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Completed

NCT Number: NCT07187050

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

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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Key information

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Department of Ophthalmology and Visual Sciences Kyoto University Graduate School of Medicine 54 Shogoin, Kawahara, Sakyo

Kyoto, 606-8507, Japan

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Template-Based LLM Assistant

Other

This study uses CocktailAI, a template-based LLM assistant co-developed by the Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, and Fitting Cloud Inc. (Kyoto, Japan). It is designed to extract relevant information from EHRs using LLMs and embed the extracted content into predefined templates. In this trial, the inputs are six simulated patient records (no real patient data). Text generation uses Gemini-2.0-flash-lite. Templates for discharge summaries and discharge referrals are pre-defined by a team member.

Manual Writing

Other

The same document templates are provided; however, all LLM instruction prompts are removed in advance. Clinicians manually write the documents, following the template structure, for each of the six simulated cases.

Primary outcomes

  1. Average time spent creating each document

    Time frame: On one study day within 2 weeks after enrollment

    In the CocktailAI & Modification group and the Control group, the time spent creating documents is measured in seconds. In the CocktailAI group, the time required for document generation is measured in seconds.

Secondary outcomes

  1. Document quality assessment

    Time frame: On one study day within 2 weeks after enrollment

    Blinded to group allocation, ophthalmology experts evaluate the documents using pre-specified criteria defined before study initiation. These criteria are developed based on six domains: Medical Accuracy, Language, Conciseness, Presence of Hallucinations, Validity for Clinical Use, and Possibility of Harm. Most domains are assessed on a three-point scale, whereas Presence of Hallucinations is evaluated dichotomously (present or absent). In addition to these domain-specific ratings, experts provide a subjective overall score on a 10-point scale (with higher scores indicating better quality) and are asked to guess which study group the document belongs to.

Sponsors and collaborators

Lead sponsor

Kyoto University, Graduate School of Medicine

Other

Collaborators

  • Fitting Cloud Inc.

Registry information

Official study title

Evaluating an LLM-Assisted Workflow for Clinical Documentation: A Pilot Randomized Controlled Trial on Time and Quality

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Sep 22, 2025
Registry last updated
Sep 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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