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Stakeholders' Attitudes on Automating Data Abstraction in a Surgical Quality Improvement Program.

Trenton M Haltom, Tracey Rosen, Patricia V Chen

Joint Commission Journal on Quality and Patient Safety

|June 11, 2026

Summary

Stakeholders in surgical quality improvement programs are optimistic about automating data collection but emphasize the need for human review. Further research is required to integrate data science methods into existing quality improvement workflows.

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Area of Science:

Health Services Research
Data Science in Healthcare
Surgical Quality Improvement

Background:

National surgical quality improvement programs depend on manual data abstraction, a time-consuming process.
Limited research exists on stakeholder perspectives regarding workflow efficiency and data collection automation.
Understanding these attitudes is crucial for modernizing national quality programs using data science.

Purpose of the Study:

To explore the attitudes of surgical quality nurses (SQNs) and researchers towards automating data collection in the VA Surgical Quality Improvement (VASQIP) program.
To identify potential modifications to enhance efficiency in data collection workflows.

Main Methods:

Qualitative interviews were conducted with 42 US Department of Veterans Affairs (VA) SQNs and researchers.
Thematic analysis was applied to analyze interview transcripts from 37 medical facilities.

Main Results:

Five key themes emerged: attitudes towards automating data abstraction, interpreting variable definitions, standardizing documentation, the necessity of human review for automated data, and SQN job security concerns.
Participants generally showed interest in data science for automation but stressed the continued importance of human oversight.
Concerns regarding job security were also identified among SQNs.

Conclusions:

SQNs and VASQIP researchers are open to data science-driven automation of data collection.
Human review is considered an essential component of the data collection process.
Additional research is needed to effectively automate data collection within current surgical quality improvement initiatives.