How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting
This addresses the challenge of integrating LLMs into clinical workflows to save clinician time, but it is incremental as it builds on existing adaptation techniques.
The study tackled the problem of aligning LLM-drafted responses to patient messages with individual clinician preferences, finding substantial uncertainty in alignment, particularly in generating questions to elicit patient information, though theme-driven adaptation strategies improved performance across most themes.
Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether they would actually save clinicians time and effort in their portal workload. We investigate LLM alignment with individual clinicians through a comprehensive evaluation of the patient message response drafting task. We develop a novel taxonomy of thematic elements in clinician responses and propose a novel evaluation framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. We release an expert-annotated dataset and conduct large-scale evaluations of local and commercial LLMs using various adaptation techniques including thematic prompting, retrieval-augmented generation, supervised fine-tuning, and direct preference optimization. Our results reveal substantial epistemic uncertainty in aligning LLM drafts with clinician responses. While LLMs demonstrate capability in drafting certain thematic elements, they struggle with clinician-aligned generation in other themes, particularly question asking to elicit further information from patients. Theme-driven adaptation strategies yield improvements across most themes. Our findings underscore the necessity of adapting LLMs to individual clinician preferences to enable reliable and responsible use in patient-clinician communication workflows.