Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT’s performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners’ deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT’s answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT’s deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

对于具有广泛医疗复杂性的患者来说，多重用药仍然是一个重要的挑战。鉴于初级保健短缺和人口老龄化日益严重，有效的多药管理对于管理日益增加的护理负担至关重要。大语言模型的容量（LLM基于人工智能来辅助复方药物管理尚未得到评估。在这里，我们通过标准化临床片段中的取消处方决策来评估 ChatGPT 在多药管理方面的表现。我们将一些最初来自全科医生取消处方决定的研究的临床片段输入到 ChatGPT 3.5（一个公开可用的）中。LLM ，并评估了其做出是/否二元取消处方决策的能力以及基于列表的提示，其中提示模型选择要取消处方的几种药物中的哪一种。我们记录了 ChatGPT 对是/否二元取消处方提示的响应以及取消处方的药物数量和类型。在是/否二元取消处方决策中，ChatGPT 普遍建议对没有叠加 CVD 病史的患者取消药物处方，无论 ADL 状态如何；对于有 CVD 病史的患者，ChatGPT 的答案因技术重复而异。停用的药物总数为 2.67 至 3.67 种（共 7 种），并且不随 CVD 状态而变化，但随着 ADL 损伤的严重程度线性增加。在药物类型中，ChatGPT 优先停用止痛药。 ChatGPT 的取消处方决策沿着 ADL 状态、CVD 病史和药物类型的轴变化，表明全科医生和模型之间的内部逻辑存在一定的一致性。 这些结果表明经过专门训练LLMs可以为初级保健医生的多药管理提供有用的临床支持。