Translating PGHD into Measures that Matter

December 2, 2021
Thought Leadership

December 2, 2021
Thought Leadership

Translating PGHD into Measures that Matter

December 2, 2021
Thought Leadership
Eve: Evidation's brand mark which is a yellow glowing orb

Elise Berliner, Global Vice President, Innovation of RWE, Kantar Health
•  Adaeze Enekwechi, Operating Partner, Welsh, Carson, Anderson & Stowe; Strategic Advisor, Evidation
•  Jennifer Goldsack, CEO, Digital Medicine Society (DiMe)
•  Bray Patrick-Lake, Sr. Director, Strategic Partnerships, Evidation (Moderator)
•  Marco Prunotto, Global Head of Technology and Translational Research, Roche


Incorporating person-generated health data (PGHD) into therapeutic development can give the industry greater understanding of patients’ lived experience and help develop measurements that matter to people. On October 13, 2021, Bray Patrick-Lake, Senior Director of Strategic Partnerships at Evidation, moderated a virtual panel discussion of industry experts who examined how PGHD can revolutionize evidence generation, from preclinical to post approval phases, to ensure we develop the measures and value frameworks that capture changes meaningful to patients.

Earlier this year, the US Food and Drug Administration (FDA) hosted a six-hour long webinar that discussed the importance of incorporating patient-centric approaches into regulatory decision-making and the critical role PGHD can play in moving the needle in developing safe and effective medical products that provide meaningful benefit to patients. This paradigm shift is the future of healthcare, as stakeholders agree that we need to measure and pay for what matters most to patients from trials to practice.

Person-generated health data (PGHD)

The Office of the National Coordinator for Health Information Technology (ONC) defines PGHD as “…health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern.”1 Such data can include health and treatment history, biometric data, symptoms, patient-reported outcomes, and lifestyle choices. The availability of digital wearable devices in particular has resulted in unprecedented opportunities for collection and analysis of physical, behavioral, and environmental information outside of clinical settings.

“Never before have we had such an opportunity to understand peoples’ lives outside the clinic and their experience with health and disease,” said Patrick-Lake.

Sources of PGHD


Unlike data generated in clinical settings and through encounters with healthcare providers, patients are primarily responsible for capturing or recording PGHD, and they decide whether and how to share or distribute these data to healthcare providers and researchers. Also unlike data from clinical visits, PGHD can:

•  enable remote, continuous monitoring at the individual level, particularly valuable for isolated patients or in times like the present COVID-19 pandemic, where traditional clinical trials cannot be conducted
•  allow direct connections to patients in real time
•  allow long-term connections to patients, to assess changes over time
•  provide for individual consent per use of the data
•  return value to patients through learning insights about their health
•  allow clinicians, researchers, and the pharma industry to reach groups of people who have not been reached before, reducing healthcare inequity
•  remove barriers to care, such as transportation difficulties
•  complement clinical data to provide a more complete picture of individual physiological, behavioral, and environmental factors

Despite the potential value of PGHD, a poll of the panel discussion audience discovered:

•  Only 56% were working with PGHD in an exploratory capacity,
•  41% were incorporating patient-reported outcomes into regulatory applications,
•  25% were incorporating novel digital endpoints in regulatory applications;|9% were submitting PGHD as evidence for reimbursement,
•  9% were submitting PGHD as evidence for reimbursement,
•  34% were supporting adoption of digital measures into clinical practice, and
•  16% were curious about PGHD but not ready to incorporate it into the drug development process.

Education and data harmonization are critical to the use of PGHD in product development and regulatory processes. Additionally, efforts to educate healthcare providers and optimize PGHD for their use should aid in incorporation of these data into clinical practice.

“We shouldn’t be thinking in terms of silos vis a vis these data,” said Jennifer Goldsack, CEO of the Digital Medicine Society (DiMe). “We should really be thinking about measures that transcend the divide between clinical practice and clinical research.”  

On the participant side, in order to encourage participation, there must be a return of value through sharing information and insights to facilitate continuity of engagement. The burdens of data collection also must be minimized, by means of passive continuous collection when possible and easily answered, secure surveys when not. Finally, value is added when participants feel as though they are part of a community.

Patient-centered drug development to practice paradigm

PGHD can play a role in understanding how patients feel, function, and survive. First, from the research and regulatory standpoints, PGHD can reduce the costs and time required for studies, along with risk.

