Results: In anticipation of the implementation of a prioritized tool for field data collection, the DSV tool was deployed in 18 member states for COVID-19 outbreak data management. We highlight preliminary findings and lessons learned from the DSV tool deployment in the WHO African Region. Conclusions: We developed a rapidly deployable tool for COVID-19 data collection and visualization in the WHO African Region. The lessons drawn on this experience offer an opportunity to learn and apply these to improve future similar public health informatics initiatives in an outbreak or similar humanitarian setting, particularly in low- and middle-income countries. UR - http://publichealth.jmir.org/2020/4/e20355/ UR - http://dx.doi.org/10.2196/20355 UR - http://www.ncbi.nlm.nih.gov/pubmed/32997641 ID - info:doi/10.2196/20355 ER - TY - JOUR AU - Li, Rui AU - Yin, Changchang AU - Yang, Samuel AU - Qian, Buyue AU - Zhang, Ping PY - 2020/9/28 TI - Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach JO - J Med Internet Res SP - e20645 VL - 22 IS - 9 KW - electronic health records KW - interpretable deep learning KW - knowledge graph KW - visual analytics N2 - Background: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. Objective: The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. Methods: A domain-knowledge?guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. Results: We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. Conclusions: In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN?based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions. UR - http://www.jmir.org/2020/9/e20645/ UR - http://dx.doi.org/10.2196/20645 UR - http://www.ncbi.nlm.nih.gov/pubmed/32985996 ID - info:doi/10.2196/20645 ER - TY - JOUR AU - Kan, Wei-Chih AU - Kuo, Shu-Chun AU - Chien, Tsair-Wei AU - Lin, John Jui-Chung AU - Yeh, Yu-Tsen AU - Chou, Willy AU - Chou, Po-Hsin PY - 2020/7/27 TI - Therapeutic Duplication in Taiwan Hospitals for Patients With High Blood Pressure, Sugar, and Lipids: Evaluation With a Mobile Health Mapping Tool JO - JMIR Med Inform SP - e11627 VL - 8 IS - 7 KW - duplicate medication KW - mHealth KW - hypertension KW - high blood sugar KW - high blood lipid N2 - Background: Cardiovascular disease causes approximately half of all deaths in patients with type 2 diabetes. Duplicative prescriptions of medication in patients with high blood pressure (hypertension), high blood sugar (hyperglycemia), and high blood lipids (hyperlipidemia) have attracted substantial attention regarding the abuse of health care resources and to implement preventive measures for such abuse. Duplicative prescriptions may occur by patients receiving redundant medications for the same condition from two or more sources such as doctors, hospitals, and multiple providers, or as a result of the patient?s wandering among hospitals. Objective: We evaluated the degree of duplicative prescriptions in Taiwanese hospitals for outpatients with three types of medications (antihypertension, antihyperglycemia, and antihyperlipidemia), and then used an online dashboard based on mobile health (mHealth) on a map to determine whether the situation has improved in the recent 25 fiscal quarters. Methods: Data on duplicate prescription rates of drugs for the three conditions were downloaded from the website of Taiwan?s National Health Insurance Administration (TNHIA) from the third quarter of 2010 to the third quarter of 2016. Complete data on antihypertension, antihyperglycemia, and antihyperlipidemia prescriptions were obtained from 408, 414, and 359 hospitals, respectively. We used scale quality indicators to assess the attributes of the study data, created a dashboard that can be traced using mHealth, and selected the hospital type with the best performance regarding improvement on duplicate prescriptions for the three types of drugs using the weighted scores on an online dashboard. Kendall coefficient of concordance (W) was used to evaluate whether the performance rankings were unanimous. Results: The data quality was found to be acceptable and showed good reliability and construct validity. The online dashboard using mHealth on Google Maps allowed for easy and clear interpretation of duplicative prescriptions regarding hospital performance using multidisciplinary functionalities, and showed significant improvement in the reduction of duplicative prescriptions among all types of hospitals. Medical centers and regional hospitals showed better performance with improvement in the three types of duplicative prescriptions compared with the district hospitals. Kendall W was 0.78, indicating that the performance rankings were not unanimous (Chi square2=4.67, P=.10). Conclusions: This demonstration of a dashboard using mHealth on a map can inspire using the 42 other quality indicators of the TNHIA by hospitals in the future. UR - https://medinform.jmir.org/2020/7/e11627 UR - http://dx.doi.org/10.2196/11627 UR - http://www.ncbi.nlm.nih.gov/pubmed/32716306 ID - info:doi/10.2196/11627 ER - TY - JOUR AU - Schleimer, Erica AU - Pearce, Jennifer AU - Barnecut, Andrew AU - Rowles, William AU - Lizee, Antoine AU - Klein, Arno AU - Block, J. Valerie AU - Santaniello, Adam AU - Renschen, Adam AU - Gomez, Refujia AU - Keshavan, Anisha AU - Gelfand, M. Jeffrey AU - Henry, G. Roland AU - Hauser, L. Stephen AU - Bove, Riley PY - 2020/7/6 TI - A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development JO - J Med Internet Res SP - e15605 VL - 22 IS - 7 KW - human-centered design KW - mobile phone KW - personal health record KW - participatory medicine KW - visualization in eHealth KW - human factors N2 - Background: Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective: This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods: We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical?many times throughout the process, we went back to the drawing board. Results: This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions: After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions. UR - https://www.jmir.org/2020/7/e15605 UR - http://dx.doi.org/10.2196/15605 UR - http://www.ncbi.nlm.nih.gov/pubmed/32628124 ID - info:doi/10.2196/15605 ER - TY - JOUR AU - Ren, Jie AU - Raghupathi, Viju AU - Raghupathi, Wullianallur PY - 2020/7/3 TI - Understanding the Dimensions of Medical Crowdfunding: A Visual Analytics Approach JO - J Med Internet Res SP - e18813 VL - 22 IS - 7 KW - crowdfunding KW - medical crowdfunding KW - GoFundMe KW - fundraising KW - health care KW - health care affordability KW - patient KW - Facebook KW - fundraiser N2 - Background: Medical crowdfunding has emerged as a growing field for fundraising opportunities. Some environmental trends have driven the emergence of campaigns to raise funds for medical care. These trends include lack of medical insurance, economic backlash following the 2008 financial collapse, and shortcomings of health care regulations. Objective: Research regarding crowdfunding campaign use, reasons, and effects on the provision of medical care and individual relationships in health systems is limited. This study aimed to explore the nature and dimensions of the phenomenon of medical crowdfunding using a visual analytics approach and data crawled from the GoFundMe crowdfunding platform in 2019. We aimed to explore and identify the factors that contribute to a successful campaign. Methods: This data-driven study used a visual analytics approach. It focused on descriptive analytics to obtain a panoramic insight into medical projects funded through the GoFundMe crowdfunding platform. Results: This study highlighted the relevance of positioning the campaign for fundraising. In terms of motivating donors, it appears that people are typically more generous in contributing to campaigns for children rather than those for adults. The results emphasized the differing dynamics that a picture posted in the campaign brings to the potential for medical crowdfunding. In terms of donor?s motivation, the results show that a picture depicting the pediatric patient by himself or herself is the most effective. In addition, a picture depicting the current medical condition of the patient as severe is more effective than one depicting relative normalcy in the condition. This study also drew attention to the optimum length of the title. Finally, an interesting trend in the trajectory of donations is that the average amount of a donation decreases with an increase in the number of donors. This indicates that the first donors tend to be the most generous. Conclusions: This study examines the relationship between social media, the characteristics of a campaign, and the potential for fundraising. Its analysis of medical crowdfunding campaigns across the states offers a window into the status of the country?s health care affordability. This study shows the nurturing role that social media can play in the domain of medical crowdfunding. In addition, it discusses the drivers of a successful fundraising campaign with respect to the GoFundMe platform. UR - https://www.jmir.org/2020/7/e18813 UR - http://dx.doi.org/10.2196/18813 UR - http://www.ncbi.nlm.nih.gov/pubmed/32618573 ID - info:doi/10.2196/18813 ER - TY - JOUR AU - Faruqui, Akhter Syed Hasib AU - Alaeddini, Adel AU - Chang, C. Mike AU - Shirinkam, Sara AU - Jaramillo, Carlos AU - NajafiRad, Peyman AU - Wang, Jing AU - Pugh, Jo Mary PY - 2020/6/17 TI - Summarizing Complex Graphical Models of Multiple Chronic Conditions Using the Second Eigenvalue of Graph Laplacian: Algorithm Development and Validation JO - JMIR Med Inform SP - e16372 VL - 8 IS - 6 KW - graphical models KW - graph summarization KW - graph Laplacian KW - disease network KW - multiple chronic conditions N2 - Background: It is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations. Clinical data on MCC can now be represented using graphical models to study their interaction and identify the path toward the development of MCC. However, the current graphical models representing MCC are often complex and