What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform

2026-02-19

AI Summary

This study addresses the challenge in text-based telemedicine where patient satisfaction often reflects perceived communication quality rather than clinical accuracy, complicating physicians’ efforts to balance professionalism with favorable evaluations. Leveraging 77,334 anonymized Romanian-language physician–patient dialogues, the authors construct an interpretable, temporally partitioned classification model using patient “likes” as positive feedback labels. The model integrates language-agnostic features, LIWC-based psycholinguistic metrics, and markers of politeness and hedging. SHAP analysis reveals that historical interaction patterns serve as strong priors for predicting satisfaction, while textual features—such as polite expressions (positively associated) and lexical diversity (negatively associated)—exert smaller yet actionable influence. This work represents the first systematic investigation in Romanian-language telemedicine to uncover associations between linguistic style and patient feedback, offering data-driven insights for optimizing clinician–patient communication.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Text-based telemedicine has become a common mode of care, requiring clinicians to deliver medical advice clearly and effectively in writing. As platforms increasingly rely on patient ratings and feedback, clinicians face growing pressure to maintain satisfaction scores, even though these evaluations often reflect communication quality more than clinical accuracy. We analyse patient satisfaction signals in Romanian text-based telemedicine. Using a sample of 77,334 anonymised patient question--doctor response pairs, we model feedback as a binary outcome, treating thumbs-up responses as positive and grouping negative or absent feedback into the other class. We extract interpretable, predominantly language-agnostic features (e.g., length, structural characteristics, readability proxies), along with Romanian LIWC psycholinguistic features and politeness/hedging markers where available. We train a classifier with a time-based split and perform SHAP-based analyses, which indicate that patient and clinician history features dominate prediction, functioning as strong priors, while characteristics of the response text provide a smaller but, crucially, actionable signal. In subgroup correlation analyses, politeness and hedging are consistently positively associated with patient feedback, whereas lexical diversity shows a negative association.

Problem

• telemedicine
• patient satisfaction
• doctor-patient communication
• text-based care
• feedback analysis

Innovation

• telemedicine
• patient satisfaction
• interpretable features
• politeness markers
• SHAP analysis