Data Quality Assessment and Multi-Organizational Reporting: Tools to Enhance Network Knowledge

Area of Science:

• Health Services Research
• Data Science
• Biostatistics

Background:

• Multi-organizational research necessitates robust data quality assessment (DQA) to ensure data integrity.
• Traditional internal reliability and validity checks are insufficient for cross-organizational data comparison.
• The absence of an external "gold standard" in multi-site studies poses challenges for data validation.

Purpose of the Study:

• To demonstrate a multi-organizational DQA approach for assessing data consistency and patterns across participating organizations.
• To present the DQA process and reporting system developed by Kaiser Permanente's (KP) Center for Effectiveness and Safety Research (CESR).
• To highlight the applicability of the KP CESR DQA model for networks aiming to enhance data quality.

Main Methods:

• Described the DQA process implemented by the CESR Data Coordinating Center (DCC).
• Emphasized the CESR DQA reporting system for standardized comparison of data summaries from eight KP organizations.
• Utilized examples of multi-organization comparisons for direct electronic health record (EHR) data and non-EHR data.

Main Results:

• Demonstrated the utility of multi-organization DQA comparisons in confirming data quality expectations.
• Provided examples of DQA confirming data consistency for both EHR and non-EHR data sources.
• Showcased the development of efficient codes and procedures for DQA implementation and reporting by the CESR DCC.

Conclusions:

• The CESR DCC empowers data managers with DQA tools for continuous self-assessment.
• Dissemination of DQA findings addresses data shortfalls and documents idiosyncrasies, fostering knowledge exchange.
• The KP CESR DQA model promotes transparency, enhances network knowledge, and informs multi-organizational research.