{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:41:10Z","timestamp":1773549670604,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2018,6,13]],"date-time":"2018-06-13T00:00:00Z","timestamp":1528848000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["T15 LM007079"],"award-info":[{"award-number":["T15 LM007079"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000867","name":"Robert Wood Johnson Foundation","doi-asserted-by":"publisher","award":["73070"],"award-info":[{"award-number":["73070"]}],"id":[{"id":"10.13039\/100000867","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Disease","doi-asserted-by":"crossref","award":["1R01DK090372- 01A1"],"award-info":[{"award-number":["1R01DK090372- 01A1"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u2009=\u2009.17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p\u2009=\u2009.027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocy054","type":"journal-article","created":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T20:29:30Z","timestamp":1524083370000},"page":"1366-1374","source":"Crossref","is-referenced-by-count":30,"title":["A visual analytics approach for pattern-recognition in patient-generated data"],"prefix":"10.1093","volume":"25","author":[{"given":"Daniel J","family":"Feller","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}]},{"given":"Marissa","family":"Burgermaster","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}]},{"given":"Matthew E","family":"Levine","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia 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