Trials / Completed
CompletedNCT06624605
Enhancing Interdisciplinary Understanding of Ophthalmology Notes Through a Local Large Language Model
Enhancing Interdisciplinary Understanding of Ophthalmology Notes Through Local Large Language Model
- Status
- Completed
- Phase
- N/A
- Study type
- Interventional
- Enrollment
- 851 (actual)
- Sponsor
- John J Chen · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
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
| Type | Name | Description |
|---|---|---|
| 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. |
Timeline
- Start date
- 2024-02-01
- Primary completion
- 2024-05-31
- Completion
- 2024-05-31
- First posted
- 2024-10-03
- Last updated
- 2024-10-03
Locations
1 site across 1 country: United States
Source: ClinicalTrials.gov record NCT06624605. Inclusion in this directory is not an endorsement.