ISPOR 2020: Advancing Real-World Evidence to Incorporate Patient-Generated Health Data

On May 19th, I moderated a workshop with Ernesto Ramirez, PhD (Evidation Health), Elektra Papadopoulos, MD, MPH (FDA), and Kalahn Taylor-Clark, PhD, MPH (Sanofi) on the topic of “Advancing Real-World Evidence to Incorporate Patient-Generated Health Data.” With over 100 participants in attendance, the workshop explored how novel data from smartphones, wearables, apps, and sensors can be incorporated into real-world evidence plans and can help us understand more about individuals’ health in everyday life. Below is a brief summary of our discussion and a few of the key takeaways.

Person-generated health data, or PGHD, was central to this discussion. We cited the definition of person-generated health data (PGHD) created by Duke Margolis Center for Health Policy: “wellness and/or health-related data created, recorded, or gathered by individuals for themselves (or by family members or others who care for an individual).” The term “patient-generated health data” is used when referring to data from a person living with a disease or medical condition. It can be collected from sensors, devices, and surveys, among other methods. The increasing use of the term “patient-generated health data” (including in the FDA’s definition of RWD) signals that novel data are becoming more accepted and adopted within the healthcare space.

During her presentation, Dr. Papadopoulos, referred to digital health technology (DHT) as the main tool for enabling capture of person-generated health data. She spoke to the importance of DHT in creating opportunities, such as reducing barriers to participating in clinical trials, enhancing collection of endpoints that matter to patients in daily life, and enabling detection of intermittent or rare events.

PGHD as Key to Understanding Outcome Gaps

Dr. Taylor-Clark spoke to the outcome gap that is often seen between a product’s efficacy in clinical trials and its effectiveness in a real-world setting. This gap is caused by a combination of many factors (such as patient adherence, access to care, reimbursement, etc.) that are often left uncovered by traditional data sources. To understand this gap, her team at Sanofi utilizes behavioral science anchored in novel person-generated health data sources. PGHD unlocks an understanding about patient behaviors and factors contributing to the outcome gap, enabling the ability to design interventions and patient support programs that lead to improved outcomes.

PGHD in Research: Measuring What Matters to Patients in Daily Life

Dr. Ramirez provided a few case studies that exhibit the power of PGHD in research. The first example he provided was a recent study conducted by Evidation in collaboration with Eli Lilly and Apple: "Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams." The findings showed that symptomatic patients with mild cognitive impairment exhibited: slower typing, less activity regularity, fewer text messages, greater reliance on helper apps, and poorer survey compliance. This study shows that PGHD can be generated from consumer devices in hard to reach clinical populations, and that it has the potential to distinguish symptomatic patients from health controls outside of clinical encounters.

Another example provided by Dr. Ramirez shows how PGHD can be leveraged to better understand safety and clinical benefit: "Continuous Digital Assessment for Weight Loss Surgery Patients." Evidation deployed a survey to individuals who had recently had a surgical procedure, requesting each individual’s permission to access data from commercial wearable devices they may have been wearing around the time of the procedure. After analyzing the activity data (steps, heart rate, etc.) 12-weeks pre and post-operation, the results showed that weeks following weight loss operations were associated with fewer daily total steps, smaller proportions of the day spent walking, lower resting and 95th percentile heart rates, more total sleep time, and greater sleep efficiency. These findings show that PGHD has the potential to create measures of patients’ postoperative recovery that are more convenient, sensitive, scalable, and individualized as well as continuously collected outside of the clinic.

Adoption of PGHD in the Pharmaceutical Industry

At the end of the panel, I polled the workshop participants to learn more about their work with digital measures and patient-generated health data. 18% of respondents indicated that they are already incorporating digital measures into their RWE plans, and 53% indicated that they plan to incorporate digital measures in the future.  However, 31% of respondents said they are facing internal barriers around clinical and operational feasibility. These polling results demonstrate the increasing appetite for digital measures, and the work that remains to be done to ensure seamless integration of patient-generated health data into real-world evidence plans. As life science companies continue to adopt novel approaches to health measurement, direct connections to individuals facilitated by digital technologies that harness the power of PGHD will be crucial to understanding health outcomes outside of clinic walls.