difficult to analyze. Therefore, it is necessary to develop improved methods for generating these models. Objective: This study aimed to summarize the complex graphical models of MCC interactions to improve comprehension and aid analysis. Methods: We examined the emergence of 5 chronic medical conditions (ie, traumatic brain injury [TBI], posttraumatic stress disorder [PTSD], depression [Depr], substance abuse [SuAb], and back pain [BaPa]) over 5 years among 257,633 veteran patients. We developed 3 algorithms that utilize the second eigenvalue of the graph Laplacian to summarize the complex graphical models of MCC by removing less significant edges. The first algorithm learns a sparse probabilistic graphical model of MCC interactions directly from the data. The second algorithm summarizes an existing probabilistic graphical model of MCC interactions when a supporting data set is available. The third algorithm, which is a variation of the second algorithm, summarizes the existing graphical model of MCC interactions with no supporting data. Finally, we examined the coappearance of the 100 most common terms in the literature of MCC to validate the performance of the proposed model. Results: The proposed summarization algorithms demonstrate considerable performance in extracting major connections among MCC without reducing the predictive accuracy of the resulting graphical models. For the model learned directly from the data, the area under the curve (AUC) performance for predicting TBI, PTSD, BaPa, SuAb, and Depr, respectively, during the next 4 years is as follows?year 2: 79.91%, 84.04%, 78.83%, 82.50%, and 81.47%; year 3: 76.23%, 80.61%, 73.51%, 79.84%, and 77.13%; year 4: 72.38%, 78.22%, 72.96%, 77.92%, and 72.65%; and year 5: 69.51%, 76.15%, 73.04%, 76.72%, and 69.99%, respectively. This demonstrates an overall 12.07% increase in the cumulative sum of AUC in comparison with the classic multilevel temporal Bayesian network. Conclusions: Using graph summarization can improve the interpretability and the predictive power of the complex graphical models of MCC. UR - http://medinform.jmir.org/2020/6/e16372/ UR - http://dx.doi.org/10.2196/16372 UR - http://www.ncbi.nlm.nih.gov/pubmed/32554376 ID - info:doi/10.2196/16372 ER - TY - JOUR AU - Wongvibulsin, Shannon AU - Wu, C. Katherine AU - Zeger, L. Scott PY - 2020/6/9 TI - Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation JO - JMIR Med Inform SP - e15791 VL - 8 IS - 6 KW - machine learning KW - interpretability KW - clinical translation KW - prediction models KW - visualization N2 - Background: Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms. Objective: The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML. Methods: To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death. Results: With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients. Conclusions: Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care. UR - https://medinform.jmir.org/2020/6/e15791 UR - http://dx.doi.org/10.2196/15791 UR - http://www.ncbi.nlm.nih.gov/pubmed/32515746 ID - info:doi/10.2196/15791 ER - TY - JOUR AU - Ko, Kyungmin AU - Lee, Won Chae AU - Nam, Sangmin AU - Ahn, Vogue Song AU - Bae, Ho Jung AU - Ban, Yong Chi AU - Yoo, Jongman AU - Park, Jungmin AU - Han, Wook Hyun PY - 2020/4/9 TI - Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis JO - J Med Internet Res SP - e15196 VL - 22 IS - 4 KW - cohort studies KW - data science KW - longitudinal studies KW - statistical data interpretation KW - medical informatics N2 - Background: In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations. Objective: This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors. Methods: We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties. Results: Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories. Conclusions: We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future. UR - https://www.jmir.org/2020/4/e15196 UR - http://dx.doi.org/10.2196/15196 UR - http://www.ncbi.nlm.nih.gov/pubmed/32271154 ID - info:doi/10.2196/15196 ER - TY - JOUR AU - Tscholl, Werner David AU - Rössler, Julian AU - Handschin, Lucas AU - Seifert, Burkhardt AU - Spahn, R. Donat AU - Nöthiger, B. Christoph PY - 2020/3/16 TI - The Mechanisms Responsible for Improved Information Transfer in Avatar-Based Patient Monitoring: Multicenter Comparative Eye-Tracking Study JO - J Med Internet Res SP - e15070 VL - 22 IS - 3 KW - computers KW - diagnosis KW - visual perception KW - awareness KW - patient safety N2 - Background: Patient monitoring is central to perioperative and intensive care patient safety. Current state-of-the-art monitors display vital signs as numbers and waveforms. Visual Patient technology creates an easy-to-interpret virtual patient avatar model that displays vital sign information as it would look in a real-life patient (eg, avatar changes skin color from healthy to cyanotic depending on oxygen saturation). In previous studies, anesthesia providers using Visual Patient perceived more vital signs during short glances than with conventional monitoring. Objective: We aimed to study the deeper mechanisms underlying information perception in conventional and avatar-based monitoring. Methods: In this prospective, multicenter study with a within-subject design, we showed 32 anesthesia providers four 3- and 10-second monitoring scenarios alternatingly as either routine conventional or avatar-based in random sequence. All participants observed the same scenarios with both technologies and reported the vital sign status after each scenario. Using eye-tracking, we evaluated which vital signs the participants had visually fixated (ie, could have potentially read and perceived) during a scenario. We compared the frequencies and durations of participants? visual fixations of vital signs between the two technologies. Results: Participants visually fixated more vital signs per scenario in avatar-based monitoring (median 10, IQR 9-11 versus median 6, IQR 4-8, P<.001; median of differences=3, 95% CI 3-4). In multivariable linear regression, monitoring technology (conventional versus avatar-based monitoring, difference=?3.3, P<.001) was an independent predictor of the number of visually fixated vital signs. The difference was less prominent in the longer (10-second) scenarios (difference=?1.5, P=.04). Study center, profession, gender, and scenario order did not influence the differences between methods. In all four scenarios, the participants visually fixated 9 of 11 vital signs statistically significantly longer using the avatar (all P<.001). Four critical vital signs (pulse rate, blood pressure, oxygen saturation, and respiratory rate) were visible almost the entire time of a scenario with the avatar; these were only visible for fractions of the observations with conventional monitoring. Visual fixation of a certain vital sign was associated with the correct perception of that vital sign in both technologies (avatar: phi coefficient=0.358; conventional monitoring: phi coefficient=0.515, both P<.001). Conclusions: This eye-tracking study uncovered that the way the avatar-based technology integrates the vital sign information into a virtual patient model enabled parallel perception of multiple vital signs and was responsible for the improved information transfer. For example, a single look at the avatar?s body can provide information about: pulse rate (pulsation frequency), blood pressure (pulsation intensity), oxygen saturation (skin color), neuromuscular relaxation (extremities limp or stiff), and body temperature (heatwaves or ice crystals). This study adds a new and higher level of empirical evidence about why avatar-based monitoring improves vital sign perception compared with conventional monitoring. UR - http://www.jmir.org/2020/3/e15070/ UR - http://dx.doi.org/10.2196/15070 UR - http://www.ncbi.nlm.nih.gov/pubmed/32175913 ID - info:doi/10.2196/15070 ER - TY - JOUR AU - Aupperle, Leora Robin AU - Paulus, P. Martin AU - Kuplicki, Rayus AU - Touthang, James AU - Victor, Teresa AU - Yeh, Hung-Wen AU - AU - Khalsa, S. Sahib PY - 2020/1/28 TI - Web-Based Graphic Representation of the Life Course of Mental Health: Cross-Sectional Study Across the Spectrum of Mood, Anxiety, Eating, and Substance Use Disorders JO - JMIR Ment Health SP - e16919 VL - 7 IS - 1 KW - mental health KW - life history KW - psychosocial factors KW - depression KW - anxiety KW - substance use disorders KW - eating disorders N2 - Background: Although patient history is essential for informing mental health assessment, diagnosis, and prognosis, there is a dearth of standardized instruments measuring time-dependent factors relevant to psychiatric disorders. Previous research has demonstrated the potential utility of graphical representations, termed life charts, for depicting the complexity of the course of mental illness. However, the implementation of these assessments is limited by the exclusive focus on specific mental illnesses (ie, bipolar disorder) and the lack of intuitive graphical interfaces for data collection and visualization. Objective: This study aimed to develop and test the utility of the Tulsa Life Chart (TLC) as a Web-based, structured approach for obtaining and graphically representing historical information on psychosocial and mental health events relevant across a spectrum of psychiatric disorders. Methods: The TLC interview was completed at baseline by 499 participants of the Tulsa 1000, a longitudinal study of individuals with depressive, anxiety, substance use, or eating disorders and healthy comparisons (HCs). All data were entered electronically, and a 1-page electronic and interactive graphical representation was developed using the Google Visualization Application Programming Interface. For 8 distinct life epochs (periods of approximately 5-10 years), the TLC assessed the following factors: school attendance, hobbies, jobs, social support, substance use, mental health treatment, family structure changes, negative and positive events, and epoch and event-related mood ratings. We