Huibo Yang ¹ Mengxuan Hu ¹ Amoreena Most ² Brian Murray ³ Susan Smith ² Anthony Hawkins ² Sheng Li ¹ Andrea Sikora ^2,3*

• ¹ University of Virginia, Charlottesville, Virginia, United States
• ² College of Pharmacy, University of Georgia, Athens, United States
• ³ University of Colorado, Denver, Colorado, United States

Background: Large language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evaluation was to test various LLMs performance optimization strategies and performance on critical care pharmacotherapy questions used in the assessment of Doctor of Pharmacy students.In a comparative analysis using 219 multiple-choice pharmacotherapy questions, five LLMs (GPT-3.5, GPT-4, Claude 2, Llama2-7b and 2-13b) were evaluated. Each LLM was queried five times to evaluate the primary outcome of accuracy (i.e., correctness) and variance. The secondary outcomes explored impact of prompt engineering techniques (e.g., chain-ofthought, CoT), training of a customized GPT on performance, and comparison to third year doctor of pharmacy students on knowledge recall vs. knowledge application questions. Accuracy and variance were compared with student's t-test to compare performance under different model settings.Results: ChatGPT-4 exhibited the highest accuracy (71.6%), while Llama2-13b had the lowest variance (0.070). All LLMs performed more accurately on knowledge recall vs. knowledge application questions (e.g., ChatGPT-4: 87% vs 67%). When applied to ChatGPT-4, few-shot CoT across five runs improved accuracy (77.4% vs 71.5%) with no effect on variance. Selfconsistency and the custom-trained GPT demonstrated similar accuracy to ChatGPT-4 with fewshot CoT. Overall pharmacy student accuracy was 81%, compared to an optimal overall LLM accuracy of 73%. Comparing question types, six of the LLMs demonstrated equivalent or higher accuracy than pharmacy students on knowledge recall questions (e.g., self-consistency vs. students: 93% vs 84%), but pharmacy students achieved higher accuracy than all LLMs on knowledge application questions (e.g., self-consistency vs. students: 68% vs 80%).Conclusions: ChatGPT-4 was the most accurate LLM on critical care pharmacy questions and few-shot CoT improved accuracy the most. Average student accuracy was similar to LLMs overall, and higher on knowledge application questions. These findings support the need for future assessment of customized training for the type of output needed. Reliance on LLMs is only supported with recall-based questions.