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

Area of Science:

• Health Informatics
• Pharmaceutical Sciences
• Data Management

Background:

• Clinical settings generate valuable real-world data (RWD) for assessing pharmaceutical effectiveness and safety.
• Regulatory agencies worldwide 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.

Main Methods:

• A scoping review of literature and regulatory guidelines (2001-2026) was performed.
• Databases searched included PubMed and Embase; regulatory agency websites were also consulted.
• Studies focused on RWD, RWE, and data quality checklists for registries were included.

Main Results:

• Thirty sources (24 publications, 6 guidelines) were analyzed.
• Data quality dimensions identified were relevance (availability, sufficiency, representativeness) and reliability (accuracy, completeness, provenance, timeliness).
• A checklist was developed covering study design, data collection/management, regulatory compliance, and continuous improvement.

Conclusions:

• The proposed checklist provides a framework for systematic RWD quality assessment.
• This tool aims to enhance RWD reliability for researchers, regulators, and industry stakeholders.
• Further studies are recommended to evaluate the checklist's applicability across diverse research settings.