Advancing the use of real world evidence in health technology assessment: insights from a multi-stakeholder workshop

FRONTIERS IN PHARMACOLOGY(2024)

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摘要
Introduction: Real-world evidence (RWE) in health technology assessment (HTA) holds significant potential for informing healthcare decision-making. A multistakeholder workshop was organised by the European Health Data and Evidence Network (EHDEN) and the GetReal Institute to explore the status, challenges, and opportunities in incorporating RWE into HTA, with a focus on learning from regulatory initiatives such as the European Medicines Agency (EMA) Data Analysis and Real World Interrogation Network (DARWIN EU (R)).Methods: The workshop gathered key stakeholders from regulatory agencies, HTA organizations, academia, and industry for three panel discussions on RWE and HTA integration. Insights and recommendations were collected through panel discussions and audience polls. The workshop outcomes were reviewed by authors to identify key themes, challenges, and recommendations.Results: The workshop discussions revealed several important findings relating to the use of RWE in HTA. Compared with regulatory processes, its adoption in HTA to date has been slow. Barriers include limited trust in RWE, data quality concerns, and uncertainty about best practices. Facilitators include multidisciplinary training, educational initiatives, and stakeholder collaboration, which could be facilitated by initiatives like EHDEN and the GetReal Institute. Demonstrating the impact of "driver projects" could promote RWE adoption in HTA.Conclusion: To enhance the integration of RWE in HTA, it is crucial to address known barriers through comprehensive training, stakeholder collaboration, and impactful exemplar research projects. By upskilling users and beneficiaries of RWE and those that generate it, promoting collaboration, and conducting "driver projects," can strengthen the HTA evidence base for more informed healthcare decisions.
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关键词
health technology assessment,regulatory,real world evidence,real world data,common data model,federated data network
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