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Related Experiment Videos

The Best of Both Worlds: How Combining a Large Language Model and a Rules-based Algorithm Makes CAUTI Surveillance

Joshua Nordman1, Claire Najjuuko2, Nicholas Jeschke3

  • 1Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America
|May 14, 2026
PubMed
Summary

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Large language models (LLMs) significantly improve catheter-associated urinary tract infection (CAUTI) surveillance by enhancing electronic algorithms. This AI approach reduces manual review time for infection preventionists, boosting patient safety.

Area of Science:

  • Infectious Disease Surveillance
  • Artificial Intelligence in Healthcare
  • Clinical Informatics

Background:

  • Catheter-associated urinary tract infection (CAUTI) surveillance is vital for patient safety.
  • Current electronic algorithms require manual chart review for CAUTI case confirmation.
  • Large language models (LLMs) offer potential to enhance CAUTI surveillance.

Purpose of the Study:

  • To evaluate the effectiveness of a large language model (LLM) in improving CAUTI surveillance.
  • To compare different methods of applying the NHSN CAUTI definition using LLMs.
  • To assess the potential of LLMs to reduce manual chart review burden.

Main Methods:

  • Analysis of 919 potential CAUTI cases flagged by electronic surveillance.
  • Comparison of rule-based logic with LLM incorporating Clinical Entity Augmented Retrieval (CLEAR).
Keywords:
AICAUTIcatheter-associated urinary tract infectiondevice-associated infectionhealthcare-associated infectionindwelling urinary catheterlarge language modelsurveillance

Related Experiment Videos

  • Application of National Healthcare Safety Network (NHSN) CAUTI definition to electronic medical record (EMR) data.

    • Main Results:

    • The combined LLM and CLEAR approach achieved 90.0% sensitivity and 93.5% specificity.
    • Expert adjudication improved sensitivity to 93.6% and specificity to 98.6%.
    • False negatives were often due to missing symptom information in clinical documentation.

    Conclusions:

    • LLM augmentation significantly enhances CAUTI surveillance accuracy.
    • This approach may improve efficiency by reducing manual chart review for infection preventionists.
    • Optimizing clinical information presented to LLMs can further improve performance.