Artifical Intelligence, Large Language Model, Ophthalmic Disease
Conditions
Keywords
Plain Language Summary, LLM, Artifical Intelligence, Patient education
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALAI-Generated Plain Language Summaries
Participants receive standard ophthalmology notes plus an AI-generated summary that explains medical information in simpler language. Each summary is reviewed by an ophthalmologist for accuracy before being given to the participant. The goal is to help participants better understand their diagnosis, treatment plan, and follow-up instructions.
Sponsors
University of California, Los Angeles
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Age ≥ 18 years English-speaking Receiving ophthalmology care at the Jules Stein Eye Institute Able to provide informed consent
Exclusion criteria
* Known cognitive impairments (e.g., dementia, intellectual disability) that would affect comprehension Prisoners or wards of the state Unable to provide informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Patient Comprehension Score (Immediate Post-Visit) | Immediately post-visit (Day 0) | Mean score on a 5-point scale assessing participants' understanding of their ophthalmology visit notes (diagnosis, treatment plan, follow-up instructions) immediately after the clinic visit. Higher scores indicate better comprehension. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Patient Comprehension Score (1-Week Follow-Up) | 1 week post-visit | Mean score on a 1-5 scale assessing retention of ophthalmology information one week after the clinic visit. Higher scores indicate better long-term comprehension. |
| Patient Satisfaction | Immediately post-visit (Day 0) | Mean satisfaction score (1-5 scale) measuring clarity, detail, and usefulness of the visit notes. Higher scores indicate greater satisfaction. |
| Comprehension Gap Reduction | Day 0 and 1 week post-visit | Difference in comprehension scores between participants with lower vs. higher baseline health literacy. A smaller gap indicates greater reduction in literacy-related disparities. |
| Time Efficiency for Ophthalmologists | Day 0 | Average additional time (in minutes) required for ophthalmologists to review and edit AI-generated Plain Language Summaries, reported in the ophthalmologist survey. Lower times indicate better efficiency. |
| Inbasket Message Rates | 2 weeks post-visit | Number of patient-initiated messages (e.g., via patient portal) within 2 weeks after the visit. Lower message rates may indicate improved clarity and fewer follow-up questions. |
| Medication Fill Compliance | 2 weeks post-visit | Percentage of prescribed medications filled within 2 weeks after the visit. Higher percentages indicate better adherence and understanding of treatment plans. |
| Ophthalmologist Satisfaction | Day 0 | Mean score (1-5 scale) from the ophthalmologist survey measuring satisfaction with the AI-generated summary's clarity and accuracy. Higher scores indicate greater satisfaction. |
| LLM Summarization Error Rate | Day 0 | Proportion of AI-generated summaries identified as having any errors by the reviewing ophthalmologist. Lower percentages indicate more accurate summaries. |
| Error Rate for Ophthalmologist Overreads | Day 0 | Percentage of critical inaccuracies in the AI-generated summaries that could lead to misinterpretation of the patient's condition or plan. Lower rates indicate higher-quality summaries. |
Countries
United States
Contacts
CONTACTPrashant Tailor, MD
PRINCIPAL_INVESTIGATORPrashant Tailor, MD
University of California, Los Angeles
Outcome results
None listed