What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform
arXiv:2602.17194v1 Announce Type: new 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
arXiv:2602.17194v1 Announce Type: new 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.
Executive Summary
This article contributes to the growing body of research on text-based telemedicine by analyzing patient satisfaction signals in Romanian text-based telemedicine. The authors use a dataset of 77,334 anonymized patient-question-doctor response pairs to model feedback as a binary outcome. Their findings indicate that patient and clinician history features dominate prediction, while characteristics of the response text provide a smaller but actionable signal. Notably, politeness and hedging are positively associated with patient feedback, whereas lexical diversity is negatively associated. The study sheds light on the importance of effective communication in telemedicine, particularly in the context of Romanian text-based platforms.
Key Points
- ▸ Patient and clinician history features dominate prediction in modeling patient satisfaction in text-based telemedicine.
- ▸ Characteristics of the response text provide a smaller but actionable signal in predicting patient satisfaction.
- ▸ Politeness and hedging are positively associated with patient feedback in text-based telemedicine.
Merits
Strength
The use of a large and diverse dataset (77,334 anonymized patient-question-doctor response pairs) provides robust findings and increases the generalizability of the results.
Methodological rigor
The authors employ a time-based split and SHAP-based analyses to ensure the validity and reliability of their results.
Interdisciplinary approach
The integration of language-agnostic features, Romanian LIWC psycholinguistic features, and politeness/hedging markers highlights the importance of an interdisciplinary approach in understanding patient satisfaction in text-based telemedicine.
Demerits
Limitation
The study's focus on patient satisfaction in Romanian text-based telemedicine may limit the generalizability of the findings to other languages or telemedicine platforms.
Bias and variability
The authors acknowledge that patient ratings and feedback may reflect communication quality more than clinical accuracy, which may introduce bias and variability in the results.
Lack of clinical accuracy analysis
The study does not investigate the relationship between clinical accuracy and patient satisfaction, which is an important aspect of telemedicine evaluation.
Expert Commentary
The article provides valuable insights into the importance of effective communication in text-based telemedicine, particularly in the context of Romanian text-based platforms. However, the study's limitations, such as the focus on patient satisfaction in Romanian text-based telemedicine and the lack of clinical accuracy analysis, should be acknowledged. Nevertheless, the findings have significant implications for healthcare providers, telemedicine platforms, and policymakers. The study underscores the need for an interdisciplinary approach in understanding patient satisfaction in text-based telemedicine, highlighting the importance of language-agnostic features, Romanian LIWC psycholinguistic features, and politeness/hedging markers. Future research should aim to investigate the relationship between clinical accuracy and patient satisfaction, as well as explore the generalizability of the findings to other languages or telemedicine platforms.
Recommendations
- ✓ Future research should investigate the relationship between clinical accuracy and patient satisfaction in text-based telemedicine.
- ✓ Healthcare providers and telemedicine platforms should prioritize effective communication in text-based telemedicine, particularly in the context of Romanian text-based platforms.
