Sridharan Kannan, Sivaramakrishnan Gowri
Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Bahrain.
Speciality Dental Residency Program, Primary Health Care Centers, Manama, Bahrain.
Explor Res Clin Soc Pharm. 2024 Aug 17;15:100492. doi: 10.1016/j.rcsop.2024.100492. eCollection 2024 Sep.
Medication review and reconciliation is essential for optimizing drug therapy and minimizing medication errors. Large language models (LLMs) have been recently shown to possess a lot of potential applications in healthcare field due to their abilities of deductive, abductive, and logical reasoning. The present study assessed the abilities of LLMs in medication review and medication reconciliation processes.
Four LLMs were prompted with appropriate queries related to dosing regimen errors, drug-drug interactions, therapeutic drug monitoring, and genomics-based decision-making process. The veracity of the LLM outputs were verified from validated sources using pre-validated criteria (accuracy, relevancy, risk management, hallucination mitigation, and citations and guidelines). The impacts of the erroneous responses on the patients' safety were categorized either as major or minor.
In the assessment of four LLMs regarding dosing regimen errors, drug-drug interactions, and suggestions for dosing regimen adjustments based on therapeutic drug monitoring and genomics-based individualization of drug therapy, responses were generally consistent across prompts with no clear pattern in response quality among the LLMs. For identification of dosage regimen errors, ChatGPT performed well overall, except for the query related to simvastatin. In terms of potential drug-drug interactions, all LLMs recognized interactions with warfarin but missed the interaction between metoprolol and verapamil. Regarding dosage modifications based on therapeutic drug monitoring, Claude-Instant provided appropriate suggestions for two scenarios and nearly appropriate suggestions for the other two. Similarly, for genomics-based decision-making, Claude-Instant offered satisfactory responses for four scenarios, followed by Gemini for three. Notably, Gemini stood out by providing references to guidelines or citations even without prompting, demonstrating a commitment to accuracy and reliability in its responses. Minor impacts were noted in identifying appropriate dosing regimens and therapeutic drug monitoring, while major impacts were found in identifying drug interactions and making pharmacogenomic-based therapeutic decisions.
Advanced LLMs hold significant promise in revolutionizing the medication review and reconciliation process in healthcare. Diverse impacts on patient safety were observed. Integrating and validating LLMs within electronic health records and prescription systems is essential to harness their full potential and enhance patient safety and care quality.
用药审查与核对对于优化药物治疗和减少用药错误至关重要。由于大语言模型（LLMs）具有演绎、归纳和逻辑推理能力，最近已显示出在医疗保健领域有许多潜在应用。本研究评估了大语言模型在用药审查和用药核对过程中的能力。
向四个大语言模型提出与给药方案错误、药物相互作用、治疗药物监测和基于基因组学的决策过程相关的适当问题。使用预先验证的标准（准确性、相关性、风险管理、幻觉缓解以及引用和指南）从经过验证的来源验证大语言模型输出的准确性。将错误回复对患者安全的影响分为重大或轻微两类。
在对四个大语言模型关于给药方案错误、药物相互作用以及基于治疗药物监测和基于基因组学的药物治疗个体化的给药方案调整建议的评估中，不同提示下的回复总体一致，各模型之间的回复质量没有明显模式。在识别给药方案错误方面，ChatGPT总体表现良好，但与辛伐他汀相关的问题除外。在潜在药物相互作用方面，所有大语言模型都识别出与华法林的相互作用，但遗漏了美托洛尔和维拉帕米之间的相互作用。在基于治疗药物监测的剂量调整方面，Claude-Instant为两种情况提供了适当建议，为另外两种情况提供了近乎适当的建议。同样，在基于基因组学的决策方面，Claude-Instant在四种情况下提供了令人满意的回复，其次是Gemini在三种情况下。值得注意的是，Gemini即使在没有提示的情况下也会提供指南或引用的参考文献，表明其回复致力于准确性和可靠性。在识别适当的给药方案和治疗药物监测方面发现了轻微影响，而在识别药物相互作用和做出基于药物基因组学的治疗决策方面发现了重大影响。
先进的大语言模型在彻底改变医疗保健中的用药审查和核对过程方面具有巨大潜力。观察到对患者安全有不同影响。在电子健康记录和处方系统中整合和验证大语言模型对于充分发挥其潜力、提高患者安全和护理质量至关重要。
