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Manually Abstracted versus Electronic Health Record Data for Surgical Quality Improvement
James L Galloway1, Vivi W Chen2, Jennifer Kramer3
1Veterans Affairs Quality Scholars Program, Atlanta VA Health Care System, Decatur, Georgia; Department of Surgery, Emory University School of Medicine, Atlanta, Georgia.
The Journal of Surgical Research
|January 10, 2026
Summary
Automating data collection for the Veterans Affairs Surgical Quality Improvement Program (VASQIP) using electronic health records (EHR) shows high accuracy for most variables. This automation can enhance surgical quality improvement (QI) programs by saving resources and improving data timeliness.
Area of Science:
- Health Informatics
- Surgical Quality Improvement
- Data Science in Healthcare
Background:
- Manual data abstraction is the benchmark for surgical quality improvement (QI) programs.
- Automating data collection can significantly benefit QI initiatives by reducing resource demands.
- The Veterans Affairs Surgical Quality Improvement Program (VASQIP) relies on manual abstraction.
Purpose of the Study:
- To assess the accuracy and concordance of electronic health record (EHR)-derived variables compared to manually abstracted VASQIP variables.
- To determine the feasibility of automating data collection for VASQIP.
- To evaluate the potential of EHR data for enhancing surgical QI.
Main Methods:
- A national, cross-sectional analysis comparing EHR-derived VASQIP variable correlates with manually abstracted VASQIP variables (2016-2020).
- Used Cohen's kappa, sensitivity, specificity, and predictive values to measure agreement.
- Defined strong agreement as kappa ≥80%.
Main Results:
- Evaluated 533,164 cases across 113 hospitals.
- High agreement (median kappa 98.1%) for race and ethnicity.
- Variable agreement for preoperative risk factors (median 28.6%) and postoperative complications (median 15.1%).
- Strong agreement for preoperative labs (median 91.9%) and intraoperative factors (median 93.9%).
Conclusions:
- EHR-derived correlates demonstrate high accuracy for automating data collection for many VASQIP variables, excluding postoperative complications.
- Automation can reduce manual data abstraction resources.
- Increased timeliness and robustness of surgical QI programs are achievable through EHR data utilization.
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