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Proof-of-Concept Prompted Large Language Model for Radiology Procedure Request Routing.

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A large language model (LLM) accurately routed hospital procedure requests, demonstrating a cost-effective solution for improving clinical communication and workflow efficiency.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Workflow Optimization

Background:

  • Efficient routing of procedure requests is crucial in large academic hospitals.
  • Current methods may be manual or complex, leading to delays and errors.
  • Large language models (LLMs) offer potential for automating and improving communication tasks.

Purpose of the Study:

  • To evaluate the accuracy and cost-effectiveness of a prompted LLM for routing procedure requests.
  • To assess LLM performance in directing requests to appropriate hospital teams, phone numbers, or pagers.

Main Methods:

  • Developed text-based rules for procedure request routing based on existing hospital teams, schedules, and contact information.
  • Created a prompted LLM using GPT-3.5-turbo and GPT-4, and four open-weight models.
  • Tested the LLM on 250 in-scope and 25 out-of-scope procedure requests.

Main Results:

  • GPT-4 achieved 96.4% accuracy for in-scope and 76.0% for out-of-scope requests, outperforming other models (P < .001).
  • All models showed reduced performance during evening and weekend hours (P < .001).
  • API costs were low: approximately $0.03/request for GPT-4 and $0.0006/request for GPT-3.5-turbo.

Conclusions:

  • Prompted LLMs are accurate and cost-effective for routing procedure requests in academic hospitals.
  • This technology can assist clinicians in navigating complex communication systems, such as radiology phone trees.
  • LLMs can serve as valuable tools for reading room coordinators to enhance request routing efficiency.