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

Kyoto University, Graduate School of Medicine

Status: Completed

Conditions: Clinical Documentation; Clinician-in-the-loop; Large Language Model

Treatments: Other: Template-Based LLM Assistant; Other: Manual Writing

Study type: Interventional

Funder types: Other

Identifiers

NCT07187050

clinician-in-the-loop_workflow

Details and patient eligibility

About

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.

Enrollment: 21 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

Inclusion Criteria:

• Ophthalmologists at Kyoto University Hospital
• Junior residents, senior residents, graduate students, board-certified ophthalmologists
• Physicians who confirm that they do not routinely use CocktailAI for clinical documentation and provide informed consent after receiving an explanation of the study.

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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21 participants in 3 patient groups

CocktailAI & Modification arm

Experimental group

Description:

Participants log into the study web application using a unique ID and their name. For each simulated patient case, they press "Start" to unlock the case record and initiate timing. After viewing the patient information, clinicians use CocktailAI to generate a first draft of the discharge summary. They then review and edit the draft and submit the final document by pressing "Submit." For the discharge referral of the same case, clinicians follow the same procedure. Copy-and-paste from the case record is permitted. This sequence is repeated for six simulated cases.

Treatment:

Other: Template-Based LLM Assistant

Control arm

Active Comparator group

Description:

Participants log into the study web application using a unique ID and their name. For each simulated patient case, they press "Start" to unlock the case record and initiate timing. After viewing the patient information, clinicians write the discharge summary from scratch and submit the document by pressing "Submit." They then write the discharge referral for the same case from scratch and submit it. Copy-and-paste from the case record is permitted. This sequence is repeated for six simulated cases.

Treatment:

Other: Manual Writing

CocktailAI arm

Active Comparator group

Description:

The LLM assistant generates drafts of discharge summaries and discharge referrals directly from the simulated patient records, with no clinician review or edits. These drafts are the unedited first drafts produced in the CocktailAI \& Modification arm (captured before any clinician edits) and are included as a separate group.

Treatment:

Other: Template-Based LLM Assistant