Improving Medical Communication using Rubric-Guided Counterfactual Recommendations

2026-06-12 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

An LM-guided counterfactual recommendation pipeline has been developed to enhance communication quality in text-based telemedicine without altering medical content. This system identifies and refines interpretable communication features like tone, personalization, actionability, and completeness using Language Models and Automatic Prompt Optimization. These features, combined with patient-doctor interaction metadata, estimate positive patient feedback. At inference, the pipeline recommends minimal, low-cost ordinal feature changes predicted to increase positive feedback probability. Independent auditor models validate these recommendations, showing a mean +6.41% gain in predicted positive feedback probability and non-negative changes for 93.31% of recommendations. The policy model achieved a 71.5% ROC-AUC, demonstrating that small, interpretable communication adjustments can capture significant predicted gains.

Key takeaway

For telemedicine platform developers or clinicians aiming to boost patient satisfaction, this system offers a precise method to improve communication without compromising medical accuracy. You should consider integrating counterfactual recommendation tools that identify specific, low-effort communication adjustments, such as enhancing response tone or personalization. This approach allows you to retain full control over medical content while significantly increasing positive patient feedback, as demonstrated by a +6.41% average gain.

Key insights

An LM-guided pipeline improves telemedicine communication by recommending minimal, interpretable feature changes for higher patient satisfaction.

Principles

• Patient feedback reflects communication quality, not medical accuracy.
• Interpretable communication features drive patient satisfaction.
• Small, targeted edits yield most feedback improvement.

Method

The pipeline uses LMs for dataset-level feature discovery and prompt refinement, trains a CatBoost feedback estimator, then searches for budget-constrained ordinal feature changes to maximize predicted positive feedback.

In practice

• Focus on response tone, personalization, and addressing patient concerns.
• Use independent auditor models to validate recommendation robustness.
• Prioritize low-cost communication edits for efficiency.

Topics

• Telemedicine Communication
• Counterfactual Recommendations
• Language Models
• Automatic Prompt Optimization
• Patient Feedback
• Explainable AI

Best for: AI Engineer, Research Scientist, AI Product Manager, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.