Assessing the Impact of Using Autonomous Artificial Intelligence (AI) for Pre-screening of Diabetic Retinopathy (DR) and Diabetic Macular Edema on Physician Productivity in Bangladesh
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- Orbis
- 入组人数
- 993
- 试验地点
- 1
- 主要终点
- Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour
研究概览
简要总结
The purpose of this study is to assess the impact of using autonomous artificial intelligence (AI) system for identification of diabetic retinopathy (DR) and diabetic macular edema on productivity of retina specialists in Bangladesh.
Globally, the number of people with diabetes mellitus is increasing. Diabetic retinopathy is a chronic, progressive complication of diabetes mellitus that affects the microvasculature of the retina, which if left untreated can potentially result in vision loss. Early detection and treatment of diabetic retinopathy can prevent potential blindness.
Study Aim: To assess the impact of using autonomous artificial intelligence (AI) system for detection of diabetic retinopathy (DR) and diabetic macular edema on physician productivity in Bangladesh.
Main study question: Will ophthalmologists with clinic days randomized to use autonomous AI DR detection for all persons with diabetes (diagnosed or un-diagnosed) visiting their clinic system have a greater number of examined patients with diabetes (by either AI or clinical exam), and a greater complexity of examined patients on a recognized grading scale, per physician working hour than those randomized not to have autonomous AI screening for their diabetes population?
The investigators anticipate that this study will demonstrate an increase in physician productivity, supporting efficiency for both physicians and patients, while also addressing increased access for DR screening; ultimately, preventing vision loss amongst diabetic patients. The study has the potential to contribute to the evidence base on the benefits of AI for physicians and patients. Additionally, the study has the potential to demonstrate the benefits (and/or challenges) of implementing AI in resource-constrained settings, such as Bangladesh.
详细描述
Bangladesh PRODUCTIVity in Eyecare (B-PRODUCTIVE) Trial
Study Aim: To assess the impact of using autonomous artificial intelligence (AI) for identification of diabetic retinopathy (DR) and diabetic macular edema on productivity of retina specialists in Bangladesh.
Hypothesis: Autonomous AI increases retina specialist productivity
Main Study Question: Will retina specialists complete a greater number of diabetic eye exams per working hour (including persons reviewed by AI whom the retina specialist does not need to see personally) when they use autonomous AI in a randomized clinical trial?
Design: Cluster-randomized (by clinic day) controlled trial.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Double (Participant, Care Provider)
盲法说明
The retina specialists are masked both to patient group assignment (that is, whether autonomous AI results were used or not on the particular clinic day) and also masked to the results of the autonomous AI on Intervention days. Patients are also masked to group assignment and autonomous AI screening results.
入排标准
- 年龄范围
- 22 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Retina specialists regularly seeing patients with DR
- •Routinely examines >= 20 patients with diabetes without known diabetic retinopathy or diabetic macular edema per week
- •Routinely provides laser treatment or intravitreal injections to >= 3 DR patients/month
- •Diagnosed with type 1 or 2 diabetes
- •Presenting visual acuity >= 6/18 best corrected visual acuity in the better-seeing eye
排除标准
- •Retina specialists
- •Currently using an AI system integrated into their clinical care and/or inability to provide informed consent.
- •Inability to provide informed consent or understand the study; persistent vision loss, blurred vision or floaters; previously diagnosed with diabetic retinopathy or diabetic macular edema; history of laser treatment of the retina or injections into either eye, or any history of retinal surgery; contraindicated for imaging by fundus imaging systems
结局指标
主要结局
Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour
时间窗: 105 randomized clinic days
Number of completed care encounters among clinic patients with diabetes per retina specialist clinic hour. Numerator is the number of care encounters among patients with diabetes (including persons evaluated by autonomous AI on Intervention Days who are determined not to need to see the retina specialist). The denominator is retina specialist clinic time in hours.
Number of Completed Care Encounters Among All Clinic Patients (With and Without Diabetes) Per Retina Specialist Clinic Hour
时间窗: 105 randomized clinic days
Number of completed care encounters among all clinic patients (with and without diabetes) per retina specialist clinic hour. Numerator is the number of completed care encounters (including persons evaluated by autonomous AI on Intervention Days who are determined not to need to see the retina specialist). The denominator is retina specialist clinic working time in hours.
次要结局
- Specialist Productivity Adjusted for Patient Complexity for Patients With Diabetes(105 randomized clinic days)
- Number of Participants Who Were Very Satisfied or Satisfied With Autonomous AI(105 randomized clinic days)