Problem
Research questions and friction points this paper is trying to address.
patient experience
interviewer judgments
automatic language analysis
Augmenting Interviewer Judgments of Patient Experience with Automatic Language Analysis
🤖 AI Summary
研究通过结合访谈者判断与自动语言分析来更准确地评估精神科患者对临床对话的主观体验，使用多种模型（如Ridge、BiLSTM）处理对话文本以预测互动质量。
📝 Abstract
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 interaction quality in free clinical interviews. We assess this integration across multiple standard model types, including Ridge, SVR, MLP, GRU, and BiLSTM, all trained on sentence embeddings extracted from dyadic transcripts of 107 free conversations between psychiatric patients and interviewers. Our results show that combining interviewer judgments with model predictions through simple averaging yields the strongest overall performance. The interviewer-only baseline reached a Pearson correlation of 0.365. Among fully automatic models, Ridge achieved the strongest Pearson correlation (r = 0.286), while BiLSTM achieved r = 0.270. The strongest result was obtained by BiLSTM interviewer integration (r = 0.403). Our findings suggest that automatic language analysis and interviewer judgment capture complementary aspects of patient experience and that their combination provides a more accurate approximation of the patient's own report than either source alone.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automatic Language Analysis
Interviewer Judgments
Patient-Reported Interaction Quality
Model Integration
BiLSTM
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