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.

翻译：基于文本的远程医疗已成为常见的诊疗模式，这要求临床医生以书面形式清晰有效地提供医疗建议。随着平台日益依赖患者评分与反馈，临床医生面临维持满意度分数的持续压力，尽管这些评价往往更多反映沟通质量而非临床准确性。本研究分析了罗马尼亚文本远程医疗中的患者满意度信号。基于77,334组匿名患者提问-医生回复配对样本，我们将反馈建模为二元结果：将点赞回复视为正面反馈，将负面或缺失反馈归为另一类别。我们提取了可解释且主要与语言无关的特征（如长度、结构特征、可读性代理指标），同时结合罗马尼亚语LIWC心理语言学特征以及可获取的礼貌/模糊限制语标记。采用基于时间的分割训练分类器并进行SHAP分析，结果表明：患者与临床医生的历史特征主导预测，发挥强先验作用；而回复文本特征虽贡献较小，但提供了关键且可操作的信号。在子群相关性分析中，礼貌表达与模糊限制语始终与患者反馈呈正相关，而词汇多样性则呈现负相关。