Findings
High-quality data must be complete, accurate, timely, and fit for purpose, ensuring reliability for RWE generation.
Effective governance is critical to ensure transparency, ethical standards, and stakeholder engagement in managing RWD.
Data capture challenges include standardization, provenance tracking, and interoperability, particularly for EHR-based data.
Data curation is iterative and involves organizing, assessing, and preparing raw data to meet study-specific needs.
The maturity model identifies five stages of organizational data capabilities, emphasizing consistency, completeness, and automation.

Recommendations
Implement robust governance frameworks to address transparency, stakeholder engagement, and ethical considerations in RWD use.
Focus on improving data capture at the point of care through standardization and semantic interoperability.
Use common data models and validated extraction-transformation-loading (ETL) processes to enhance data consistency and reliability.
Prioritize iterative data curation practices, supported by metadata and provenance tracking, to improve fitness for use over time.
Leverage the NESTcc Data Quality Maturity Model to benchmark and enhance organizational capabilities in RWD management.

Regulatory Considerations
Ensure compliance with patient privacy laws such as HIPAA and GDPR, especially when linking data across sources.
Align data capture and curation practices with FDA guidance for RWE generation and medical device evaluation.
Establish clear data use agreements to protect patient data while enabling analysis for regulatory and research purposes.
Document data transformations, including metadata and provenance, to support reproducibility and transparency in regulatory submissions.
Embrace standard terminologies and data dictionaries to facilitate interoperability and regulatory acceptance.