Chart: regulatory, payers and practices

PGHD can offer unprecedented opportunities for rapid collection of novel safety and efficacy endpoints—such as deep phenotyping—and quality-of-life measures that matter most to patients. As an example, observing which patient-reported outcomes participants choose to track can provide insight into which symptoms patients are most interested in addressing. In addition, how individuals interact and engage with technologies has implications for the development and optimization of novel interventions as well as the management of symptoms and treatment of disease.

“The ability to follow the patients over a long time is something we were not able to do previously with these measurements,” said Mario Prunotto, Global Head of Technology and Translational Research at Roche, “… but it is very important to phenotype the patients over a long time and in a deeper way. Instead of measuring one 6-minute walk test, for example, we can integrate how this ability changes over time. A second aspect of these data is that patient-reported outcomes can be captured digitally in real time, without recall bias. ‘You measure it when you feel it,’ in other words.”

For payers, real-world data can aid in assessment of effectiveness and financial value through health and economic outcomes studies and share-of-cost comparisons. And their uptake into clinical practice guidelines will result in disease management and monitoring that better reflects patient-centered outcomes.

For regulators, PGHD can provide real-world data, capture patient perspectives, enable precision medicine, include people typically left out of clinical trials, and provide novel clinical opportunities, including in real time. As a result, regulatory bodies are increasingly recognizing the importance of these data. As one example, the US Food and Drug Administration (FDA) released in 2019 a draft guidance for industry on the use of quality-of-life endpoints in development of drugs to treat heart failure. It states specifically “…that an effect on symptoms or physical function, without a favorable effect on survival or risk of hospitalization, can be a basis for approving drugs to treat heart failure.”4 This represents a paradigm shift in the regulatory process, toward endpoints of greater value to patients.

“There has been a sea change,” said Goldsack. “Had we asked people 2 or 3 years ago…we would have had no idea what regulators think. … Now, they are very open to accepting this kind of data and they want to be part of the conversation.”

As medical stakeholders increasingly embrace PGHD, it will become more critical to incorporate PGHD methods into clinical research and development programs, to adequately incorporate more passive and continuous ways to frame patient status changes along the disease trajectory and to monitor therapeutic impact.

Accordingly, groups such as the Core Outcome Measures in Effectiveness Trials (COMET) initiative5 and International Consortium For Health Outcomes Measurement, Inc. (ICHOM) have created standardized sets of core patient-centered outcome measures for conditions such as breast cancer, chronic kidney disease, dementia, diabetes, inflammatory arthritis, and Parkinson’s disease, among others. As an example, the ICHOM heart failure set of endpoints includes not only the traditional “hard” endpoint of mortality but also those reflecting the burden of care for the patient (hospitalizations, complications, side effects) and the burden of disease for the patient (symptoms, activities of daily living, independence, and psychosocial health).6

“We have to get away from ‘an army of one,’” said Elise Berliner, Global Vice President of Innovation of Real-World Evidence at Kantar Health. “We have to move into the idea of … harmonizing outcome measures.”  

In a complementary effort, DiMe launched in October 2019 a crowdsourced Library of Digital Endpoints focused on industry-sponsored studies of new medical products or new applications of existing medical products.7 These defined endpoints offer significant opportunities to improve what is known about patients’ biological processes and responses, as well as how they feel, function, and survive in both clinical trials and routine clinical care.

As of September 2021, 69 sponsors have collected 225 distinct digital outcomes:

•  66% were from Phase 1–3 trials of drugs, devices, and biologics
•  94% were primary or secondary study endpoints
•  60% were for drug trials, 25% were for device trials, and 10% were for biologics

Despite the development of such standardized endpoints, and despite regulatory encouragement, uptake of patient-centered endpoints has been slow and inconsistent. In one technology assessment on a retinal prosthesis in the Medicare population, the authors found 74 outcome measures reported in 11 studies.8 Only 3 of the outcome measures had been reported by 3 or more of the studies, only 4 outcome measures had any evidence of validity and reliability, and only 1 study had measured quality of life directly (and then, in a population different from the one in which it had been validated). Conversely, several of the outcome measures developed specifically for patients with ultra-low vision were not used in any of the studies.

“This is what’s typically found in the literature,” said Berliner, “…a total example of how we as a community really have to think about these issues.”

Given the newness of PGHD, organizations are making great efforts to determine the best ways to mobilize ingestion/action PGHD and initiate relevant research. However, there is a clear need for education around adoption and standardization of these clinical measures to reflect which endpoints matter most to patients.

