ArticleJournal of the American Medical Informatics Association : JAMIA2018
A visual analytics approach for pattern-recognition in patient-generated data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Remote symptom monitoring integrated into electronic health records: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
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- Opportunities to design better computer vison-assisted food diaries to support individuals and experts in dietary assessment: An observation and interview study with nutrition experts.PLOS digital health · 2024Article
- Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.Journal of biomedical informatics · 2023Article
- Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study.JMIR mHealth and uHealth · 2023Article
- Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making.Applied clinical informatics · 2022Article
- Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.JMIR diabetes · 2021Article
- Mobile-Based and Cloud-Based System for Self-management of People With Type 2 Diabetes: Development and Usability Evaluation.Journal of medical Internet research · 2021Article
- From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021Article
- Enabling personalized decision support with patient-generated data and attributable components.Journal of biomedical informatics · 2021Article
- Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application.JMIR medical informatics · 2020Article
- A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.International journal of medical informatics · 2020Article
- Early experiences with patient generated health data: health system and patient perspectives.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Visual Analytics Dashboard to Summarize Serial Anesthesia Records in Pediatric Radiation Treatment.Applied clinical informatics · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Objective: To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods: Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results: Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions: Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.