发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以临床问诊为场景，利用439份真实转录文本，发现对话阶段结构可观测且有用，而患者状态仅部分可观测，警示勿仅靠对话转录推断人类状态。
AI 中文摘要
许多现代AI系统分析对话轨迹以推断人类互动与状态的各个方面，隐含假设这类信息可从对话中恢复。我们研究可观测性：仅从对话转录文本能否恢复目标信息。可观测性难以评估，因为转录文本可能仅提供许多目标的部分视图，且大规模分析需基于模型的标注，导致难以区分对话信号的真实极限与标注者误差。因此我们研究临床问诊，其中患者报告结局测量（PROMs）为患者状态提供外部锚点，且问诊遵循大致结构化模式。我们使用涵盖134小时的439份真实世界临床问诊转录文本（包括245份耳鼻喉科（ENT）转录文本与273份PROM调查配对数据），研究患者状态与对话阶段结构的可观测性。我们用PROM评分（针对声音、咳嗽和吞咽）操作化患者状态，用对话阶段分割操作化阶段结构。为使这些分析在大规模下可信，我们使用符合PHI标准的GPT-5部署进行转录文本标注，并开展40小时人工验证，降低可观测性的明显极限仅反映标注者误差的风险。我们的核心发现是可观测性不对称性：阶段结构可观测且对表征临床问诊组织有用，而患者状态仅部分可观测，即便在旨在引出患者症状与体验的场景中，这警示人们勿仅通过转录文本推断人类状态。
英文摘要
Many modern AI systems analyze conversational traces to infer aspects of human interaction and state, implicitly assuming that such information is recoverable from conversation. We study observability: whether a target is recoverable from conversational transcripts alone. Observability is difficult to assess because transcripts may provide only a partial view of many targets, and large-scale analysis requires model-based annotation, making true limits of the conversational signal hard to distinguish from annotator error. We therefore study clinical encounters, where patient-reported outcome measures (PROMs) provide an external anchor for patient state, and visits follow broadly structured patterns. We study observability of patient state and conversational phase structure using 439 real-world clinical encounter transcripts spanning 134 hours, including 245 ENT transcripts paired with 273 PROM surveys. We operationalize patient state using PROM scores for voice, cough, and swallowing; phase structure using conversational phase segmentation. To make these analyses credible at scale, we use a PHI-compliant GPT-5 deployment for transcript annotation and conduct 40 hours of manual validation, reducing the risk that apparent limits of observability simply reflect annotator error. Our core finding is an observability asymmetry: phase structure is observable and useful for characterizing clinical encounter organization, while patient state is only partially observable, even in a setting designed to elicit patient symptoms and experiences, cautioning against transcript-only inference of human state.
CommentsCOLM 2026
