Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data...

Area of Science:

• Public Health Surveillance
• Health Informatics
• Data Quality Management

Background:

• Modernization efforts increasingly utilize electronic health record (EHR) data for public health surveillance.
• EHR data streams often exhibit variability in completeness, accuracy, and representativeness, posing challenges for reliable surveillance.
• Ensuring the quality of EHR data is paramount for accurate public health insights.

Purpose of the Study:

• To develop and implement a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project.
• To identify and resolve data quality issues impacting chronic disease prevalence estimates derived from EHR data.
• To outline actionable steps for improving EHR data quality in surveillance.

Main Methods:

• Examined MENDS validation processes across 5 data-contributing organizations from December 2020 to August 2023.
• Developed protocols to identify gaps in EHR databases and data extraction, mapping, integration, and analysis processes.
• Documented specific examples of source-data and data-processing problems.

Main Results:

• Identified critical errors in both EHR source data and data transformation processes.
• Observed issues such as missing race/ethnicity and zip code data, duplicate or missing patient records, and implausible values.
• Highlighted the impact of data quality gaps on surveillance data.

Conclusions:

• Data validation is essential for enhancing the quality and accuracy of EHR-based surveillance estimates.
• The MENDS validation process demonstrated the value of rigorous data quality checks.
• Lessons learned from this pilot project are applicable to broader EHR-based surveillance initiatives.