Pilot application of automated surveillance of postoperative infections (ASPIN) to monitor patient outcomes.

Authors

Nicole M Mott, Micaella R Zubkov, Guannan Shen, William G Henderson, Ahmad M Hider, Michael R Bronsert, Yi Su, Yaxu Zhuang, Yizhou Fei, Anne Lambert-Kerzner, Quintin W O Myers, Kathryn L Colborn, Robert A Meguid

Published in

American journal of surgery. Volume 262. Pages 117264. Sep 26, 2026. Epub Sep 26, 2026.

Abstract

Traditional quality registries are limited by manual data abstraction. We aimed to apply Automated Surveillance of Postoperative Infections (ASPIN), models for detecting post-operative infections in electronic health record (EHR) data, to surgeons from two different surgical service lines to pilot implementation.
We applied ASPIN to all thoracic surgery and surgical oncology operations in 2024, generating a report for a surgeon from each service line with patients' pre-operative risk and post-operative probability of experiencing a post-operative infection using estimated infection rates and observed-to-expected ratios. We benchmarked the individual surgeon's data to their service line and confirmed infections through chart review.
Post-operative infectious complication rates varied for each surgeon compared to their service line. Several patients flagged by ASPIN experienced a post-operative infection on chart review.
Pilot application of ASPIN illustrates the strengths of automated surveillance for quality improvement and provides a roadmap for broader application.

PMID:
42842970
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

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