Large language models management of complex medication regimens: a case-based evaluation

Aaron Chase¹Amoreena Most²Shaochen Xu²Erin Barreto³Brian Murray⁴Kelli Henry⁵Susan Smith²Tanner Hedrick⁶XianYan Chen²Sheng Li⁷Tianming Liu²Andrea Sikora^4,8*

• ¹University of Georgia, Tbilisi, Georgia
• ²University of Georgia, Athens, Georgia, United States
• ³Mayo Clinic, Rochester, Minnesota, United States
• ⁴University of Colorado, Denver, Colorado, United States
• ⁵WellStar Health System, Atlanta, Georgia, United States
• ⁶University of North Carolina Hospitals, Chapel Hill, North Carolina, United States
• ⁷University of Virginia, Charlottesville, Virginia, United States
• ⁸College of Pharmacy, University of Georgia, Athens, United States

Background: Large language models (LLMs) have shown the ability to diagnose complex medical cases, but only limited studies have evaluated the performance of LLMs in the development of evidence-based treatment plans. The purpose of this evaluation was to test four LLMs on their ability to develop safe and efficacious treatment plans on complex patients managed in the intensive care unit (ICU). Methods: Eight high-fidelity patient cases focusing on medication management were developed by critical care clinicians including history of present illness, laboratory values, vital signs, home medications, and current medications. Four LLMs [ChatGPT (GPT-3.5), ChatGPT (GPT-4), Claude-2, and Llama-2-70b] were prompted to develop an optimized medication regimen for each case. LLM generated medication regimens were then reviewed by a panel of seven critical care clinicians to assess safety and efficacy, as defined by medication errors identified and appropriate treatment for the clinical conditions. Appropriate treatment was measured by the average rate of clinician agreement to continue each medication in the regimen and compared using analysis of variance (ANOVA). Results: Clinicians identified a median of 4.1-6.9 medication errors per recommended regimen, and life-threatening medication recommendations were present in 16.3-57.1% of the regimens, depending on LLM. Clinicians continued LLM-recommended medications at a rate of 54.6-67.3%, with GPT-4 having the highest rate of medication continuation among all LLMs tested (p <0.001) and the lowest rate of life-threatening medication errors (p <0.001). Conclusions: Caution is warranted using present LLMs for medication regimens given the number of medication errors that were identified in this pilot study. However, LLMs did demonstrate potential to serve as clinical decision support for the management of complex medication regimens given the need for domain specific prompting and testing.