Evaluating AI-Generated Plain Language Summaries on Patient Comprehension of Ophthalmology Notes Among English-Speaking Patients

This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better. Adults receiving eye care at the Jules Stein Eye Institute will get either the usual medical notes or a note with the addition of an AI-generated summary that explains the information in simple, everyday words. Participants will then answer a short survey and receive a follow-up call to share how clear the information was, how well they understood their diagnosis and treatment, and whether they feel more confident about their care. The goal is to find out if these plain language summaries can make it easier for people to understand their eye care and improve communication between patients and health care providers.

This study employs a two-arm randomized controlled trial to evaluate whether artificial intelligence (AI)-generated plain language summaries (PLSs) can improve patient comprehension of ophthalmology notes. Eligible participants are recruited during their routine visits at the Jules Stein Eye Institute, and once screened using standardized clinical criteria, they are randomly assigned to either receive the standard ophthalmology note (SON) or the SON supplemented with an AI-generated PLS. The randomization process uses a computer-generated sequence with concealed allocation to ensure unbiased group assignment.

The AI system used in this study is deployed locally on a secured UCLA intranet. It leverages a large language model (LLM) that has been customized and validated for generating plain language explanations of complex ophthalmologic information. All processing occurs on UCLA-approved, encrypted devices, and no data are transmitted externally. Before the PLS is provided to participants, each summary is reviewed by an ophthalmologist to verify accuracy and ensure that essential clinical details are correctly and clearly communicated.

Data collection is performed using survey instruments. The survey includes a series of 5-point Likert scale items, open-ended questions, and structured response sections designed to assess comprehension of diagnosis, treatment plans, and follow-up instructions. Participants complete the survey immediately after their clinic visit, and a follow-up telephone interview is conducted approximately seven days later by trained research staff to capture additional feedback on clarity and retention of the information provided. The study does not employ audio or video recording; all responses are either directly recorded by research personnel or entered electronically into a secured database.

Statistical analyses will be conducted using standard software packages to compare outcomes between the intervention and control groups. Primary analyses include independent t-tests or Mann-Whitney U tests for continuous variables, chi-square tests for categorical variables, and multivariable regression models to adjust for confounding variables such as age, education level, and baseline health literacy. The sample size was calculated to detect clinically meaningful differences in comprehension scores, with power analyses indicating a need for between 460 and 2030 participants depending on the effect size.

Data security is maintained through rigorous measures. Electronic data are stored on encrypted, UCLA-secured laptops and in a secure Box repository. All data handling follows UCLA policies and IRB guidelines for data retention and destruction, with identifiable information destroyed using secure methods once it is no longer required.

Quality control procedures include periodic audits of data entry, regular review meetings with study personnel, and cross-checks of survey responses against clinical records where applicable. An independent monitoring process is in place to ensure compliance with the study protocol and to address any deviations promptly.

Overall, this study is designed to provide robust evidence on the feasibility and effectiveness of AI-generated PLSs in enhancing patient understanding of complex medical information. By integrating technical safeguards, rigorous statistical methods, and a streamlined data collection process, the research aims to deliver insights that may lead to improved patient communication strategies and more effective health care delivery across multiple specialties.