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Zero-shot large language model application for surgical site infection auditing.

Shrirajh Satheakeerthy1, Brandon Stretton1, James Tsimiklis2

  • 1Lyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia; Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia; Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.

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Large language models (LLMs) show promise in identifying surgical site infections (SSI) by analyzing patient notes. This study demonstrates high accuracy, suggesting LLMs could aid in infection monitoring.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Surveillance

Background:

  • Surgical site infections (SSI) pose a significant clinical challenge.
  • Artificial intelligence (AI), specifically large language models (LLMs), offers potential for enhanced SSI monitoring.
  • Current monitoring methods can be labor-intensive and may benefit from AI assistance.

Purpose of the Study:

  • To evaluate the feasibility and performance of a large language model (LLM) in identifying surgical site infections (SSI) from clinical notes.
  • To assess the accuracy, sensitivity, and specificity of the LLM in detecting SSI cases.
  • To determine the timeliness of SSI detection by the LLM.

Main Methods:

  • A retrospective study utilizing the Llama 3.0 70-billion parameter model.
  • Analysis of clinical inpatient and outpatient progress notes from 28 patients (14 SSI cases, 14 controls).
  • LLM classification of notes for SSI presence, followed by performance characteristic analysis.

Main Results:

  • The LLM achieved 93% overall patient-level accuracy in identifying SSI.
  • Demonstrated 100% sensitivity and 86% specificity for SSI detection.
  • For initial case identification, 92.3% of notes flagged by the LLM were on or before the clinician-identified infection onset date.

Conclusions:

  • Large language models (LLMs) are a feasible tool for screening medical records to detect surgical site infections (SSI).
  • The study highlights the potential of LLMs to assist in clinical workflows for infection surveillance.
  • Further research is recommended to explore LLM integration into routine clinical practice for SSI management.