Conversation as Measurement in Clinical Encounters: Observable Phase Structure, Partially Observable Patient State
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...
| Authors | Lily Chen, Ted Mau, Michael Gensheimer et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 August 2026 |
| Last updated at source | 11 August 2026 |
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