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Enhancing Interdisciplinary Understanding of Ophthalmology Notes Through a Local Large Language Model

NCT06624605 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 851

Last updated 2024-10-03

No results posted yet for this study

Summary

This prospective, randomized controlled trial evaluated the efficacy of adding Large Language model (LLM)-generated Plain Language Summaries (PLSs) to Standard Ophthalmology Notes (SONs) in enhancing comprehension among non-ophthalmology providers. The study utilized surveys to assess non-ophthalmology providers\' comprehension and satisfaction with the notes and ophthalmologists\' evaluation of PLS accuracy, safety, and time burden. An objective semantic and linguistic analysis of the PLSs was also conducted.

Conditions

  • Communication
  • Artificial Intelligence (AI)
  • Artificial Intelligence Technology
  • Interdisciplinary Communication

Interventions

OTHER

Large Language Model-generated Plain Language Summary of Ophthalmology notes

Prospective, randomized Quality Improvement study with real-world implementation of Large Language Model-generated Plain Language Summaries of Ophthalmology notes.

Sponsors & Collaborators

  • John J Chen

    lead OTHER

Principal Investigators

  • John J Chen, MD, PhD · Mayo Clinic

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-02-01
Primary Completion
2024-05-31
Completion
2024-05-31

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06624605 on ClinicalTrials.gov