used generalized linear mixed models (GLMMs) to evaluate trajectories of each domain over time and by sex, age, and diagnosis, using case examples and Web-based interactive graphs to visualize data. Results: GLMM analyses revealed main or interaction effects of epoch and diagnosis for all domains. Epoch by diagnosis interactions were identified for mood ratings and the number of negative-versus-positive events (all P values <.001), with all psychiatric groups reporting worse mood and greater negative-versus-positive events than HCs. These differences were most robust at different epochs, depending on diagnosis. There were also diagnosis and epoch main effects for substance use, mental health treatment received, social support, and hobbies (P<.001). User experience ratings (each on a 1-5 scale) revealed that participants found the TLC pleasant to complete (mean 3.07, SD 1.26) and useful for understanding their mental health (mean 3.07, SD 1.26), and that they were likely to recommend it to others (mean 3.42, SD 0.85). Conclusions: The TLC provides a structured, Web-based transdiagnostic assessment of psychosocial history relevant for the diagnosis and treatment of psychiatric disorders. Interactive, 1-page graphical representations of the TLC allow for the efficient communication of historical life information that would be useful for clinicians, patients, and family members. UR - http://mental.jmir.org/2020/1/e16919/ UR - http://dx.doi.org/10.2196/16919 UR - http://www.ncbi.nlm.nih.gov/pubmed/32012081 ID - info:doi/10.2196/16919 ER - TY - JOUR AU - ten Klooster, Iris AU - Noordzij, Leendert Matthijs AU - Kelders, Marion Saskia PY - 2020/1/21 TI - Exploring How Professionals Within Agile Health Care Informatics Perceive Visualizations of Log File Analyses: Observational Study Followed by a Focus Group Interview JO - JMIR Hum Factors SP - e14424 VL - 7 IS - 1 KW - log file analyses KW - user-centered design KW - agile KW - Markov Chains KW - health care systems N2 - Background: An increasing number of software companies work according to the agile software development method, which is difficult to integrate with user-centered design (UCD) practices. Log file analysis may provide opportunities for integrating UCD practices in the agile process. However, research within health care information technology mostly has a theoretical approach and is often focused on the researcher?s interpretation of log file analyses. Objective: We aimed to propose a systematic approach to log file analysis in this study and present this to developers to explore how they react and interpret this approach in the context of a real-world health care information system, in an attempt to answer the following question: How may log file analyses contribute to increasing the match between the health care system and its users, within the agile development method, according to agile team members? Methods: This study comprised 2 phases to answer the research question. In the first phase, log files were collected from a health care information system and subsequently analyzed (summarizing sequential patterns, heat mapping, and clustering). In the second phase, the results of these analyses are presented to agile professionals during a focus group interview. The interpretations of the agile professionals are analyzed by open axial coding. Results: Log file data of 17,924 user sessions and, in total, 176,678 activities were collected. We found that the Patient Timeline is mainly visited, with 23,707 (23,707/176,678; 13.42%) visits in total. The main unique user session occurred in 5.99% (1074/17,924) of all user sessions, and this comprised Insert Measurement Values for Patient and Patient Timeline, followed by the page Patient Settings and, finally, Patient Treatment Plan. In the heat map, we found that users often navigated to the pages Insert Measurement Values and Load Messages Collaborate. Finally, in the cluster analysis, we found 5 clusters, namely, the Information-seeking cluster, the Collaborative cluster, the Mixed cluster, the Administrative cluster, and the Patient-oriented cluster. We found that the interpretations of these results by agile professionals are related to stating hypotheses (n=34), comparing paths (n=31), benchmarking (n=22), and prioritizing (n=17). Conclusions: We found that analyzing log files provides agile professionals valuable insights into users? behavior. Therefore, we argue that log file analyses should be used within agile development to inform professionals about users? behavior. In this way, further UCD research can be informed by these results, making the methods less labor intensive. Moreover, we argue that these translations to an approach for further UCD research will be carried out by UCD specialists, as they are able to infer which goals the user had when going through these paths when looking at the log data. UR - https://humanfactors.jmir.org/2020/1/e14424 UR - http://dx.doi.org/10.2196/14424 UR - http://www.ncbi.nlm.nih.gov/pubmed/31961325 ID - info:doi/10.2196/14424 ER - TY - JOUR AU - Sleigh, Joanna AU - Schneider, Manuel AU - Amann, Julia AU - Vayena, Effy PY - 2020/1/14 TI - Visualizing an Ethics Framework: A Method to Create Interactive Knowledge Visualizations From Health Policy Documents JO - J Med Internet Res SP - e16249 VL - 22 IS - 1 KW - ethics framework KW - health data KW - health policy KW - knowledge visualization KW - systems map N2 - Background: Data have become an essential factor in driving health research and are key to the development of personalized and precision medicine. Primary and secondary use of personal data holds significant potential for research; however, it also introduces a new set of challenges around consent processes, privacy, and data sharing. Research institutions have issued ethical guidelines to address challenges and ensure responsible data processing and data sharing. However, ethical guidelines directed at researchers and medical professionals are often complex; require readers who are familiar with specific terminology; and can be hard to understand for people without sufficient background knowledge in legislation, research, and data processing practices. Objective: This study aimed to visually represent an ethics framework to make its content more accessible to its stakeholders. More generally, we wanted to explore the potential of visualizing policy documents to combat and prevent research misconduct by improving the capacity of actors in health research to handle data responsibly. Methods: We used a mixed methods approach based on knowledge visualization with 3 sequential steps: qualitative content analysis (open and axial coding, among others); visualizing the knowledge structure, which resulted from the previous step; and adding interactive functionality to access information using rapid prototyping. Results: Through our iterative methodology, we developed a tool that allows users to explore an ethics framework for data sharing through an interactive visualization. Our results represent an approach that can make policy documents easier to understand and, therefore, more applicable in practice. Conclusions: Meaningful communication and understanding each other remain a challenge in various areas of health care and medicine. We contribute to advancing communication practices through the introduction of knowledge visualization to bioethics to offer a novel way to tackle this relevant issue. UR - https://www.jmir.org/2020/1/e16249 UR - http://dx.doi.org/10.2196/16249 UR - http://www.ncbi.nlm.nih.gov/pubmed/31934866 ID - info:doi/10.2196/16249 ER - TY - JOUR AU - Chishtie, Ahmed Jawad AU - Babineau, Jessica AU - Bielska, Anna Iwona AU - Cepoiu-Martin, Monica AU - Irvine, Michael AU - Koval, Andriy AU - Marchand, Jean-Sebastien AU - Turcotte, Luke AU - Jeji, Tara AU - Jaglal, Susan PY - 2019/10/28 TI - Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review JO - JMIR Res Protoc SP - e14019 VL - 8 IS - 10 KW - data mining KW - data visualization KW - population health KW - public health KW - health services research KW - machine learning N2 - Background: Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR). Objective: The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care. Methods: We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers. Results: As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019. Conclusions: VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally. International Registered Report Identifier (IRRID): DERR1-10.2196/14019 UR - http://www.researchprotocols.org/2019/10/e14019/ UR - http://dx.doi.org/10.2196/14019 UR - http://www.ncbi.nlm.nih.gov/pubmed/31661081 ID - info:doi/10.2196/14019 ER - TY - JOUR AU - Chen, T. Annie AU - Swaminathan, Aarti AU - Kearns, R. William AU - Alberts, M. Nicole AU - Law, F. Emily AU - Palermo, M. Tonya PY - 2019/04/15 TI - Understanding User Experience: Exploring Participants? Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain JO - J Med Internet Res SP - e11756 VL - 21 IS - 4 KW - data visualization KW - natural language processing KW - chronic pain KW - cluster analysis KW - technology N2 - Background: Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs. Objective: In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents. Methods: We explored the main themes in coaches? and participants? messages using an automated textual analysis method, topic modeling. We then clustered participants? messages to identify subgroups of participants with similar engagement patterns. Results: First, we performed topic modeling on coaches? messages. The themes in coaches? messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants? message histories. Similar to the coaches? topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster. Conclusions: In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content. UR - http://www.jmir.org/2019/4/e11756/ UR - http://dx.doi.org/10.2196/11756 UR - http://www.ncbi.nlm.nih.gov/pubmed/30985288 ID - info:doi/10.2196/11756 ER - TY - JOUR AU - Concannon, David AU - Herbst, Kobus AU - Manley, Ed PY - 2019/04/04 TI - Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings JO - JMIR Form Res SP - e11342 VL - 3 IS - 2 KW - data visualization KW - data dashboards KW - health and demographic surveillance KW - sub-Saharan Africa KW - treatment as prevention KW - clinical trials KW - demographics KW - real-time KW - data literacy N2 - Background: Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information. Objectives: This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context. Methods: The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards. Results: The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use. Conclusions: Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context. UR - https://formative.jmir.org/2019/2/e11342/ UR - http://dx.doi.org/10.2196/11342 UR - http://www.ncbi.nlm.nih.gov/pubmed/30946016 ID - info:doi/10.2196/11342 ER - TY - JOUR AU - Shaffer, Anne Victoria AU - Wegier, Pete AU - Valentine, KD AU - Belden, L. Jeffery AU - Canfield, M. Shannon AU - Patil, J. Sonal AU - Popescu, Mihail AU - Steege, M. Linsey AU - Jain, Akshay AU - Koopman, J. Richelle PY - 2019/03/26 TI - Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data JO - J Med Internet Res SP - e11366 VL - 21 IS - 3 KW - data visualization KW - hypertension KW - hypertension control KW - patients? judgment KW - primary care N2 - Background: Uncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease. Objective: As part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data. Methods: In 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient?s BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients? judgments about hypertension control and the need for a medication change. Results: Participants? judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time?Study 2), and extreme values (ie, outliers?Study 3). Conclusions: Patients? judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information. UR - https://www.jmir.org/2019/3/e11366/ UR - http://dx.doi.org/10.2196/11366 UR - http://www.ncbi.nlm.nih.gov/pubmed/30912759 ID - info:doi/10.2196/11366 ER - TY - JOUR AU - Zanetti, Michele AU - Campi, Rita AU - Olivieri, Paola AU - Campiotti, Marta AU - Faggianelli, Alice AU - Bonati, Maurizio PY - 2019/03/22 TI - A Web-Based Form With Interactive Charts Used to Collect and Analyze Data on Home Births in Italy JO - J Med Internet Res SP - e10335 VL - 21 IS - 3 KW - Web-based form KW - home birth KW - interactive charts KW - internet KW - survey methods N2 - Background: The use of Web-based forms and data analysis can improve the collection and visualization of data in clinical research. In Italy, no register exists that collects clinical data concerning home births. Objective: The purpose of this study was (1) to develop a Web portal to collect, through a Web-based form, data on home births in Italy and (2) to provide those interested with a graphic visualization of the analyses and data collected. Methods: Following the World Health Organization?s guidelines, and adding questions based on scientific evidence, the case report form (CRF) on the online form was drafted by midwives of the National Association of Out-of-Hospital Birth Midwives. During an initial phase, a group of midwives (n=10) tested the CRF, leading to improvements and adding the necessary questions to achieve a CRF that would allow a more complete collection of data. After the test phase, the entire group of midwives (n=166) registered themselves on the system and began filling out birth questionnaires. In a subsequent phase, the administrators of the portal were able to view the completed forms in a graphic format through the use of interactive maps and graphs. Results: From 2014 to 2016, 58 midwives included 599 birth questionnaires via the Web portal; of these, 443 were home-based, 76% (321/424) of which were performed at home and 24% (103/424) at a midwifery unit. Most of the births assisted (79%, 335/424) were in northern Italy, and the average ages of the mother and father were 33.6 (SD 4.7) years and 37.0 (SD 5.6) years, respectively. Conclusions: We developed an innovative Web-based form that allows, for the first time in Italy, the collection of data on home births and births in the midwifery unit. Furthermore, the data collected are viewable online by the midwives through interactive maps and graphs that allow them to have a general and continuously updated view of the situation of out-of-hospital births performed by the National Association of Out-of-Hospital Birth Midwives. The future goal is to be able to expand this data collection to all out-of-hospital births throughout the national territory. With an increase in the number of enrolled midwives, it would be possible to use the portal as a Web-based form and also as a portal for sharing resources that would help midwives in their clinical practice. UR - https://www.jmir.org/2019/3/e10335/ UR - http://dx.doi.org/10.2196/10335 UR - http://www.ncbi.nlm.nih.gov/pubmed/30900993 ID - info:doi/10.2196/10335 ER - TY - JOUR AU - Velappan, Nileena AU - Daughton, Rae Ashlynn AU - Fairchild, Geoffrey AU - Rosenberger, Earl William AU - Generous, Nicholas AU - Chitanvis, Elizabeth Maneesha AU - Altherr, Michael Forest AU - Castro, A. Lauren AU - Priedhorsky, Reid AU - Abeyta, Luis Esteban AU - Naranjo, A. Leslie AU - Hollander, Dawn Attelia AU - Vuyisich, Grace AU - Lillo, Maria Antonietta AU - Cloyd, Kathryn Emily AU - Vaidya, Rajendra Ashvini AU - Deshpande, Alina PY - 2019/02/25 TI - Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks JO - JMIR Public Health Surveill SP - e12032 VL - 5 IS - 1 KW - epidemiology KW - infectious diseases KW - algorithm KW - public health informatics KW - web browser N2 - Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user?s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak. UR - http://publichealth.jmir.org/2019/1/e12032/ UR - http://dx.doi.org/10.2196/12032 UR - http://www.ncbi.nlm.nih.gov/pubmed/30801254 ID - info:doi/10.2196/12032 ER - TY - JOUR AU - Comulada, Scott W. AU - Swendeman, Dallas AU - Rezai, Roxana AU - Ramanathan, Nithya PY - 2018/11/05 TI - Time Series Visualizations of Mobile Phone-Based Daily Diary Reports of Stress, Physical Activity, and Diet Quality in Mostly Ethnic Minority Mothers: Feasibility Study JO - JMIR Formativ Res SP - e11062 VL - 2 IS - 2 KW - changepoint KW - diet quality KW - mobile phone KW - moving average KW - physical activity KW - stress KW - time series N2 - Background: Health behavior patterns reported through daily diary data are important to understand and intervene upon at the individual level in N-of-1 trials and related study designs. There is often interest in relationships between multiple outcomes, such as stress and health behavior. However, analyses often utilize regressions that evaluate aggregate effects across individuals, and standard analyses target single outcomes. Objective: This paper aims to illustrate how individuals? daily reports of stress and health behavior (time series) can be explored using visualization tools. Methods: Secondary analysis was conducted on 6 months of daily diary reports of stress and health behavior (physical activity and diet quality) from mostly ethnic minority mothers who pilot-tested a self-monitoring mobile health app. Time series with minimal missing data from 14 of the 44 mothers were analyzed. Correlations between stress and health behavior within each time series were reported as a preliminary step. Stress and health behavior time series patterns were visualized by plotting moving averages and time points where mean shifts in the data occurred (changepoints). Results: Median correlation was small and negative for associations of stress with physical activity (r=?.14) and diet quality (r=?.08). Moving averages and changepoints for stress and health behavior were aligned for some participants but not for others. A third subset of participants exhibited little variation in stress and health behavior reports. Conclusions: Median correlations in this study corroborate prior findings. In addition, time series visualizations highlighted variations in stress and health behavior across individuals and time points, which are difficult to capture through correlations and regression-based summary measures. UR - https://formative.jmir.org/2018/2/e11062/ UR - http://dx.doi.org/10.2196/11062 UR - http://www.ncbi.nlm.nih.gov/pubmed/30684407 ID - info:doi/10.2196/11062 ER - TY - JOUR AU - Backonja, Uba AU - Haynes, C. Sarah AU - Kim, K. Katherine PY - 2018/10/16 TI - Data Visualizations to Support Health Practitioners? Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study JO - JMIR Hum Factors SP - e11826 VL - 5 IS - 4 KW - cancer care facilities KW - informatics KW - patient-centered care KW - patient-generated health data KW - precision medicine KW - visualization N2 - Background: There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care. Objective: The aim of the study was to assess the health care practitioners? perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care. Methods: Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners? rankings of overview visualizations. Results: Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data. Conclusions: Visualizations guided by a framework and literature can support health care practitioners? understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care. UR - http://humanfactors.jmir.org/2018/4/e11826/ UR - http://dx.doi.org/10.2196/11826 UR - http://www.ncbi.nlm.nih.gov/pubmed/30327290 ID - info:doi/10.2196/11826 ER - TY - JOUR AU - Westendorf, Lauren AU - Shaer, Orit AU - Pollalis, Christina AU - Verish, Clarissa AU - Nov, Oded AU - Ball, Price Mad PY - 2018/9/24 TI - Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool JO - J Med Internet Res SP - e10297 VL - 20 IS - 9 KW - genomics KW - consumer health informatics N2 - Background: The growth in the availability of personal genomic data to nonexperts poses multiple challenges to human-computer interaction research; data are highly sensitive, complex, and have health implications for individuals and families. However, there has been little research on how nonexpert users explore their genomic data. Objective: We focus on how to support nonexperts in exploring and comparing their own personal genomic report with those of other people. We designed and evaluated CrossGenomics, a novel tool for comparing personal genetic reports, which enables exploration of shared and unshared genetic variants. Focusing on communicating comparative impact, rarity, and certainty, we evaluated alternative novel interactive prototypes. Methods: We conducted 3 user studies. The first focuses on assessing the usability and understandability of a prototype that facilitates the comparison of reports from 2 family members. Following a design iteration, we studied how various prototypes support the comparison of genetic reports of a 4-person family. Finally, we evaluated the needs of early adopters?people who share their genetic reports publicly for comparing their genetic reports with that of others. Results: In the first study, sunburst- and Venn-based comparisons of two genomes led to significantly higher domain comprehension, compared with the linear comparison and with the commonly used tabular format. However, results show gaps between objective and subjective comprehension, as sunburst users reported significantly lower perceived understanding and higher levels of confusion than the users of the tabular report. In the second study, users who were allowed to switch between the different comparison views presented higher comprehension levels, as well as more complex reasoning than users who were limited to a single comparison view. In the third study, 35% (17/49) reported learning something new from comparing their own data with another person?s data. Users indicated that filtering and toggling between comparison views were the most useful features. Conclusions: Our findings (1) highlight features and visualizations that show strengths in facilitating user comprehension of genomic data, (2) demonstrate the value of affording users the flexibility to examine the same report using multiple views, and (3) emphasize users? needs in comparison of genomic data. We conclude with design implications for engaging nonexperts with complex multidimensional genomic data. UR - http://www.jmir.org/2018/9/e10297/ UR - http://dx.doi.org/10.2196/10297 UR - http://www.ncbi.nlm.nih.gov/pubmed/30249582 ID - info:doi/10.2196/10297 ER - TY - JOUR AU - Jonassaint, R. Charles AU - Rao, Nema AU - Sciuto, Alex AU - Switzer, E. Galen AU - De Castro, Laura AU - Kato, J. Gregory AU - Jonassaint, C. Jude AU - Hammal, Zakia AU - Shah, Nirmish AU - Wasan, Ajay PY - 2018/08/03 TI - Abstract Animations for the Communication and Assessment of Pain in Adults: Cross-Sectional Feasibility Study JO - J Med Internet Res SP - e10056 VL - 20 IS - 8 KW - pain KW - pain measurement KW - chronic pain KW - medical informatics KW - mobile apps N2 - Background: Pain is the most common physical symptom requiring medical care, yet the current methods for assessing pain are sorely inadequate. Pain assessment tools can be either too simplistic or take too long to complete to be useful for point-of-care diagnosis and treatment. Objective: The aim was to develop and test Painimation, a novel tool that uses graphic visualizations and animations instead of words or numeric scales to assess pain quality, intensity, and course. This study examines the utility of abstract animations as a measure of pain. Methods: Painimation was evaluated in a chronic pain medicine clinic. Eligible patients were receiving treatment for pain and reported pain more days than not for at least 3 months. Using a tablet computer, participating patients completed the Painimation instrument, the McGill Pain Questionnaire (MPQ), and the PainDETECT questionnaire for neuropathic symptoms. Results: Participants (N=170) completed Painimation and indicated it was useful for describing their pain (mean 4.1, SE 0.1 out of 5 on a usefulness scale), and 130 of 162 participants (80.2%) agreed or strongly agreed that they would use Painimation to communicate with their providers. Animations selected corresponded with pain adjectives endorsed on the MPQ. Further, selection of the electrifying animation was associated with self-reported neuropathic pain (r=.16, P=.03), similar to the association between neuropathic pain and PainDETECT (r=.17, P=.03). Painimation was associated with PainDETECT (r=.35, P<.001). Conclusions: Using animations may be a faster and more patient-centered method for assessing pain and is not limited by age, literacy level, or language; however, more data are needed to assess the validity of this approach. To establish the validity of using abstract animations (?painimations?) for communicating and assessing pain, apps and other digital tools using painimations will need to be tested longitudinally across a larger pain population and also within specific, more homogenous pain conditions. UR - http://www.jmir.org/2018/8/e10056/ UR - http://dx.doi.org/10.2196/10056 UR - http://www.ncbi.nlm.nih.gov/pubmed/30076127 ID - info:doi/10.2196/10056 ER - TY - JOUR AU - Theis, Sabine AU - Rasche, Victor Peter Wilhelm AU - Bröhl, Christina AU - Wille, Matthias AU - Mertens, Alexander PY - 2018/07/09 TI - Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults JO - JMIR Med Inform SP - e39 VL - 6 IS - 3 KW - classification KW - data display KW - computer graphics KW - task performance and analysis KW - medicine KW - telemedicine KW - user/machine systems KW - human factors N2 - Background: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists. Objective: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization. Methods: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations. Results: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (?24=14.1, P=.002) and monitoring (?24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for ?monitoring? between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. Conclusions: We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine?chosen as the basis for the construction of present taxonomy?was confirmed. UR - http://medinform.jmir.org/2018/3/e39/ UR - http://dx.doi.org/10.2196/medinform.9394 UR - http://www.ncbi.nlm.nih.gov/pubmed/29986844 ID - info:doi/10.2196/medinform.9394 ER - TY - JOUR AU - Bian, Jiantao AU - Weir, Charlene AU - Unni, Prasad AU - Borbolla, Damian AU - Reese, Thomas AU - Wan, Jacob Yik-Ki AU - Del Fiol, Guilherme PY - 2018/06/25 TI - Interactive Visual Displays for Interpreting the Results of Clinical Trials: Formative Evaluation With Case Vignettes JO - J Med Internet Res SP - e10507 VL - 20 IS - 6 KW - clinical decision-making KW - clinician information needs KW - information display KW - information foraging theory KW - information seeking behavior N2 - Background: At the point of care, evidence from randomized controlled trials (RCTs) is underutilized in helping clinicians meet their information needs. Objective: To design interactive visual displays to help clinicians interpret and compare the results of relevant RCTs for the management of a specific patient, and to conduct a formative evaluation with physicians comparing interactive visual versus narrative displays. Methods: We followed a user-centered and iterative design process succeeded by development of information display prototypes as a Web-based application. We then used a within-subjects design with 20 participants (8 attendings and 12 residents) to evaluate the usability and problem-solving impact of the information displays. We compared subjects? perceptions of the interactive visual displays versus narrative abstracts. Results: The resulting interactive visual displays present RCT results side-by-side according to the Population, Intervention, Comparison, and Outcome (PICO) framework. Study participants completed 19 usability tasks in 3 to 11 seconds with a success rate of 78% to 100%. Participants favored the interactive visual displays over narrative abstracts according to perceived efficiency, effectiveness, effort, user experience and preference (all P values <.001). Conclusions: When interpreting and applying RCT findings to case vignettes, physicians preferred interactive graphical and PICO-framework-based information displays that enable direct comparison of the results from multiple RCTs compared to the traditional narrative and study-centered format. Future studies should investigate the use of interactive visual displays to support clinical decision making in care settings and their effect on clinician and patient outcomes. UR - http://www.jmir.org/2018/6/e10507/ UR - http://dx.doi.org/10.2196/10507 UR - http://www.ncbi.nlm.nih.gov/pubmed/29941416 ID - info:doi/10.2196/10507 ER - TY - JOUR AU - Barger, Diana AU - Leleux, Olivier AU - Conte, Valérie AU - Sapparrart, Vincent AU - Gapillout, Marie AU - Crespel, Isabelle AU - Erramouspe, Marie AU - Delveaux, Sandrine AU - Dabis, Francois AU - Bonnet, Fabrice PY - 2018/06/07 TI - Integrating Electronic Patient-Reported Outcome Measures into Routine HIV Care and the ANRS CO3 Aquitaine Cohort?s Data Capture and Visualization System (QuAliV): Protocol for a Formative Research Study JO - JMIR Res Protoc SP - e147 VL - 7 IS - 6 KW - patient-reported outcomes KW - HIV KW - patient-centered care KW - health-related quality of life KW - patient-generated health data N2 - Background: Effective antiretroviral therapy has greatly reduced HIV-related morbidity and mortality, dramatically changing the demographics of the population of people living with HIV. The majority of people living with HIV in France are well cared for insofar as their HIV infection is concerned but remain at risk for age-associated comorbidities. Their long-term, potentially complex, and growing care needs make the routine, longitudinal assessment of health-related quality of life and other patient-reported