Key Real-World Data Management Practices for Registry Evaluation and Quality Assurance

Area of Science:

• Health Informatics
• Data Science
• Pharmaceutical Research

Background:

• Clinical settings generate valuable real-world data (RWD) for assessing pharmaceutical effectiveness and safety.
• Regulatory agencies like the FDA, EMA, and PMDA increasingly utilize real-world evidence (RWE) in decision-making.
• Registries are vital for RWE studies, necessitating stringent quality control for RWD reliability.

Purpose of the Study:

• To identify essential quality elements for registry-based RWD.
• To develop a data quality management checklist for registry-based research.
• To enhance the reliability and systematic quality assessment of RWD.

Main Methods:

• Conducted a scoping review of literature and regulatory guidelines (2001-2026).
• Searched databases PubMed, Embase, and major regulatory agency websites.
• Included studies focused on RWD, RWE, and data quality checklists for registries.

Main Results:

• Analyzed 30 sources (24 publications, 6 guidelines).
• Identified two core data quality dimensions: relevance (availability, sufficiency, representativeness) and reliability (accuracy, completeness, provenance, timeliness).
• Developed a quality assessment checklist covering study design, data management, regulatory compliance, and continuous improvement.

Conclusions:

• The proposed checklist provides a framework for enhancing RWD reliability and systematic quality assessment.
• Supports robust data quality management for researchers, regulatory agencies, and pharmaceutical companies.
• Future studies should evaluate the checklist's applicability across diverse research settings.