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临床试验/NCT07317661
NCT07317661Enrolling By Invitation不适用

Effectiveness of a Large Language Model-Based Educational Tool on Intraocular Lens Options: A Randomized Controlled Trial

Stanford University1 个研究点 分布在 1 个国家目标入组 70 人开始时间: 2026年1月1日最近更新:

试验速览

阶段
不适用
状态
Enrolling By Invitation
入组人数
70
试验地点
1
主要终点
Total Consultation Time

研究概览

简要总结

Patients with cataracts disease need to choose what type of artificial lens will go into their eye prior to surgery date. Some lenses are standard and are usually covered by insurance. Other "premium" lenses have various benefits such as reducing the need for glasses but usually require out-of-pocket costs.

The combined busy outpatient clinic and complexity of artificial lens choices in the ever-changing world of cataract surgery tends to lead patients confused about their available lens options. There is an abundance of educational material present in premium lenses, however these are limited by accessibility and are standardized at single educational levels.

Therefore in the present study, we want to test whether giving patients a short LLM powered AI-guided explanation from Custom GPT from OpenAI of lens options prior to their consultation with their doctor can improve visit efficiency, physician explanation and patient understanding of lens options. We will compare two groups: standard of care versus standard of care plus AI education.

The LLM in this study is intended to provide supplemental information about premium intraocular lens(IOLs) options to study participants, and is no means supposed to replace a health care professional in the diagnosis, cure, treatment, and/or mitigation of disease. Study is analogous to giving a verified health pamphlet to a patient for them to view and learn different IOL options, in other words, facilitating patient understanding of their options.

The LLM will be trained by several health care professionals and MD specialists to provide sufficient instructions. Sources will include verified online resources and MD information.

The investigators hope to learn if a large language model-based educational tool can improve visit efficiency, physician explanation and patient understanding of intraocular lens options. New knowledge of this study could guide how cataract counseling is delivered in the future and may help clinics spend more time on individualized questions instead of repeating generic information.

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Treatment
盲法
Single (Care Provider)

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Age 18 or older
  • Presenting for cataract evaluation or preoperative cataract counseling in the ophthalmology clinic
  • Able to provide informed consent
  • English-speaking
  • No prior cataract surgery in either eye (so that all patients are making a first-eye IOL decision)

排除标准

  • Any cognitive impairment or hearing impairment that prevents meaningful counseling or survey completion
  • Urgent ocular condition requiring immediate attention that would override routine cataract counseling (for example, acute retinal detachment)
  • Patient declines or is unable to complete the brief post-visit survey
  • Has ocular conditions that would impact eligibility of non-monofocal lens options

结局指标

主要结局

Total Consultation Time

时间窗: Same Day of Enrollment up to 2 hours

Total consultation time of both fellow and attending physician in their visit with the study participant will be recorded in minutes.

次要结局

  • Percentage of Monofocal Lens Chosen as IOL of Choice Between Arms(Up to 4 weeks post enrollment)
  • Patient satisfaction scale score as measured by Client Satisfaction Questionnaire-8 (CSQ-8)(Same Day of Enrollment up to 2 hours)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Robert T. Chang, MD

Associate Professor of Ophthalmology

Stanford University

研究点 (1)

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