研究显示对话式 Text-to-SQL 界面更受医生认可，SUS 分数升至 85.9
Conversational Interaction for LLM-Based Clinical Text-to-SQL: A User Study with Healthcare Professionals
14 名医生实测了对话式 vs 单轮的病历查询界面，93% 更满意对话式，还发现基准测试完全低估了真实查询的模糊度，做医疗 AI 的都该看看。
14 名经常使用电子健康记录（EHR）的医生在 MIMIC-IV 数据库上测试了单轮与多轮对话式 text-to-SQL 界面。多轮对话式界面的 System Usability Scale 平均分从 68.2 升至 85.9（p<0.001）。在强制选择比较中，93% 的参与者对对话式界面整体满意度更高，86% 更愿意在实际工作中采用它。425 条真实用户查询记录中，自由撰写的查询有 24.8% 触发了澄清请求，而预设的完整问题只有 1.8%；64.3% 的澄清涉及 EHR 或临床领域知识。研究表明基准测试式评估低估了真实临床查询的模糊性，对话式交互是自然语言访问 EHR 的必要条件。
Conversational Interaction for LLM-Based Clinical Text-to-SQL: A User Study with Healthcare Professionals
Conversational text-to-SQL interfaces could give healthcare professionals natural-language access to electronic health record (EHR) data, but evidence for their real-world usability remains limited: prior evaluations have relied largely on curated benchmarks with fully specified questions or on simulated users. We report a user study in which 14 physicians who regularly work with EHR data used both a single-turn and a conversational multi-turn text-to-SQL interface over the MIMIC-IV database in a within-subjects design; dialogue analyses additionally drew on interaction logs from 21 healthcare professionals across the main study and two pilot studies. The conversational interface was consistently favored: mean System Usability Scale scores rose from 68.2, near the industry average, to 85.9 (p<0.001), with significant advantages across technology-acceptance constructs including performance expectancy, effort expectancy, self-efficacy, and behavioral intention. In post-study forced-choice comparisons, 93% of participants reported greater overall satisfaction with the conversational interface, 86% preferred it for real-world adoption, and 71% trusted its results more; no participant preferred or trusted the single-turn interface more. Turn-level analysis of 425 logged user turns showed that freely composed queries triggered clarification requests in 24.8% of cases versus 1.8% for predefined, fully specified questions, and 64.3% of clarification episodes stemmed from EHR- or clinical-domain knowledge. Conversational interaction is a practical prerequisite, rather than a convenience, for natural-language EHR access. The frequent need for clarification in realistic clinical queries further underscores the importance of conversational interfaces, while benchmark-style evaluation can substantially underrepresent the ambiguity of real clinical use.