Developing valid PGHD measures

The first step to developing valid PGHD outcome measures is to conduct qualitative research on what measures are most important to the relevant patients, from symptoms to side effects to functioning. From this point, there are two main aspects to developing effective PGHD outcome measures: the methods for developing the endpoints and the populations in which they are being tested.

Any resulting measure should yield reproducible, internally consistent results of what it purports to assess. It also should be able to detect changes over time that matter to patients. The population being tested should be diverse and inclusive, and clinical and social determinants of health should be reported. Researchers must also be mindful of selection bias based on patient health literacy and access to technology, and take steps to reduce any preferential enrollment.

“Even if you give patients access to technology…,” noted Berliner, “if you look at the maps of connectivity … there are still parts of the U.S. that still do not have cell(ular) reception. There are places that do not have access to broadband Internet.”

An open question remains how to validate PGHD outcome measures that matter to patients in the context of rapidly evolving technology. But above all of these considerations, input from patients is critical.

“The risk of tech timelines outpacing clinical timelines is largely ameliorated if we actually focus on measuring things that matter,” said Goldsack. In other words, the possible rapid evolution of a tool or technology would be less relevant if the gold standard remains the measure that is important to the patient.

Image: Patient Considerations that should drive digital measure selection and development

Use of PGHD in clinical care and for quality reporting

The use of PGHD in clinical care is promising. One analysis from the Agency for Healthcare Research and Quality (AHRQ) reviewed the impact of automated-entry PGHD devices and mobile apps on the prevention or treatment of 11 chronic conditions.9 The outcomes included mortality, quality of life, and symptom improvement, among others. For 3 of the 11 chronic conditions studied—coronary artery disease, heart failure, and asthma—they found a possible positive effect of these technologies on health outcomes. Two other conditions showed possible or consistent evidence of a positive effect of PGHD interventions on surrogate endpoints: blood pressure and time to detection of cardiac arrhythmia. Effects were unclear for obesity, hypertension, and arrhythmias, primarily because of a lack of reporting of health outcomes and insufficient statistical power for analysis. The authors noted that most studies on PGHD technologies still focus on non-health-related outcomes. Future trials of PGHD interventions should focus on measurement of health outcomes and be designed to isolate the effect of the PGHD aspect from other components in multicomponent interventions.

The Centers for Medicare and Medicaid Services (CMS) are optimistic about the future of PGHD for quality reporting. They have indicated that digital data used for quality measurement could expand beyond those captured in traditional clinical settings, administrative insurance claims, and electronic medical records (EHRs). In a recent proposed rule for patients with end-stage kidney disease,10 they stated:

“Many important data sources are not currently captured digitally, such as survey and PGHD. We intend to work to innovate and broaden the digital data used across the quality measurement enterprise beyond the clinical EHR and administrative claims.”

They also noted that agreed-upon standards for PGHD will be important for interoperability and quality measurement, as will implementation guides and device data  validation. These efforts will also result in a higher likelihood of reimbursement.

However, “Regulators have to approach a lot of this with a great deal of scrutiny,” said Adaeze Enekwechi, Operating Partner at Welsh, Carson, Anderson & Stowe, “because there is no shortage of people who will bring forth ‘skinny’ evidence with no research… To the extent that the industry wants to be responsive to CMS’ goal of incorporating more real-world evidence, … it makes a lot of sense … for scientists in this space, for analysts, for data scientists to help articulate … what (PGHD) makes sense to add..”

Role of PGHD in controversial regulatory decisions

On June 7, 2021, the FDA granted accelerated approval to aducanumab,11 a drug developed for treatment of Alzheimer’s disease, despite its advisory board recommending rejection of the drug.12 The drug was approved on the basis of a surrogate endpoint—the reduction in amyloid beta plaque in the brain—in contrast to previous trials, which assessed changes in cognitive function or other disease symptoms. The use of this surrogate for approval has led to insurance company refusals for coverage, and hospital refusals to administer the drug, based on the lack of definitive evidence of benefit.13

PGHD might be used to complement evidence packages for diseases such as Alzheimer’s that lack sensitive, patient-centered measures. Key opportunities include better (and earlier) detection of disease, to prompt more timely diagnosis for therapy initiation and impact; the ability to better understand and demonstrate therapy impact on cognition and function; value assessments to support therapy decisions and reimbursement; and label expansions as products are used in practice, via accumulating observational data.

“It’s important to reach the right individuals and patients, develop more sensitive measures, capture changes that are meaningful to patients, incorporate the RIGHT measures, along with continuous longitudinal measurements, etc.,” said Patrick-Lake.

