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**Efficient Data Quality Assessment for Health Research**

Explore how Kaiser Permanente's Center for Effectiveness and Safety Research implements systematic and scalable clinical data quality assessment through its Virtual Data Warehouse, guiding principles, program templates, and reports. Learn how the DQA process ensures consistent, reliable output for researchers to inform their studies.

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**Efficient Data Quality Assessment for Health Research**

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  1. Systematic and Scalable Clinical Data Quality AssessmentKaiser Permanente Center for Effectiveness and Safety Research Alan Bauck

  2. Background • Kaiser is composed of eight regions, each with a research center • Kaiser Permanente (KP) Center for Effectiveness and Safety Research (CESR) is a KP research collaborative • Common Data Model is the CESR Virtual Data Warehouse • KP CESR Data Coordinating Center responsible for data quality assessments

  3. Guiding Principles for DQA

  4. DQA Process

  5. Process

  6. DQA Program Templates & Macros • Audience: DCC programmers and site programmers • Provides efficient, tested code to simplify DQA programs and ensures consistent, reliable output

  7. Single Site Report • Audience: Site data managers and programmers • Generated by the DQA program and available to sites as soon as they run the DQA. This encourages site review and self-improvement of data.

  8. Multi-Site Comparisons Report • Audience: Site data managers and programmers • Combines site data and compares patterns across sites and over time

  9. Summary Report • Audience: Researchers • Synthesizes key points across data domains to inform research and avoid large data quality surprises

  10. Take Aways • Provide feedback loops to improve the data quality • Encourages site review and self-improvement • Allows comparisons between sites • Develop tools for repeatable processes • Check for a wide range of the data quality categories (conformance, completeness, plausibility)

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