BackgroundPersonal fitness trackers (PFT) have substantial potential to improve healthcare.ObjectiveTo quantify and characterize early adopters who shared their PFT data with providers.MethodsWe used bivariate statistics and logistic regression to compare patients who shared any PFT data vs. patients who did not.ResultsA patient portal was used to invite 79,953 registered portal users to share their data. Of 66,105 users included in our analysis, 499 (0.8%) uploaded data during an initial 37-day study period. Bivariate and regression analysis showed that early adopters were more likely than non-adopters to be younger, male, white, health system employees, and to have higher BMIs. Neither comorbidities nor utilization predicted adoption.ConclusionOur results demonstrate that patients had little intrinsic desire to share PFT data with their providers, and suggest that patients most at risk for poor health outcomes are least likely to share PFT data. Marketing, incentives, and/or cultural change may be needed to induce such data-sharing.

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.

Objective Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data. Materials and Methods This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model. Results Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0–8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0–706.0) would have anticoagulation recommended even after a new AFib diagnosis. Discussion and Conclusion These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

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