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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Informatics
  • Natural Language Processing (NLP)

Background:

  • Evaluating large language model (LLM) agents in clinical settings is challenging.
  • Existing benchmarks lack FHIR-compliant Electronic Health Record (EHR) integration.
  • Previous LLM agents struggled with complex clinical tasks and EHR interactions.

Purpose of the Study:

  • To introduce MedAgentBench, the first benchmark for LLM agents on clinical tasks within a FHIR-compliant EHR.
  • To present improved prompt engineering, tool design, and a memory component for LLM agents.
  • To develop new clinically-driven tasks to evaluate agent generalization and real-world applicability.

Main Methods:

  • Developed MedAgentBench with a FHIR-compliant EHR.
  • Implemented prompt engineering techniques, including chain-of-thought reasoning and few-shot examples.
  • Introduced enhanced tools for EHR interaction, output formatting, and calculations, plus a memory component.

Main Results:

  • The LLM agent achieved a 91.0% success rate without memory and 98.0% with memory using GPT-4.1.
  • The agent demonstrated improved performance on tasks without memory entries, suggesting adaptability.
  • 300 new multi-step clinical tasks were developed in collaboration with a physician for comprehensive evaluation.

Conclusions:

  • Significant improvements in LLM agent design enhance performance on clinical EHR tasks.
  • The memory component and prompt engineering are crucial for high success rates.
  • Further development of EHR agents and benchmarks is needed for responsible AI deployment in healthcare.