Hidden failure modes of large language models in healthcare-associated infection surveillance: a structured evaluation using NHSN definitions

GPT-5.1 Thinking shows potential to support infection surveillance under strict constraints but exhibits systematic limitations, including overreliance on clinical intuition and difficulty with complex exclusion pathways. Currently, LLMs are unsuitable for autonomous NHSN classification but may serve as supervised decision-support tools with robust human oversight. Further development is needed to enhance LLMs' ability to synthesize surveillance definitions and complex situational characteristics critical for effective HAI surveillance, though fully autonomous deployment would require further validation. These findings are based on synthetic data that may differ from real-world clinical data in ways likely to overestimate the accuracy of these tools.