Improving Medical Communication using Rubric-Guided Counterfactual Recommendations
For telemedicine platforms, this work provides a method to enhance patient satisfaction without compromising medical accuracy, though the gains are modest and domain-specific.
The paper introduces a rubric-guided counterfactual recommendation pipeline that improves patient feedback in text-based telemedicine by suggesting minimal, interpretable communication changes (e.g., tone, personalization). The system achieves a mean +6.41% gain in predicted positive feedback probability under independent auditors, with 93.31% of recommendations being non-negative.
Text-based telemedicine increasingly relies on lightweight patient feedback, however, such feedback primarily reflects perceived communication quality rather than medical accuracy. We introduce an LM-guided counterfactual recommendation pipeline that discovers and refines interpretable communication features such as tone, personalization, actionability and completeness in addressing patient concerns, without interfering with the medical content. These features are used together with patient-doctor interaction metadata to estimate positive feedback. At inference time, the system searches over low-cost ordinal feature changes and recommends minimal communication changes predicted to increase the probability of positive feedback, while independent auditor models test whether these gains generalize beyond the selection model. Across interactions, recommendations yield a mean +6.41% gain in predicted positive feedback probability under independent auditors, and are non-negative for 93.31% of recommendations. These results suggest that small, interpretable communication changes can capture most predicted gains while preserving the doctor's control over medical reasoning and final wording.