PiVily Quarterly Report
The evidence infrastructure took shape
Regulatory ambition around data and AI became more operational, while real-world evidence guidance focused attention on methods, transparency and the question a dataset can actually answer.
The quarter in one reading
The quarter shifted the conversation from whether regulators would use broader data and AI to how those capabilities should be organised, governed and made useful across the medicines lifecycle.
This retrospective edition was reconstructed from official materials and completed on 11 August 2026. It describes the quarter; it does not imply contemporaneous publication.
Material signals
What moved—and why it mattered.
Data and AI moved into a multi-year regulatory workplan.
EMA and HMA published a joint Data and AI workplan to 2028 in May 2025, covering data discoverability and quality, real-world evidence, AI guidance, tools, capability and collaboration across the European medicines regulatory network.
A workplan is not proof of completed transformation. It is evidence that data access, methods, technology and workforce readiness are being managed as one regulatory capability rather than unrelated experiments.
Does the PV technology roadmap connect data quality, scientific use, change management and governance—or fund them separately?
Real-world evidence was framed around the research question.
EMA published its reflection paper on non-interventional studies using real-world data for regulatory purposes in April 2025 and continued developing a roadmap for RWE guidance.
The recurring discipline is fit-for-purpose use. Large, accessible or familiar data are not automatically relevant, reliable or sufficient for a safety question.
Can the team explain why a specific data source and design are suitable for the decision—not simply available?
The supporting infrastructure made provenance more visible.
EMA updated guidance supporting the HMA-EMA catalogues of real-world data sources and studies, which are intended to improve discoverability, transparency and trust in research based on real-world data.
Evidence maturity includes the ability to locate, describe and reconstruct the source and study—not only interpret the final result.
Would another qualified reviewer be able to trace the evidence from research question through data provenance, protocol and result?
Next-quarter watch
Questions to carry forward.
- How regulator workplans translate into operational guidance and tools
- Greater convergence on RWE terminology and study expectations
- Whether organisations build evidence literacy beyond specialist teams
Method & source register
Trace the reading back to the record.
PiVily selects developments for system-level relevance to human pharmacovigilance. Facts are attributed to official publishers; interpretation is editorial. This is not an exhaustive jurisdictional change log or organisation-specific advice.