Joshua M Pevnick1,2, Yaron Elad1, Lisa M Masson1, Richard V Riggs1,3, Ray G Duncan1. 1. Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United States. 2. Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States. 3. Department of Physical Medicine and Rehabilitation, Cedars-Sinai Medical Center, Los Angeles, California, United States.
Abstract
BACKGROUND: Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. OBJECTIVE: This study aimed to develop a protocol to address millions of unreviewed heart rates. METHODS: As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. RESULTS: We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. CONCLUSION: We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology. Thieme. All rights reserved.
BACKGROUND: Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. OBJECTIVE: This study aimed to develop a protocol to address millions of unreviewed heart rates. METHODS: As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. RESULTS: We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. CONCLUSION: We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology. Thieme. All rights reserved.
Authors: John N Mafi; Macda Gerard; Hannah Chimowitz; Melissa Anselmo; Tom Delbanco; Jan Walker Journal: Ann Intern Med Date: 2017-11-14 Impact factor: 25.391
Authors: Martha Gulati; Leslee J Shaw; Ronald A Thisted; Henry R Black; C Noel Bairey Merz; Morton F Arnsdorf Journal: Circulation Date: 2010-06-28 Impact factor: 29.690
Authors: Marco V Perez; Kenneth W Mahaffey; Haley Hedlin; John S Rumsfeld; Ariadna Garcia; Todd Ferris; Vidhya Balasubramanian; Andrea M Russo; Amol Rajmane; Lauren Cheung; Grace Hung; Justin Lee; Peter Kowey; Nisha Talati; Divya Nag; Santosh E Gummidipundi; Alexis Beatty; Mellanie True Hills; Sumbul Desai; Christopher B Granger; Manisha Desai; Mintu P Turakhia Journal: N Engl J Med Date: 2019-11-14 Impact factor: 176.079
Authors: Keith Feldman; Ray G Duncan; An Nguyen; Galen Cook-Wiens; Yaron Elad; Teryl Nuckols; Joshua M Pevnick Journal: J Am Med Inform Assoc Date: 2022-05-11 Impact factor: 7.942