In the face of rapid innovation, a strong need exists for improved evidence generation capabilities to answer critical questions faced by regulators and payers. Systematically collecting real-world data to generate real-world evidence may provide the means to supplement the evidence available for newly approved novel therapies. Under the auspices of the Duke-Margolis Real-World Evidence (RWE) Collaborative and the Center’s Value for Medical Products Consortium, a multi-stakeholder working group considered how the health care community might align to ensure real-world data is capable of generating evidence that meets the needs of payers and regulators.
Primarily through the lens of three disease use cases (Alzheimer’s disease, cardiovascular disease, and spinal muscular atrophy), this paper explores considerations and provides recommendations to stakeholders for building a robust real-world data ecosystem through seven key themes. Additionally, data governance and resource needs are considered.
To build a robust real-world data ecosystem, policymakers and other key stakeholders should take the following steps:
- Provide resources needed to support electronic health record (EHR) interoperability between clinical research networks and central claims data repositories, such as those funded by Patient-Centered Outcomes Research Institute (PCORI);
- Provide additional funding for the United States Food and Drug Administration’s (FDA) Sentinel Initiative and other existing initiatives focused on safety and effectiveness (e.g., registries or observational studies that monitor the safety and effectiveness of therapies granted accelerated approval);
- Reconsider bans on unique patient identifier funding and encourage the Department of Health and Human Services (HHS) through the Office of the National Coordinator for Health IT to advance unique patient identifier development;
- Harmonize stakeholder goals and initiatives by cultivating and supporting pre-competitive, public-private partnerships.
If implemented, we believe the recommendations herein support robust post-market data collection that could enable efficient generation of evidence to supplement clinical trial results and further enable learning health care systems.