Med Tech Report

The power and promise of person-generated health data – part 1


The time shared during clinical encounters provides small peeks into patients’ lives that get documented as episodic snapshots in electronic health records. But there is little information about how patients are doing outside of the office. With increasing emphasis on filling out mandatory parts of the EHR, there is less time available for in-depth, in-office conversations and phone follow-ups.

Dr. Neil Skolnik, professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, and associate director, family medicine residency program, Abington (Pa.) Jefferson Health.

Dr. Neil Skolnik

At the same time, it has become clear that it is not just the medicines we prescribe that affect our patients’ lives. Their behaviors outside of the office – being physically active, eating well, getting a good night’s rest, and adhering to medications – also impact their health outcomes.

The explosion of technology and personal data in our increasingly connected world provides powerful new sources of health and behavior information that generate new understanding of patients’ lives in their everyday settings.

The ubiquity and remarkable technological progress of personal computing devices – including wearables, smartphones, and tablets – along with the multitude of sensor modalities embedded within these devices, has enabled us to establish a continuous connection with people who want to share information about their behavior and daily life.

Such rich, longitudinal information, known as person-generated health data (PGHD), can be searched for physiological and behavioral signatures that can be used in combination with traditional clinical information to predict, diagnose, and treat disease. It can also be used to understand the safety and effectiveness of medical interventions.

PGHD is defined as wellness and/or health-related data created, recorded, or gathered by individuals. It reflects events and interactions that occur during an person’s everyday life. Systematically gathering this information and organizing it to better understand patients’ approach to their health or their unique experience living with disease provides meaningful insights that complement the data traditionally collected as part of clinical trials or periodic office visits.

PGHD can produce a rich picture of a person’s health or symptom burden with disease. It allows the opportunity to measure the real human burden of a patient’s disease and how it changes over time, with an opportunity to detect changes in symptoms in real time.

PGHD can also enable participation in health research.

Dr. Luca Foschini, cofounder and chief data scientist, Evidation Health, Santa Barbara, Calif.

Dr. Luca Foschini

An example would be the work of Evidation Health in San Mateo, Calif. Evidation provides a platform to run research studies utilizing technology and systems to measure health in everyday life. Its app, Achievement, collects continuous behavior-related data from smartphones, wearables, connected devices, and apps. That provides opportunities for participants to join research studies that develop novel measures designed to quantify health outcomes in a way that more accurately reflects an individual’s day-to-day activities and experience. All data collected are at the direction of and with the permission of the individual.

“Achievers” are given points for taking health-related actions such as tracking steps or their sleep, which convert to cash that can be kept or donated to their favorite charities. Achievement’s 3.5 million diverse participants also receive offers to join research studies. This paradigm shift dramatically expands access to research to increase diversity, shortens the time to first data through rapid recruitment, and enhances retention rates by making it easier to engage. To date, more than 1 million users have chosen to participate in research studies. The technology is bringing new data and insights to health research; it supports important questions about quality of life, medical products’ real-world effectiveness, and the development of hyperpersonalized health care services.

Bray Patrick-Lake, director, strategic partnerships, Evidation Health, San Mateo, Calif.

Bray Patrick-Lake

This new type of data is transforming medical research by creating real-world studies of unprecedented size, such as the Apple Heart Study – a virtual study with more than 400,000 enrolled participants – which was designed to test the accuracy of Apple Watches in safely identifying atrial fibrillation. The FDA has cleared two features on the Apple Watch: the device’s ability to detect and notify the user of an irregular heart rhythm, and the ability to take a single-lead EKG feature that can provide a rhythm strip for a clinician to review.

The FDA clearance letters specify that the apps are “not intended to replace traditional methods of diagnosis or treatment.” They provide extra information, and that information might be helpful – but the apps won’t replace a doctor’s visit. It remains to be seen how these data will be used, but they have the potential to identify atrial fibrillation early, leading to treatment that may prevent devastating strokes.

Another example of home-generated health data is a tool that has obtained FDA clearance as a diagnostic device with insurance reimbursement: WatchPAT, a portable sleep apnea diagnostic device. WatchPAT is worn like a simple wristwatch, with no need for belts, wires, or nasal cannulas.

Over time, in-home tests like these that are of minimal inconvenience to the patient and reflect a real-world experience may eclipse traditional sleep studies that require patients to spend the night in a clinic while attached to wires and monitors.

Health data generated by connected populations will yield novel insights that may help us better predict, diagnose, and treat disease. These are examples of innovations that can extend clinicians’ abilities to remotely monitor or diagnose health conditions, and we can expect that more will continue to be integrated into the clinical and research settings in the near future.

In part 2 of this series, we will discuss novel digital measures and studies utilizing PGHD to impact population health.

Dr. Skolnik is professor of family and community medicine at Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, and associate director, family medicine residency program, Abington (Pa.) Jefferson Health. Dr. Foschini is cofounder and chief data scientist at Evidation Health in San Mateo, Calif. Bray Patrick-Lake is a patient thought leader and director, strategic partnerships, at Evidation Health.


Determining real-world data’s fitness for use and the role of reliability, September 2019. Duke-Margolis Center for Health Policy.

N Engl J Med. 2019 Nov 14;381(20):1909-17.

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