Augmenting Interviewer Judgments of Patient Experience with Automatic Language Analysis
Understanding how psychiatric patients subjectively experienced a clinical conversation is important for feedback and alliance-related process monitoring. While interviewers form post-session judgments about patient experience, these judgments do not always match patients' self-reports. Automatic approaches for predicting perceived interaction quality from conversation have been proposed, but it remains unclear whether such approaches can complement human judgment rather than simply replicate it. To address this gap, we evaluate a clinician-support framework in which post-session interviewer ratings are combined with automatic language-based predictions to estimate patient-reported intera...
| Authors | Aowen Shi, Michal Balazia, Danilo Postin et al. |
|---|---|
| Primary category | cs.HC |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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