Generative artificial intelligence for surgical site infection surveillance

Abstract

BACKGROUND: Surgical site infection (SSI) surveillance can be time consuming and resource intensive. This study investigates the potential of generative artificial intelligence (GenAI) to augment the detection and classification of SSIs. METHODS: A case control study of patients with SSI following spine surgery at one US hospital. SSIs were classified into superficial, deep, and organ space. All SSIs were confirmed by infection prevention (IP) experts as they occurred from October, 2023 to September, 2025 and matched 1:1 by year to surgeries deemed non-SSI. A secure GenAI was used to determine if patients had an SSI based on standardized prompts and clinical data. IP nurses used GenAI output to review cases with the ability to ask GenAI questions within the data provided or independently open the medical record. We compared GenAI determinations to initial IP nurses' determinations. RESULTS: A total of 555 patients had spine surgeries. All 16 SSIs were matched by year to 16 non-SSI. All SSIs were correctly identified by GenAI (sensitivity 100%, 16/16) and only 1 non-SSI was incorrectly identified as SSI (specificity 93.7%, 15/16). Although GenAI accurately identified all SSI cases, it was discordant with original review at classifying the level of infection in 37.5% (6/16) of cases. Upon final IP physician review, GenAI was correct in 66.7% (4/6) of discordant cases (often determining "organ space infections" rather than "deep"). Median time to complete GenAI assisted SSI reviews was 9 minutes (IQR 7-21). CONCLUSION: GenAI is a promising tool to assist in SSI surveillance following spinal surgery that could improve efficiency.

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