Cross-Registry Benchmarking of Data Quality: Lessons Learned.

Area of Science:

• Health Services Research
• Data Quality Management
• Registry Science

Background:

• Feedback on data quality measures is standard practice for individual registries.
• However, comparative analyses of data quality across different registries are lacking.
• This gap hinders comprehensive quality assessment in health services research.

Purpose of the Study:

• To implement and evaluate a cross-registry benchmarking approach for data quality within health services research.
• To identify common data quality issues and areas for improvement across multiple research projects.
• To assess the feasibility of inter-registry data quality comparisons.

Main Methods:

• Selected quality indicators from national recommendations for two consecutive years (2020, 2021).
• Adapted indicator calculations to suit the specific settings of six participating health services research projects.
• Calculated and reported nineteen (2020) and twenty-nine (2021) quality indicators.

Main Results:

• A significant proportion of results exceeded predefined thresholds: 74% in 2020 and 79% in 2021, based on 95% confidence limits.
• Benchmarking identified specific areas needing weak-point analysis by comparing results against thresholds and among registries.
• The study demonstrated the utility of comparative data quality analysis.

Conclusions:

• Cross-registry benchmarking provides valuable insights into data quality performance across multiple health services research projects.
• This comparative approach facilitates targeted improvements and strengthens overall data integrity.
• Future health services research infrastructures could integrate such benchmarking as a standard service.