Encouraging Post-Market Evidence Generation for Transformative Therapies
Policies that Could Reduce the Data Collection Burden on Healthcare Providers
Beena Bhuiyan Khan
Rebecca Ray
Duke-Margolis Center for Health Policy
@DukeMargolis
I

nnovations in medical technology have led to a proliferation of transformative therapies that promise to alter treatment paradigms and even reverse disease progression. The promise of treatment outcomes can make these technologies eligible for expedited regulatory pathways that do not require extensive clinical trial data for market approval.

As more novel technologies, such as cell and gene therapies and other high cost technologies, are approved faster based on promising early clinical data, it is critical to encourage evidence generation in the post-market setting that evaluates long-term safety, effectiveness, and durability. Currently, data collection is mostly undertaken through traditional prospective clinical registries. With advances in data systems and platforms for real-world evidence (RWE), there is an opportunity to further enhance evidence generation while also addressing limitations and challenges in collecting data through traditional registries.

Stakeholders Involved in Evidence Generation

Evidence generation is driven by multiple stakeholders including regulators, healthcare providers, and payers. Regulators, like the US Food and Drug Administration (FDA), typically require post-market surveillance (PMS) data for newly approved technologies. Post-market surveillance studies are limited to identifying safety signals in real-world practice settings. Healthcare providers are key actors in generating new evidence about the performance of new technologies in real-world settings, as well as in identifying which patient populations best respond. With the onset of provider payment reform and alternative care delivery models, healthcare providers also have many data collection requirements to satisfy quality and performance metrics. Multiple data collection and reporting requirements often lead to administrative burdens and resource costs that ultimately discourage additional data collection.

Payers have been instrumental in motivating data collection through requirements for reimbursement, quality metrics, specialty certification, site accreditations, among others. The most notable US example requiring data collection for reimbursement is Medicare’s Coverage with Evidence Development (CED), a paradigm in which novel therapies are granted national Medicare coverage with the condition that outcomes data is captured in a national clinical registry, thus allowing patients to benefit from promising interventions that warrant additional evidence. By promoting continued evidence development under real-world settings, it helps to address questions that are not answered through clinical trials, such as long-term outcomes and effectiveness, durability of the therapy, and collecting clinical evidence across subgroups of patients or indications. Medicare has used CED since 2006 and found it to be instrumental in facilitating the adoption of novel therapies. The Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry (TVT Registry) is an example of a registry that is managed by specialty societies and has been used to satisfy Medicare CED requirements as well as FDA’s PMS requirements for novel cardiac valve therapies. Data from this registry has also been used in regulatory submissions for label expansions and informing clinical practice guidelines.

The single national registry model, such as the TVT Registry and other CED registries, have several advantages that include:

  • enabling direct comparison across all technologies in the same therapeutic category;
  • facilitating analyses of patients treated by different subgroups and provider types;
  • studying durability and long-term effects on a predictable timeline across all patients and subgroups; and
  • developing benchmarks for quality of care and outcomes.

There are, however, many limitations in these registries, stemming from their model of prospective clinical studies. These include high costs to participate and maintain, administrative burdens of multiple reporting requirements, and operational limitations in data access. These challenges can discourage healthcare providers from enrolling in the registry and, thus, offering the therapies. On the other hand, Medicare requirements for data collection for reimbursement as well as other payer requirements establish a strong motivation for healthcare providers to remain engaged.

Improved data collection, curation, and analysis methods can mitigate these challenges and lessen the burden on healthcare providers. Data collection that builds on advanced data systems RWE are less costly and can effectively complement prospective clinical registry models. These systems are based on an infrastructure of automated, near real-time data collection that allow for faster data analyses, allowing stakeholders to make faster evidence-based decisions.

Policy Recommendations to Improve Evidence Generation

Payers can play a key role in improved data collection efforts by enacting policies that encourage healthcare providers to adopt advanced data systems that modernize existing evidence infrastructure. For example, CMS (the Centers for Medicare and Medicaid Services) can expand the Promoting Interoperability Programs, formerly known as the Medicare and Medicaid EHR Incentives Program, to provide clear incentives for healthcare providers to further develop their use of electronic health record (EHR) technology to enable streamlined data collection and invest in more advanced data capabilities.

CMS as well as other payers can expand their payment reforms both on the provider and care delivery levels. In 2019, there were 1,588 existing public and private accountable care organizations participating in both Medicare’s Shared Savings Program and other provider payment reforms that have quality and performance metrics that healthcare providers need to report. As these models already require layers of data reporting, one opportunity to ensure continued evidence generation for new technologies is to align their performance measures with larger care delivery and provider performance measures. This alignment will ensure the data being collected for new technologies have synergies with other reporting requirements. Simplifying and standardizing performance measurement reporting by aligning on outcome measures can reduce redundancies and administrative burden for the provider.

Finally, payers can undertake regular reviews of evidence generation priorities and criteria to inform what evidence should be collected that can yield more value. This change could entail working alongside other stakeholders and forming public-private partnerships to agree upon the most important evidence questions, streamline data collection forms, and ease the implementation by sharing best practices and a formal evaluation plan.

Conclusion

Transformative therapies offer the promise of significant improvements in healthcare. Given their complexities and cost, it is critical to ensure continued data collection in the post-market setting. Efforts to reduce the burden of evidence generation by developing a sophisticated data infrastructure and streamlining longitudinal data collection can ensure effective use of transformative therapies.
References available upon request.
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