outcomes of relevance in the current treatment era. Objective: We aim to describe the development of a Web-based electronic patient-reported outcomes system for people living with HIV linked to the ANRS CO3 Aquitaine cohort?s data capture and visualization system (ARPEGE) and designed to facilitate the electronic collection of patient-reported data and ultimately promote better patient-physician communication and quality of care (both patient satisfaction and health outcomes). Methods: Participants who meet the eligibility criteria will be invited to engage with the Web-based electronic patient-reported outcomes system and provided with the information necessary to create a personal patient account. They will then be able to access the electronic patient-reported outcomes system and complete a set of standardized validated questionnaires covering health-related quality of life (World Health Organization's Quality of Life Instrument in HIV infection, named WHOQOL-HIV BREF) and other patient-reported outcomes. The information provided via questionnaires will ultimately be presented in a summary format for clinicians, together with the patient?s HIV care history. Results: The prototype of the Web-based electronic patient-reported outcome system will be finalized and the first 2 formative research phases of the study (prototyping and usability testing) will be conducted from December 2017 to May 2018. We describe the sequential processes planned to ensure that the proposed electronic patient-reported outcome system is ready for formal pilot testing, referred to herein as phases 1a and 1b. We also describe the planned pilot-testing designed to evaluate the acceptability and use of the system from the patient?s perspective (phase 2). Conclusions: As the underlying information technology solution, ARPEGE, has being developed in-house, should the feasibility study presented here yield promising results, the panel of services provided via the proposed portal could ultimately be expanded and used to experiment with health-promoting interventions in aging people living with HIV in hospital-based care or adapted for use in other patient populations. Trial Registration: ClinicalTrials.gov NCT03296202; https://clinicaltrials.gov/ct2/show/NCT03296202 (Archived by WebCite at http://www.webcitation.org/6zgOBArps) Registered Report Identifier: RR1-10.2196/9439 UR - http://www.researchprotocols.org/2018/6/e147/ UR - http://dx.doi.org/10.2196/resprot.9439 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/resprot.9439 ER - TY - JOUR AU - Khairat, Sherif Saif AU - Dukkipati, Aniesha AU - Lauria, Alico Heather AU - Bice, Thomas AU - Travers, Debbie AU - Carson, S. Shannon PY - 2018/05/31 TI - The Impact of Visualization Dashboards on Quality of Care and Clinician Satisfaction: Integrative Literature Review JO - JMIR Hum Factors SP - e22 VL - 5 IS - 2 KW - intensive care unit KW - visualization, Dashboard KW - cognitive load KW - information overload KW - usability KW - user interface design KW - health information technology KW - electronic health record N2 - Background: Intensive Care Units (ICUs) in the United States admit more than 5.7 million people each year. The ICU level of care helps people with life-threatening illness or injuries and involves close, constant attention by a team of specially-trained health care providers. Delay between condition onset and implementation of necessary interventions can dramatically impact the prognosis of patients with life-threatening diagnoses. Evidence supports a connection between information overload and medical errors. A tool that improves display and retrieval of key clinical information has great potential to benefit patient outcomes. The purpose of this review is to synthesize research on the use of visualization dashboards in health care. Objective: The purpose of conducting this literature review is to synthesize previous research on the use of dashboards visualizing electronic health record information for health care providers. A review of the existing literature on this subject can be used to identify gaps in prior research and to inform further research efforts on this topic. Ultimately, this evidence can be used to guide the development, testing, and implementation of a new solution to optimize the visualization of clinical information, reduce clinician cognitive overload, and improve patient outcomes. Methods: Articles were included if they addressed the development, testing, implementation, or use of a visualization dashboard solution in a health care setting. An initial search was conducted of literature on dashboards only in the intensive care unit setting, but there were not many articles found that met the inclusion criteria. A secondary follow-up search was conducted to broaden the results to any health care setting. The initial and follow-up searches returned a total of 17 articles that were analyzed for this literature review. Results: Visualization dashboard solutions decrease time spent on data gathering, difficulty of data gathering process, cognitive load, time to task completion, errors, and improve situation awareness, compliance with evidence-based safety guidelines, usability, and navigation. Conclusions: Researchers can build on the findings, strengths, and limitations of the work identified in this literature review to bolster development, testing, and implementation of novel visualization dashboard solutions. Due to the relatively few studies conducted in this area, there is plenty of room for researchers to test their solutions and add significantly to the field of knowledge on this subject. UR - http://humanfactors.jmir.org/2018/2/e22/ UR - http://dx.doi.org/10.2196/humanfactors.9328 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/humanfactors.9328 ER - TY - JOUR AU - Dancy-Scott, Nicole AU - Dutcher, A. Gale AU - Keselman, Alla AU - Hochstein, Colette AU - Copty, Christina AU - Ben-Senia, Diane AU - Rajan, Sampada AU - Asencio, Guadalupe Maria AU - Choi, Jongwon Jason PY - 2018/05/04 TI - Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years JO - JMIR Public Health Surveill SP - e50 VL - 4 IS - 2 KW - acquired immunodeficiency syndrome KW - data mining KW - history KW - HIV infections KW - terminology N2 - Background: The language encompassing health conditions can also influence behaviors that affect health outcomes. Few published quantitative studies have been conducted that evaluate HIV-related terminology changes over time. To expand this research, this study included an analysis of a dataset of abstracts presented at the International AIDS Conference (IAC) from 1989 to 2014. These abstracts reflect the global response to HIV over 25 years. Two powerful methodologies were used to evaluate the dataset: text mining to convert the unstructured information into structured data for analysis and data visualization to represent the data visually to assess trends. Objective: The purpose of this project was to evaluate the evolving use of HIV-related language in abstracts presented at the IAC from 1989 to 2014. Methods: Over 80,000 abstracts were obtained from the International AIDS Society and imported into a Microsoft SQL Server database for data processing and text mining analyses. A text mining module within the KNIME Analytics Platform, an open source software, was then used to mine the partially processed data to create a terminology corpus of key HIV terms. Subject matter experts grouped the terms into categories. Tableau, a data visualization software, was used to visualize the frequency metrics associated with the terms as line graphs and word clouds. The visualized dashboards were reviewed to discern changes in terminology use across IAC years. Results: The major findings identify trends in HIV-related terminology over 25 years. The term ?AIDS epidemic? was dominantly used from 1989 to 1991 and then declined in use. In contrast, use of the term ?HIV epidemic? increased through 2014. Beginning in the mid-1990s, the term ?treatment experienced? appeared with increasing frequency in the abstracts. Use of terms identifying individuals as ?carriers or victims? of HIV rarely appeared after 2008. Use of the terms ?HIV positive? and ?HIV infected? peaked in the early-1990s and then declined in use. The terms ?men who have sex with men? and ?MSM? were rarely used until 1994; subsequently, use of these terms increased through 2014. The term ?sex worker? steadily increased in frequency throughout conference years, whereas the term ?prostitute? decreased over time. Conclusions: The results of this study highlight changes in HIV terminology use over 25 years, including the addition, disappearance, and changing use of terms that reflect advances in HIV research and medical practice and destigmatization of the disease. Coupled with findings from related quantitative research, HIV-related terminology recommendations based on results of this study are included. Adoption of these recommendations will further efforts to use less stigmatizing language and facilitate effective communication between health professionals and people affected by HIV. UR - http://publichealth.jmir.org/2018/2/e50/ UR - http://dx.doi.org/10.2196/publichealth.8552 UR - http://www.ncbi.nlm.nih.gov/pubmed/29728344 ID - info:doi/10.2196/publichealth.8552 ER - TY - JOUR AU - Ben Ramadan, Ahmed Awatef AU - Jackson-Thompson, Jeannette AU - Schmaltz, Lee Chester PY - 2018/05/03 TI - Improving Visualization of Female Breast Cancer Survival Estimates: Analysis Using Interactive Mapping Reports JO - JMIR Public Health Surveill SP - e42 VL - 4 IS - 2 KW - survival KW - female breast cancer KW - Missouri KW - cancer registry N2 - Background: The Missouri Cancer Registry collects population-based cancer incidence data on Missouri residents diagnosed with reportable malignant neoplasms. The Missouri Cancer Registry wanted to produce data that would be of interest to lawmakers as well as public health officials at the legislative district level on breast cancer, the most common