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We are people first, patients second. Yet most data analyzed to create real world evidence by biopharma organizations has been heavily dependent on what we might term ‘traditional’ real-world data sources such as EHRs (electronic health records) and insurance claims that only portray the patient, not the person. These data are episodic: they only represent times a patient fulfilled a prescription, visited the doctor’s office, or interacted with the healthcare system – providing a partial, fragmented picture of the person’s overall health. We are left no better informed about their health prior to their interaction with the healthcare system, or what they experience afterwards. A far more valuable perspective emerges when biopharma companies leverage novel, person-generated health data (PGHD) to understand an individuals’ everyday life.

PGHD is health-related data that is created, recorded, or gathered by individuals or their caregivers. The sources of PGHD include wearable devices or phones, electronic surveys, apps, or any other interactions with technology that generate personal data that can be analyzed to characterize a person’s health status. These data can be captured passively and continuously, thus creating a more accurate and holistic picture of the individual that is not captured by EHRs or insurance claims data that document payment for clinical encounters and prescriptions.

PGHD enables valuable insights that help to better understand disease burden, patient identification, and real-world effectiveness.

 Without PGHD  With PGHD
Disease Burden
Develop a patient-centered understanding of disease impact on everyday function and quality of life
People with condition X have an average of one hospitalization and five missed days of work per year People with condition X experience pain and diminished activity for two days per week


For Y% of people, a hospitalization due to flare ups can result in sustained cognitive loss

Patient Identification
Leverage signs and symptoms data to identify populations who might receive the greatest benefit from a given treatment
Individuals with condition X on first line treatment report moderate residual pain Many with perceived symptom control have meaningful sleep disturbances due to nighttime disease activity


Individuals in regions with poorer air quality experience more exacerbations

Real-world effectiveness
Uncover the behavioral and physiological determinants of health that impact the effectiveness of treatments
Individuals taking medication X report better quality of life Individuals taking medication X demonstrate activities (shorter time to get out of bed and more overall activity) consistent with improvement in stiffness


Individuals taking medication X show increased social behavior as measured through reduced time at home

The FDA is recognizing the important role of these data by identifying PGHD as one of the main sources of real-world data and suggesting PGHD be leveraged to create real-world evidence for key analyses in randomized trials and observational studies. As biopharma organizations and regulatory bodies continue to adopt novel approaches to health measurement, PGHD, enabled by direct connections with individuals, will be critical to unlocking deeper insights in the development of new medical products, and the provision of more person-centric care. Utilizing these data can improve health outcomes, care delivery, and patient lives – and should therefore be a part of all evidence generation strategies.

PGHD has the ability to solve many of the key challenges encountered when using real world evidence today. Traditional real-world evidence data sources such as EHRs and insurance claims data are heavily slanted toward representing those people with access to the healthcare system. A gap exists when health research does not take into account the reality that there is vastly uneven access to healthcare in the United States. Consider a financially constrained, chronically ill patient. Because this person is financially constrained and unable to leverage the same healthcare services as other individuals, their data may not be included in a claims database or might be severely limited. Next, consider an MS patient living in a remote location. As a result of not being able to seek care at the standard frequency, their disease symptoms and characteristics of progression would be missing in EHR and insurance claims data sets. This might limit the patient’s eligibility to receive a second or third-line treatment. Regardless of the sophistication of the data science approach, basing research on this unrepresentative data risks excluding large sections of society from evidence that drives decision making across the healthcare industry. We must collect and analyze data that is more representative of all individuals.

It is possible to conduct direct-to-participant, decentralized studies that leverage robust, compliant platforms which ingest, store, integrate, and analyze multiple terabytes of data. Virtual sites may consist of millions of individuals, who can opt-in and provide informed consent via their own mobile phones or other devices. Immediate connection to cohorts of tens, or hundreds, or thousands of study participants enables rapid recruitment and decreases time to insight.

Executing on this type of research program requires both new capabilities and expertise. Such programs must be built in a privacy-safe, consent-based manner that is low burden for study participants. Data collection efforts can include daily or weekly app-based questionnaires, electronic patient-reported outcomes or at-home lab testing. Other permissioned data from connected medical devices and wearable devices might include activity, heart rate, sleep patterns, weight change, or contextual data like weather or pollution levels at a participant’s location that might negatively impact their health. Study designs can be cross-sectional or longitudinal, retrospective or prospective, collecting data from the right survey, device or sensor for the challenge at hand.

Better understanding disease and patient health depends on seeing people as complex individuals rather than just patients during interactions with the healthcare system. It is critical to use representative data as the basis for evidence generation, not just data from the segment of the U.S. population that has reliable access to healthcare services. By including person-generated health data, real world evidence can have an expanded impact on the understanding of patients and their disease.

To learn more about using person-generated health data to derive meaningful and representative patient insights, please visit Evidation’s website and sign up for the monthly newsletter to stay up to date on the latest in the world of digital, direct-to-patient research.