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Automated Versus Semi-Automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program.

Alex H S Harris¹, Asqar Shotqara², Esther L Meerwijk²

¹Veterans Affairs Health Services Research, Development Center for Innovation to Implementation, Palo Alto Veterans Affairs Health Care System, Palo Alto, California; Department of Surgery, Stanford University, Palo Alto, California.

The Journal of Surgical Research

|July 31, 2024

Summary

This summary is machine-generated.

Fully automating preoperative lab value extraction for cardiac surgery cases showed high accuracy and reduced missing data compared to the current semiautomated system. This method offers advantages for quality improvement programs.

Keywords:

Cardiac surgery Clinical registries Data science Quality improvement

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Area of Science:

Health Informatics
Medical Data Extraction
Surgical Quality Improvement

Background:

The Veterans Affairs Surgical Quality Improvement Program (VASQIP) relies on surgical quality nurses (SQNs) to extract 187 variables for cardiac surgery cases.
VASQIP currently uses a semiautomated (SA) system for ten preoperative lab values, with manual extraction for data from other facilities.

Purpose of the Study:

To develop and validate a fully automated method for extracting ten preoperative laboratory values.
To compare the automated method against the existing SA method within VASQIP.

Main Methods:

Developed methods to extract ten preoperative lab values and dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes.
Compared automated (A) extraction against SA extraction based on agreement, data definition conformance, proximity to surgery, and missingness.

Main Results:

High intraclass correlation coefficients (0.90–0.98) were observed between automated and SA lab values.
The automated method significantly reduced missing data (e.g., 2.4% vs. 22.5% for HDL) and eliminated out-of-range entries.

Conclusions:

Fully automating preoperative lab value extraction demonstrates high congruence with SA SQN-verified data.
Automated extraction offers advantages, including reduced missingness and improved data accuracy for surgical quality improvement.