non-skin cancer among females. Objective: The aim was to measure and interactively visualize survival data of female breast cancer cases in the Missouri Cancer Registry. Methods: Female breast cancer data were linked to Missouri death records and the Social Security Death Index. Unlinked female breast cancer cases were crossmatched to the National Death Index. Female breast cancer cases in subcounty senate districts were geocoded using TIGER/Line shapefiles to identify their district. A database was created and analyzed in SEER*Stat. Senatorial district maps were created using US Census Bureau?s cartographic boundary files. The results were loaded with the cartographic data into InstantAtlas software to produce interactive mapping reports. Results: Female breast cancer survival profiles of 5-year cause-specific survival percentages and 95% confidence intervals, displayed in tables and interactive maps, were created for all 34 senatorial districts. The maps visualized survival data by age, race, stage, and grade at diagnosis for the period from 2004 through 2010. Conclusions: Linking cancer registry data to the National Death Index database improved accuracy of female breast cancer survival data in Missouri and this could positively impact cancer research and policy. The created survival mapping report could be very informative and usable by public health professionals, policy makers, at-risk women, and the public. UR - http://publichealth.jmir.org/2018/2/e42/ UR - http://dx.doi.org/10.2196/publichealth.8163 UR - http://www.ncbi.nlm.nih.gov/pubmed/29724710 ID - info:doi/10.2196/publichealth.8163 ER - TY - JOUR AU - Kaiser, Tim AU - Laireiter, Rupert Anton PY - 2017/07/20 TI - DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy JO - JMIR Med Inform SP - e20 VL - 5 IS - 3 KW - health information management KW - mental health KW - mental disorders KW - psychotherapeutic processes KW - algorithms N2 - Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy. UR - http://medinform.jmir.org/2017/3/e20/ UR - http://dx.doi.org/10.2196/medinform.6808 UR - http://www.ncbi.nlm.nih.gov/pubmed/28729233 ID - info:doi/10.2196/medinform.6808 ER - TY - JOUR AU - Cleary, Galkina Ekaterina AU - Patton, P. Allison AU - Wu, Hsin-Ching AU - Xie, Alan AU - Stubblefield, Joseph AU - Mass, William AU - Grinstein, Georges AU - Koch-Weser, Susan AU - Brugge, Doug AU - Wong, Carolyn PY - 2017/04/12 TI - Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy JO - JMIR Public Health Surveill SP - e16 VL - 3 IS - 2 KW - computer visualization KW - digital cartography KW - environmental health literacy KW - health communication KW - environmental health KW - computer-based education KW - air pollution KW - ultrafine particles KW - immigrant education N2 - Background: Digital maps are instrumental in conveying information about environmental hazards geographically. For laypersons, computer-based maps can serve as tools to promote environmental health literacy about invisible traffic-related air pollution and ultrafine particles. Concentrations of these pollutants are higher near major roadways and increasingly linked to adverse health effects. Interactive computer maps provide visualizations that can allow users to build mental models of the spatial distribution of ultrafine particles in a community and learn about the risk of exposure in a geographic context. Objective: The objective of this work was to develop a new software tool appropriate for educating members of the Boston Chinatown community (Boston, MA, USA) about the nature and potential health risks of traffic-related air pollution. The tool, the Interactive Map of Chinatown Traffic Pollution (?Air Pollution Map? hereafter), is a prototype that can be adapted for the purpose of educating community members across a range of socioeconomic contexts. Methods: We built the educational visualization tool on the open source Weave software platform. We designed the tool as the centerpiece of a multimodal and intergenerational educational intervention about the health risk of traffic-related air pollution. We used a previously published fine resolution (20 m) hourly land-use regression model of ultrafine particles as the algorithm for predicting pollution levels and applied it to one neighborhood, Boston Chinatown. In designing the map, we consulted community experts to help customize the user interface to communication styles prevalent in the target community. Results: The product is a map that displays ultrafine particulate concentrations averaged across census blocks using a color gradation from white to dark red. The interactive features allow users to explore and learn how changing meteorological conditions and traffic volume influence ultrafine particle concentrations. Users can also select from multiple map layers, such as a street map or satellite view. The map legends and labels are available in both Chinese and English, and are thus accessible to immigrants and residents with proficiency in either language. The map can be either Web or desktop based. Conclusions: The Air Pollution Map incorporates relevant language and landmarks to make complex scientific information about ultrafine particles accessible to members of the Boston Chinatown community. In future work, we will test the map in an educational intervention that features intergenerational colearning and the use of supplementary multimedia presentations. UR - http://publichealth.jmir.org/2017/2/e16/ UR - http://dx.doi.org/10.2196/publichealth.7492 UR - http://www.ncbi.nlm.nih.gov/pubmed/28404541 ID - info:doi/10.2196/publichealth.7492 ER - TY - JOUR AU - Demelo, Jonathan AU - Parsons, Paul AU - Sedig, Kamran PY - 2017/02/02 TI - Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE JO - JMIR Med Inform SP - e4 VL - 5 IS - 1 KW - MEDLINE KW - user-computer interface KW - information storage and retrieval KW - medical informatics KW - PubMed N2 - Background: Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks. Objective: Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem. Methods: We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches. Results: Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem. Conclusions: Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts. UR - http://medinform.jmir.org/2017/1/e4/ UR - http://dx.doi.org/10.2196/medinform.6918 UR - http://www.ncbi.nlm.nih.gov/pubmed/28153818 ID - info:doi/10.2196/medinform.6918 ER - TY - JOUR AU - Kamaleswaran, Rishikesan AU - McGregor, Carolyn PY - 2016/11/21 TI - A Review of Visual Representations of Physiologic Data JO - JMIR Med Inform SP - e31 VL - 4 IS - 4 KW - survey KW - human-centered computing KW - visualization application domains KW - information visualization KW - visualization systems and tools KW - visualization toolkits N2 - Background: Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising from information overload and diminished situational awareness becomes increasingly relevant. A systematic review was conducted to identify novel visual representations of physiologic data that address cognitive, analytic, and monitoring requirements in critical care environments. Objective: The aims of this review were to identify knowledge pertaining to (1) support for conveying event information via tri-event parameters; (2) identification of the use of visual variables across all physiologic representations; (3) aspects of effective design principles and methodology; (4) frequency of expert consultations; (5) support for user engagement and identifying heuristics for future developments. Methods: A review was completed of papers published as of August 2016. Titles were first collected and analyzed using an inclusion criteria. Abstracts resulting from the first pass were then analyzed to produce a final set of full papers. Each full paper was passed through a data extraction form eliciting data for comparative analysis. Results: In total, 39 full papers met all criteria and were selected for full review. Results revealed great diversity in visual representations of physiological data. Visual representations spanned 4 groups including tabular, graph-based, object-based, and metaphoric displays. The metaphoric display was the most popular (n=19), followed by waveform displays typical to the single-sensor-single-indicator paradigm (n=18), and finally object displays (n=9) that utilized spatiotemporal elements to highlight changes in physiologic status. Results obtained from experiments and evaluations suggest specifics related to the optimal use of visual variables, such as color, shape, size, and texture have not been fully understood. Relationships between outcomes and the users? involvement in the design process also require further investigation. A very limited subset of visual representations (n=3) support interactive functionality for basic analysis, while only one display allows the user to perform analysis including more than one patient. Conclusions: Results from the review suggest positive outcomes when visual representations extend beyond the typical waveform displays; however, there remain numerous challenges. In particular, the challenge of extensibility limits their applicability to certain subsets or locations, challenge of interoperability limits its expressiveness beyond physiologic data, and finally the challenge of instantaneity limits the extent of interactive user engagement. UR - http://medinform.jmir.org/2016/4/e31/ UR - http://dx.doi.org/10.2196/medinform.5186 UR - http://www.ncbi.nlm.nih.gov/pubmed/27872033 ID - info:doi/10.2196/medinform.5186 ER - TY - JOUR AU - Price, M. Margaux AU - Crumley-Branyon, J. Jessica AU - Leidheiser, R. William AU - Pak, Richard PY - 2016/06/01 TI - Effects of Information Visualization on Older Adults? Decision-Making Performance in a Medicare Plan Selection Task: A Comparative Usability Study JO - JMIR Hum Factors SP - e16 VL - 3 IS - 1 KW - Information visualization KW - aging KW - health-related websites KW - working memory N2 - Background: Technology gains have improved tools