Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using...

Area of Science:

• Health Informatics
• Clinical Data Quality
• Learning Health Systems

Background:

• Learning health systems aim to improve chronic disease management using electronic health records (EHR).
• Accurate cardiovascular risk scores depend on high-quality EHR data, but its reliability is often uncertain.
• A lack of open standards hinders the assessment of EHR data quality for secondary data uses.

Purpose of the Study:

• To develop an open data quality standard for EHR data to support learning health systems.
• To establish explicit data quality requirements for secondary use of clinical data.
• To assess the quality of EHR data for cardiovascular risk scoring.

Main Methods:

• Applied the DataGauge process, incorporating expert clinical, analytical, and informatics knowledge.
• Conducted four interviews and one focus group to gather data quality requirements.
• Developed 61 individual data quality requirements covering all standard data quality dimensions.

Main Results:

• Uncovered 705 quality issues within EHR data for 456 patients.
• Generated a comprehensive set of data quality requirements for EHR data.
• Initiated the development of explicit data quality standards for secondary data use.

Conclusions:

• The developed data quality standard provides a foundation for assessing EHR data quality.
• Addressing identified quality issues is essential for reliable cardiovascular risk estimation.
• Further validation and expansion of these requirements are planned for future research.