Patient-Initiated Data: Our Experience with Enabling Patients to Initiate Incorporation of Heart Rate Data into the Electronic Health Record

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.

Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs

“…27 Two studies involved user-centered design of a hypertension dashboard, 17 with subsequent postimplementation comparison of the utilization of the dashboard versus the paper report during clinic visits. 28 One study focused on the development of a protocol for analyzing physiologic data capture 29 initiated by patients. Another was a randomized controlled trial that compared the This article is protected by copyright.…”

“…18 Another study used heart rates and step counts, not to manage a known condition, but to detect previously undiagnosed atrial fibrillation. 28 One study focused on Parkinson's disease, 15 collecting data from wrist and bed sensors, in addition to multiple PROs, with the goal of improving patient self-management at home.…”

“…23,32 According to one framework developed for how to address new PGHD, a key principle is considering how other EHR data can be leveraged as context to understand what the PGHD means. 29 However, there is a need to distinguish between PGHD and healthcare-generated data, 19,39 and some clinicians have concerns about the legal liability of viewing data outside their scope of practice. 40 3.…”

“…Line graph variations Line connecting data points, seven-day moving average, 17 smoothed line, 19,28 lines of each day's maximum and minimum value 24,25 Multiple variables on same plot with different y-axis labels 29 Different by source (home vs. clinic blood pressure) 28 Each day over same plot to show blood glucose trends by time of day 25 Visualization of summary statistics Box plots of day's range of values 27 Circle plots of percent time spent in states (e.g. activity types; 24 high, low, or target blood glucose 25 )…”

“…Bar plots of tallies of abnormal values each day 29 Stacked and clustered bar charts indicating activity intensity 23 Calendar views Four-week overview highlighting days with abnormal measurements 20 Shaded blocks by day of sleep quality (measured by duration and interruptions) 16 Statistical Interpretations Descriptive statistics: e.g., maximum, minimum, range, average, median, and standard deviation Percent of data within predetermined categories (e.g., within target blood glucose range)…”