Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.

翻译：文档负担是导致临床医生职业倦怠的主要因素，这一问题在全国范围内日益加剧，并对我们照护患者的能力构成紧迫威胁。人工智能（AI）聊天机器人（如ChatGPT）可通过辅助文档处理减轻临床医生负担。尽管许多医院正在积极将这些系统整合到电子病历系统中，但AI聊天机器人在这一预期用途中的实用性及其对临床决策的影响尚未得到研究。我们首次探究了大语言模型在协助临床医生起草患者问题回复中的效用。在两阶段横断面研究中，6名肿瘤科医生首先手动、随后在AI辅助下对100个模拟常见医疗场景的合成癌症患者案例及门户消息进行回复。研究发现：AI辅助的回复更长、可读性更低，但58%的情况下无需编辑即可提供可接受的草稿。AI辅助在77%的情况下提升了效率，且伤害风险较低（82%安全）。然而，7.7%未经编辑的AI回复可能造成严重伤害。在31%的案例中，医生认为AI草稿由人类撰写。与手动回复相比，AI辅助产生了更多患者教育建议，但减少了临床措施。研究结果表明，若能谨慎使用，AI通过辅助文档处理有望提升临床医生效率和患者照护质量。但监测模型输出及人机交互对安全实施仍然至关重要。