LLM-Assisted vs Manual Writing for Clinical Documentation: Effects on Time and Quality

Clinical Documentation, Clinician-in-the-loop, Large Language Model

clinician-in-the-loop, Large Language Model, Clinical Documentation, Electronic Health Record

The goal of this clinical trial is to learn whether an LLM-assisted writing workflow can reduce the time to complete hospital discharge summaries and discharge referrals and maintain or improve document quality compared with writing from scratch by clinicians. The study used six simulated patient records (no real patient data). The main questions it aims to answer are: * Does the LLM-assisted writing workflow reduce the time needed to complete each document compared with manual writing? * Does the LLM-assisted writing workflow improve (or at least maintain) document quality compared with manual writing, as rated by blinded experts? Researchers will compare LLM-assisted versus manual writing to see if the LLM-assisted approach is faster and has equal or better quality. LLM-only drafts (unedited first drafts) will be evaluated as a separate third group to understand the baseline quality of LLM output without clinician edits. Participants will create two documents-a discharge summary and a discharge referral-for each of six simulated cases. Those assigned to CocktailAI & Modification group will use an LLM assistant (called CocktailAI) to generate a first draft for each document and then review and edit it to finalize; those assigned to the control group will write each document from scratch without LLM assistance.

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