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Targeted use of large language models for EHR-based computable phenotyping.

Dylan Owens1, Jing Cao2, Mehak Gupta3

  • 1Office of Clinical Research, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.

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Summary
This summary is machine-generated.

Uncertainty-guided selective use of large language models (LLMs) improves electronic health record (EHR) phenotyping accuracy and scalability. This approach efficiently targets LLM analysis to patients needing it most, enhancing clinical research and quality reporting.

Keywords:
clinical decision support systemscomputable phenotypeelectronic health recordslarge language modelsmachine learning

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Area of Science:

  • Biomedical Informatics
  • Clinical Data Science
  • Artificial Intelligence in Healthcare

Background:

  • Electronic health records (EHRs) are crucial for clinical research and quality reporting.
  • Large language models (LLMs) can extract valuable information from unstructured EHR notes.
  • Universal LLM application for phenotyping is computationally expensive.

Purpose of the Study:

  • To evaluate the effectiveness of uncertainty-guided selective LLM use for improving EHR phenotyping accuracy and scalability.
  • To develop a framework integrating structured and unstructured EHR data with LLMs.

Main Methods:

  • Developed a selective augmentation framework using uncertainty-guided triage.
  • Integrated structured EHR data with LLM analysis of unstructured notes for flagged patients.
  • Evaluated performance for diabetes mellitus and peripheral arterial disease (PAD) phenotypes.

Main Results:

  • Selective augmentation improved sensitivity for diabetes mellitus (0.81 to 0.90) without impacting specificity (0.92).
  • For PAD, sensitivity increased from 0.18 to 0.97 with high specificity (0.99), using LLMs for only 10% of patients.
  • Over 70% of triage-flagged patients were misclassified by structured data alone.

Conclusions:

  • Uncertainty-guided triage efficiently focuses LLM use on patients benefiting most.
  • This method enhances case identification, especially for phenotypes poorly captured by structured data.
  • Selective LLM integration offers a scalable, accurate, and practical EHR phenotyping solution.