for evaluating complex tasks by providing environmental supports (ES) that increase ease of use and improve performance outcomes through the use of information visualizations (info-vis). Complex info-vis emphasize the need to understand individual differences in abilities of target users, the key cognitive abilities needed to execute a decision task, and the graphical elements that can serve as the most effective ES. Older adults may be one such target user group that would benefit from increased ES to mitigate specific declines in cognitive abilities. For example, choosing a prescription drug plan is a necessary and complex task that can impact quality of life if the wrong choice is made. The decision to enroll in one plan over another can involve comparing over 15 plans across many categories. Within this context, the large amount of complex information and reduced working memory capacity puts older adults? decision making at a disadvantage. An intentionally designed ES, such as an info-vis that reduces working memory demand, may assist older adults in making the most effective decision among many options. Objective: The objective of this study is to examine whether the use of an info-vis can lower working memory demands and positively affect complex decision-making performance of older adults in the context of choosing a Medicare prescription drug plan. Methods: Participants performed a computerized decision-making task in the context of finding the best health care plan. Data included quantitative decision-making performance indicators and surveys examining previous history with purchasing insurance. Participants used a colored info-vis ES or a table (no ES) to perform the decision task. Task difficulty was manipulated by increasing the number of selection criteria used to make an accurate decision. A repeated measures analysis was performed to examine differences between the two table designs. Results: Twenty-three older adults between the ages of 66 and 80 completed the study. There was a main effect for accuracy such that older adults made more accurate decisions in the color info-vis condition than the table condition. In the low difficulty condition, participants were more successful at choosing the correct answer when the question was about the gap coverage attribute in the info-vis condition. Participants also made significantly faster decisions in the info-vis condition than in the table condition. Conclusions: Reducing the working memory demand of the task through the use of an ES can improve decision accuracy, especially when selection criteria is only focused on a single attribute of the insurance plan. UR - http://humanfactors.jmir.org/2016/1/e16/ UR - http://dx.doi.org/10.2196/humanfactors.5106 UR - http://www.ncbi.nlm.nih.gov/pubmed/27251110 ID - info:doi/10.2196/humanfactors.5106 ER - TY - JOUR AU - Tilahun, Binyam AU - Kauppinen, Tomi AU - Keßler, Carsten AU - Fritz, Fleur PY - 2014/10/25 TI - Design and Development of a Linked Open Data-Based Health Information Representation and Visualization System: Potentials and Preliminary Evaluation JO - JMIR Med Inform SP - e31 VL - 2 IS - 2 KW - Linked Open Data KW - Semantic Web KW - ontology KW - health information systems KW - HIV KW - WHO KW - public health KW - public health informatics KW - visualization N2 - Background: Healthcare organizations around the world are challenged by pressures to reduce cost, improve coordination and outcome, and provide more with less. This requires effective planning and evidence-based practice by generating important information from available data. Thus, flexible and user-friendly ways to represent, query, and visualize health data becomes increasingly important. International organizations such as the World Health Organization (WHO) regularly publish vital data on priority health topics that can be utilized for public health policy and health service development. However, the data in most portals is displayed in either Excel or PDF formats, which makes information discovery and reuse difficult. Linked Open Data (LOD)?a new Semantic Web set of best practice of standards to publish and link heterogeneous data?can be applied to the representation and management of public level health data to alleviate such challenges. However, the technologies behind building LOD systems and their effectiveness for health data are yet to be assessed. Objective: The objective of this study is to evaluate whether Linked Data technologies are potential options for health information representation, visualization, and retrieval systems development and to identify the available tools and methodologies to build Linked Data-based health information systems. Methods: We used the Resource Description Framework (RDF) for data representation, Fuseki triple store for data storage, and Sgvizler for information visualization. Additionally, we integrated SPARQL query interface for interacting with the data. We primarily use the WHO health observatory dataset to test the system. All the data were represented using RDF and interlinked with other related datasets on the Web of Data using Silk?a link discovery framework for Web of Data. A preliminary usability assessment was conducted following the System Usability Scale (SUS) method. Results: We developed an LOD-based health information representation, querying, and visualization system by using Linked Data tools. We imported more than 20,000 HIV-related data elements on mortality, prevalence, incidence, and related variables, which are freely available from the WHO global health observatory database. Additionally, we automatically linked 5312 data elements from DBpedia, Bio2RDF, and LinkedCT using the Silk framework. The system users can retrieve and visualize health information according to their interests. For users who are not familiar with SPARQL queries, we integrated a Linked Data search engine interface to search and browse the data. We used the system to represent and store the data, facilitating flexible queries and different kinds of visualizations. The preliminary user evaluation score by public health data managers and users was 82 on the SUS usability measurement scale. The need to write queries in the interface was the main reported difficulty of LOD-based systems to the end user. Conclusions: The system introduced in this article shows that current LOD technologies are a promising alternative to represent heterogeneous health data in a flexible and reusable manner so that they can serve intelligent queries, and ultimately support decision-making. However, the development of advanced text-based search engines is necessary to increase its usability especially for nontechnical users. Further research with large datasets is recommended in the future to unfold the potential of Linked Data and Semantic Web for future health information systems development. UR - http://medinform.jmir.org/2014/2/e31/ UR - http://dx.doi.org/10.2196/medinform.3531 UR - http://www.ncbi.nlm.nih.gov/pubmed/25601195 ID - info:doi/10.2196/medinform.3531 ER - TY - JOUR AU - Haque, Waqar AU - Urquhart, Bonnie AU - Berg, Emery AU - Dhanoa, Ramandeep PY - 2014/08/06 TI - Using Business Intelligence to Analyze and Share Health System Infrastructure Data in a Rural Health Authority JO - JMIR Med Inform SP - e16 VL - 2 IS - 2 KW - business intelligence KW - health care systems KW - availability of health services KW - data visualization N2 - Background: Health care organizations gather large volumes of data, which has been traditionally stored in legacy formats making it difficult to analyze or use effectively. Though recent government-funded initiatives have improved the situation, the quality of most existing data is poor, suffers from inconsistencies, and lacks integrity. Generating reports from such data is generally not considered feasible due to extensive labor, lack of reliability, and time constraints. Advanced data analytics is one way of extracting useful information from such data. Objective: The intent of this study was to propose how Business Intelligence (BI) techniques can be applied to health system infrastructure data in order to make this information more accessible and comprehensible for a broader group of people. Methods: An integration process was developed to cleanse and integrate data from disparate sources into a data warehouse. An Online Analytical Processing (OLAP) cube was then built to allow slicing along multiple dimensions determined by various key performance indicators (KPIs), representing population and patient profiles, case mix groups, and healthy community indicators. The use of mapping tools, customized shape files, and embedded objects further augment the navigation. Finally, Web forms provide a mechanism for remote uploading of data and transparent processing of the cube. For privileged information, access controls were implemented. Results: Data visualization has eliminated tedious analysis through legacy reports and provided a mechanism for optimally aligning resources with needs. Stakeholders are able to visualize KPIs on a main dashboard, slice-and-dice data, generate ad hoc reports, and quickly find the desired information. In addition, comparison, availability, and service level reports can also be generated on demand. All reports can be drilled down for navigation at a finer granularity. Conclusions: We have demonstrated how BI techniques and tools can be used in the health care environment to make informed decisions with reference to resource allocation and enhancement of the quality of patient care. The data can be uploaded immediately upon collection, thus keeping reports current. The modular design can be expanded to add new datasets such as for smoking rates, teen pregnancies, human immunodeficiency virus (HIV) rates, immunization coverage, and vital statistical summaries. UR - http://medinform.jmir.org/2014/2/e16/ UR - http://dx.doi.org/10.2196/medinform.3590 UR - http://www.ncbi.nlm.nih.gov/pubmed/25599727 ID - info:doi/10.2196/medinform.3590 ER -