In the specific case of aducanumab, the drug manufacturer could have used PGHD in a long-term observational cohort study of cognitive function, to address concerns about benefit being observed only in terms of a short-term surrogate marker. Such a study could also have identified people who might have benefited from emerging disease-modifying therapeutics, or measured other intermediate and long-term outcomes important to patients and their caregivers. In fact, caregivers themselves might have been included in such a study. In addition, Alzheimer’s disease disproportionately affects women, low-resourced communities, and people of color,14 who often live in rural communities. PGHD could have been used to offset the typical underrepresentation of these groups in clinical research, who frequently lack access to neurologists who can diagnose and treat the disease or invite them to participate in studies. Finally, a longitudinal study could have collected patient-reported symptoms and impacts on activities of daily as modeled in Patient-focused Drug Development meetings or the FDA’s Project Patient Voice for people enrolled in cancer treatment trials.15 Patient-reported symptoms are important data that can help patients and healthcare providers when talking about the risks and benefits of a drug.

The bottom line

Patient-generated real-world evidence can be the key to understanding patients’ lived experience with health and disease, as well as lifestyles and behaviors, thus filling in gaps in traditional clinical trial data. PGHD will also be increasingly leveraged to better understand drug safety and clinical benefit, enabling a more comprehensive picture of interventions that lead to improved outcomes that matter to patients.

“We need to understand what matters most to patients. The more we observe individuals outside of clinical settings, the better we can understand what measures matter to them and optimize the development of novel interventions, management of symptoms, and treatment of disease,” said Patrick-Lake.

Capturing PGHD through digital tools outside the clinic and traditional clinical research sites  offers opportunities to reach groups that are not being adequately represented in clinical trials and to answer questions that are not being adequately addressed in clinical trials. In this way, PGHD has the potential to reduce health inequities and disparities.

The ability of PGHD to complement traditional clinical trial data might be of particular value when a study relies on a surrogate endpoint that might not reflect the lived experience of patients. It will be critical to reach the right patients, more sensitively measure meaningful changes to patients and use continuous longitudinal measurement when possible to show changes over time.

To realize the full potential of PGHD’s role in drug development, from the regulatory process through reimbursement, we also need to gain a better understanding of the nature of various types of data, assess the appropriate context of use for different types of data, continue to develop standards and value frameworks for digital endpoints, and explore and promote acceptance of PGHD as valid scientific evidence.


1. Office of the National Coordinator for Health Information Technology (ONC)
What are patient-generated health data?
January 19, 2018
Available at

2. Kourtis LC, Regele OB, Wright JM, Jones GB
Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity
npj Dig Med 2019
Available at

3. Digital Medicine Society (DiMe)
The Playbook: a DiMe Tour of Duty
Available at

4. US Food and Drug Administration (FDA)
Treatment for Heart Failure: Endpoints for Drug Development: Guidance for Industry
June 2019
Available at

5. Core Outcome Measures in Effectiveness Trials (COMET) initiative. Home page.
Available at

6. International Consortium for Health Outcomes Measurement, Inc. (ICHOM)
Heart Failure
Available at

7. DiMe
DiMe’s Library of Digital Endpoints
Available at

8. Fontanarosa J, Treadwell JR, Samson DJ, VanderBeek BL, Schoelles K.
Retinal Prostheses in the Medicare Population
Rockville, MD
Agency for Healthcare Research and Quality
2016 Sep 30
Available at

Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. March 1, 2021
Available at

10. Centers for Medicare & Medicaid Services
Proposed Rule: Medicare Program; End-Stage Renal Disease Prospective Payment System, Payment for Renal Dialysis Services Furnished to Individuals With Acute Kidney Injury, End-Stage Renal Disease Quality Incentive Program, and End-Stage Renal Disease Treatment Choices Model
July 9, 2021
Fed Register 2021;86(12):36322  
Available at

11. FDA
FDA Grants Accelerated Approval for Alzheimer’s Drug
June 7, 2021
Available at

12. Associated Press
FDA panel rejects Biogen’s new Alzheimer’s drug
November 6, 2020
Available at

13. Kissell C.
Will health insurance cover new Alzheimer’s drug, Aduhelm?
July 27, 2021
Available at

14. Basu A, Lynn N, Peschin S, Resendez J.
Value Assessment in Alzheimer’s Disease: A Focus on Equity
March 2021

15. FDA
Project Patient Voice
June 23, 2020
Available at

Related Therapeutic Areas:

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