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Improving Medical Communication using Rubric-Guided Counterfactual Recommendations

arXiv 2606.18889 ↗First posted 17 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

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.

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References · 17

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5 more
  1. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  2. 2024Robust Counterfactual Explanations in Machine Learning: A Surveycited by 29
  3. 2024An LLM Feature-based Framework for Dialogue Constructiveness Assessmentcited by 9
  4. 2025Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romaniancited by 4
  5. 2026Automatic Prompt Optimization for Dataset-Level Feature Discoverycited by 2

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