On March 16, 2026, the EMA’s Committee for Medicinal Products for Human Use (CHMP) adopted a Data Quality Framework (DQF) for Real-World Data (RWD) to support regulatory use of Real-World Evidence (RWE). The framework extends the existing EMRN DQF and provides structured, actionable guidance for assessing data quality in regulatory submissions and decision-making.

Key Objectives

  • Establish a harmonised approach to evaluating RWD quality in regulatory contexts
  • Support fit-for-purpose assessment of datasets for specific research questions
  • Enable transparent and consistent use of RWE across the product lifecycle
  • Align with EU initiatives (e.g., EHDS, DARWIN EU) and existing standards (ICH, ENCePP)

Core Framework Components

The RW-DQF is structured around three main pillars:

  1. Systems & Process Characterisation
    • Assessment of how data are collected, processed, and governed
    • Includes maturity model and checklist for data provenance, governance, and infrastructure
  2. Data Quality Metrics
    • Quantitative assessment across key dimensions:
      • Reliability (accuracy, traceability)
      • Extensiveness (completeness, coverage)
      • Coherence (consistency, standardisation)
      • Timeliness (data currency)
    • Metrics support DQ assessment, assurance, and reporting
  3. Fitness-for-Use Assessment
    • Evaluates whether data are suitable for a specific research question
    • Requires alignment of dataset with:
      • Study design
      • Population
      • Variables (exposure, outcomes, confounders)

Key Regulatory Principles

  • No fixed thresholds: Data quality acceptability is context-dependent
  • Question-driven assessment: “Relevance” is defined by the research question
  • Lifecycle responsibility: Data quality is shared across stakeholders(data holders, users, regulators)
  • Transparency requirement: Detailed documentation of data processes and limitations is expected

Important Considerations for RWD

The framework highlights challenges specific to RWD:

  • Heterogeneity of sources, formats, and coding systems
  • Secondary use limitations (data not originally collected for research)
  • Data linkage and pooling risks (e.g., misclassification, loss of precision)
  • Data drift over time affecting reliability
  • Privacy constraints limiting access to patient-level data

Implications for Industry

  • Sponsors must provide robust data quality documentation in submissions
  • Increased emphasis on:
    • Metadata transparency
    • Traceability and lineage
    • Standardisation (e.g., CDMs, vocabularies)
  • Early feasibility and relevance assessments will be critical
  • May impact study design, data source selection, and regulatory strategy

Regulatory Impact

  • Strengthens EMA expectations for RWE use in regulatory decision-making
  • Supports broader EU data initiatives and integration of RWD into regulatory frameworks
  • Provides a non-prescriptive but structured approach adaptable across use cases

Bottom Line

The EMA RW-DQF represents a major step toward standardising RWD quality assessment, reinforcing that data must be demonstrably fit-for-purpose—not just available—to support regulatory decisions.