Harvard Medical School, Boston, MA, USA.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA.
J Med Syst. 2024 Apr 18;48(1):41. doi: 10.1007/s10916-024-02058-y.
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.
药物治疗方案数量过多仍然是患有多种严重疾病的患者面临的一个重要挑战。鉴于初级保健的短缺和人口老龄化的加剧，有效的药物治疗方案管理对于减轻不断增加的医疗负担至关重要。基于大型语言模型（LLM）的人工智能在药物治疗方案管理中的辅助作用尚未得到评估。在这里，我们通过其在标准化临床病例中的撤药决策来评估 ChatGPT 在药物治疗方案管理方面的性能。我们将几个最初来自全科医生撤药决策研究的临床病例输入到 ChatGPT 3.5 中，这是一个公开可用的 LLM，并评估了它进行是/否二进制撤药决策的能力，以及在提示模型选择要撤药的几种药物的基于列表的提示。我们记录了 ChatGPT 对是/否二进制撤药提示的反应，以及撤药的药物数量和类型。在是/否二进制撤药决策中，ChatGPT 普遍建议撤药，无论患者的日常生活活动（ADL）状态如何，也无论是否有潜在的心血管疾病（CVD）病史；在有 CVD 病史的患者中，ChatGPT 的答案因技术重复而有所不同。撤药的药物总数从 2.67 到 3.67（共 7 种）不等，与 CVD 状态无关，但随着 ADL 损害的严重程度呈线性增加。在药物类型中，ChatGPT 优先撤掉止痛药。ChatGPT 的撤药决策沿 ADL 状态、CVD 病史和药物类型的轴变化，表明一般从业者和模型之间存在一些内在逻辑的一致性。这些结果表明，经过专门训练的 LLM 可能为初级保健医生的药物治疗方案管理提供有用的临床支持。
