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arXiv · 2505.04152

SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation

Manas Satish Bedmutha·Feng Chen·Andrea Hartzler·Trevor Cohen·Nadir Weibel
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Abstract

Effective patient-provider communication is difficult to assess at scale. We examine whether large language models (LLMs) can track 20 social behaviors from clinical transcripts without fine-tuning. Across three model families and multiple prompting strategies, LLMs reliably detect social signals, though performance varies by patient race and visit segment. To address this variability under query-only API constraints, we introduce an agreement-weighted ensemble using group-level agreement patterns. This approach improves both accuracy and stability over the best individual model, demonstrating a practical pathway for scalable social signal tracking in clinical conversations.

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Manas Satish Bedmutha, Feng Chen, Andrea Hartzler, Trevor Cohen, Nadir Weibel. 2025-05-07. SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation. https://arxiv.org/abs/2505.04152

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