Department of Surgery, University of Florida, Gainesville, FL.
Department of Medicine, University of Florida, Gainesville, FL.
Crit Care Explor. 2024 Aug 12;6(8):e1131. doi: 10.1097/CCE.0000000000001131. eCollection 2024 Aug 1.
Surrogates, proxies, and clinicians making shared treatment decisions for patients who have lost decision-making capacity often fail to honor patients' wishes, due to stress, time pressures, misunderstanding patient values, and projecting personal biases. Advance directives intend to align care with patient values but are limited by low completion rates and application to only a subset of medical decisions. Here, we investigate the potential of large language models (LLMs) to incorporate patient values in supporting critical care clinical decision-making for incapacitated patients in a proof-of-concept study.
We simulated text-based scenarios for 50 decisionally incapacitated patients for whom a medical condition required imminent clinical decisions regarding specific interventions. For each patient, we also simulated five unique value profiles captured using alternative formats: numeric ranking questionnaires, text-based questionnaires, and free-text narratives. We used pre-trained generative LLMs for two tasks: 1) text extraction of the treatments under consideration and 2) prompt-based question-answering to generate a recommendation in response to the scenario information, extracted treatment, and patient value profiles. Model outputs were compared with adjudications by three domain experts who independently evaluated each scenario and decision.
Automated extractions of the treatment in question were accurate for 88% (n = 44/50) of scenarios. LLM treatment recommendations received an average Likert score by the adjudicators of 3.92 of 5.00 (five being best) across all patients for being medically plausible and reasonable treatment recommendations, and 3.58 of 5.00 for reflecting the documented values of the patient. Scores were highest when patient values were captured as short, unstructured, and free-text narratives based on simulated patient profiles. This proof-of-concept study demonstrates the potential for LLMs to function as support tools for surrogates, proxies, and clinicians aiming to honor the wishes and values of decisionally incapacitated patients.
代理人、代表和临床医生在为失去决策能力的患者做出共同治疗决策时，由于压力、时间压力、误解患者价值观以及投射个人偏见，往往无法尊重患者的意愿。预先指示旨在使护理与患者价值观保持一致，但由于完成率低且仅适用于部分医疗决策，其应用受到限制。在这里，我们通过概念验证研究调查了大型语言模型 (LLM) 在支持丧失决策能力的患者进行关键护理临床决策方面纳入患者价值观的潜力。
我们为 50 名决策能力丧失的患者模拟了基于文本的场景，这些患者的病情需要对特定干预措施进行紧急临床决策。对于每位患者，我们还使用替代格式模拟了五个独特的价值档案：数字排名问卷、基于文本的问卷和自由文本叙述。我们使用预训练的生成式 LLM 完成了两项任务：1）提取正在考虑的治疗方法的文本；2）基于提示的问答，根据情景信息、提取的治疗方法和患者价值档案生成推荐。将模型输出与由三位领域专家进行的裁决进行比较，每位专家独立评估每个场景和决策。
对于 50 个场景中的 88%（n=44/50），问题治疗方法的自动提取是准确的。对于所有患者，LLM 治疗建议的平均得分由裁判给出 3.92 分（五分制，五分最好），认为这些建议在医学上是合理的；对于反映患者记录的价值，平均得分为 3.58 分。当患者价值是基于模拟患者档案的简短、非结构化和自由文本叙述时，评分最高。这项概念验证研究表明，大型语言模型有潜力作为代理人、代表和临床医生的支持工具，旨在尊重丧失决策能力的患者的意愿和价值观。
