JMIR Perioperative Medicine · Published 2026-06-26 · DOI 10.2196/87896
Arjun Chakraborty, Peter Tarczy-Hornoch, Dustin Long, Meliha Yetisgen
Abstract BackgroundSurgical site infections (SSIs) affect 160,000 to 300,000 patients annually, increasing postoperative mortality, causing significant complications, and incurring US $3.5 to US $10 billion in excess costs each year. Effective SSI surveillance can inform strategies to mitigate these outcomes. Traditional SSI surveillance methods, primarily manual chart reviews, are costly and labor-intensive. ObjectiveThis study aimed to evaluate whether an automated SSI surveillance system built using newer natural language processing methods and deep learning could outperform previous approaches and whether such an approach could enable more efficient infection surveillance. MethodsOur dataset comprised approximately 30,000 surgical cases from the University of Washington Medical Center (UWMC) and Harborview Medical Center (HMC). Data from UWMC were captured for the National Surgical Quality Improvement Program, and data from HMC were captured for the National Healthcare Safety Network. Electronic health record (EHR) data for each surgical case included structured data pertaining to surgical procedure characteristics, laboratory values, and antibiotic administration, as well as clinical text notes for a surgical case from 7 days before to 90 days after surgery. Using this data, we applied a myriad of machine learning approaches to the task of SSI prediction. We reported the following performance metrics: F1 ResultsIn a cohort of 5996 surgical cases, incorporating multimodal EHR information—including contextual information from clinical text and temporal information from laboratory values—improved SSI prediction performance. Models using structured data and clinical text outperformed structured data alone (F1F1PF1PF1F1P ConclusionsAutomated surveillance approaches—particularly deep learning approaches—in combination with voluminous, multimodal data from the EHR, can enable more efficient infection surveillance processes. This has the potential to increase the quantity of SSI surveillance data available to guide interventions aimed at reducing SSI rates.
Abstract from DOAJ. Public domain (CC0 1.0).
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Chakraborty, A., Tarczy-Hornoch, P., Long, D., et al. (2026). Automated Identification of Surgical Site Infections From Electronic Medical Records: Retrospective Observational Predictive Modeling Study. JMIR Perioperative Medicine. https://doi.org/10.2196/87896