用ChatGPT生成针对心衰患者的个性化对话，提升自我照护沟通质量。
Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failure
- 设计四种提示策略，融合文化语境与社会健康因素。
- 对话质量随提示设计优化而提升，但情感共鸣仍不足。
- 适合医疗对话系统开发者及健康数字干预研究者。
我们探索了ChatGPT（3.5-turbo和4）在生成聚焦于非裔美国人心衰患者自我照护策略对话方面的潜力——该领域缺乏专用数据集。为模拟患者与健康教育者的对话，采用了四种提示策略：领域知识、非裔美国人方言（AAVE）、社会健康决定因素（SDOH）及基于SDOH的推理。对话覆盖饮食、运动和液体摄入三大自我照护领域，采用5、10、15轮次长度，并融入患者特定的SDOH属性如年龄、性别、居住地及经济状况。研究发现，有效提示设计至关重要。尽管引入SDOH和推理可提升对话质量，但ChatGPT仍缺乏医疗沟通所需的情感共情与参与感。
原文摘要 · Abstract (English)
We explore the potential of ChatGPT (3.5-turbo and 4) to generate conversations focused on self-care strategies for African-American heart failure patients -- a domain with limited specialized datasets. To simulate patient-health educator dialogues, we employed four prompting strategies: domain, African American Vernacular English (AAVE), Social Determinants of Health (SDOH), and SDOH-informed reasoning. Conversations were generated across key self-care domains of food, exercise, and fluid intake, with varying turn lengths (5, 10, 15) and incorporated patient-specific SDOH attributes such as age, gender, neighborhood, and socioeconomic status. Our findings show that effective prompt design is essential. While incorporating SDOH and reasoning improves dialogue quality, ChatGPT still lacks the empathy and engagement needed for meaningful healthcare communication.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。