Automated system to improve compliance to diabetic retinopathy screening

自动化系统可提高糖尿病视网膜病变筛查的依从性

• 批准号: 10697609
• 负责人: Vinayak S Joshi
• 金额: $ 27.41万
• 依托单位: VISIONQUEST BIOMEDICAL INC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697609
• 关键词: Address; Adult; Affect; Algorithms; Appointment; Awareness; Blindness; Blood; Blood Pressure; Caring; Certification; Cholesterol; Clinic; Clinical; Clinical Research; Complications of Diabetes Mellitus; Computer software; Data; Data Set; Databases; Diabetes Mellitus; Diabetic Retinopathy; Early Diagnosis; Education; Educational Materials; Electronic Health Record; Eye; Future; Glycosylated hemoglobin A; Grant; Health; Hospitals; Individual; Insurance Carriers; Intervention; Knowledge; Label; Lead; Marketing; Measures; Medicare; Mexico; Motivation; Notification; Operative Surgical Procedures; Patient Education; Patient Monitoring; Patient Selection; Patient risk; Patients; Periodicals; Persons; Phase; Population; Primary Care Physician; Process; Productivity; Provider; Public Health; Recommendation; Reporting; Retina; Risk; Schedule; Screening Result; Severities; Specialist; Symptoms; System; Time; Training; United States; Update; Urine; Validation; Visit; Visual; Wait Time; Work; automated algorithm; care providers; checkup examination; commercialization; compliance behavior; diabetic; diabetic patient; electronic health data; electronic health record system; experience; health data; improved; innovation; longitudinal database; non-compliance; ophthalmic examination; patient screening; payment; prevent; prototype; retinal imaging; risk prediction; screening; screening guidelines; software development; success; tool; trend; visual information

Summary The objective of this grant is to develop a fully automated, Electronic Health Record (EHR)-integrable software application, “DR-SCRN”, to improve patient’s compliance to diabetic retinopathy (DR) screening; by predicting a patient’s risk of DR based on the analysis of patient’s own health data, and educating the patient as well as notifying the provider of the patient’s DR risk and screening needs. DR-SCRN will analyze the readily available health data of patient from EHR to identify and predict a trend of DR risk over next 3 years. The core innovations of this project are: a) demonstrate a fully automated and EHR-integrated tool to predict patient’s DR risk. b) improve screening compliance by educating the patient with visual and numerical data, c) calculate DR risk based on patient’s health data, readily available from EHR, d) develop software to notify the provider of patient’s screening needs and to assist with planning of the recommended screening schedule. The main motivation for DR-SCRN is to improve patient’s compliance to DR screening by predicting their DR risk using their own health data and making themselves and their care-provider aware of the potential risks. DR is preventable with early detection through periodic screening and timely intervention. Although DR screening examinations are readily available, only 18 to 40% of diabetics undergo the recommended annual eye exam. Low compliance to DR screening results in vision loss and a $4.3 billion burden to the US due to vision loss treatment, surgery, and diminished productivity. Low compliance results from patient’s ignorance, financial constraints, lack of access, and lack of symptoms or knowledge that vision loss was associated with diabetes. Patient’s ignorance and lack of knowledge have been the most common barriers even when others are minimized. DR- SCRN attempts to improve the compliance by promoting the education of patients and providers on health condition and potential risks and providing a seamless system for both patients and providers to make the screening examinations easily accessible to the population in need. The objectives of this project will be accomplished through three specific aims. In aim 1, we will develop an automatic algorithm to calculate DR risk based on patient’s health data, using a retrospective dataset of N=5000 diabetic patients selected from VisionQuest’s proprietary retinal screening database. In aim 2, we will develop an extrapolation or trend estimation algorithm to predict future DR risk based current DR risk trend, using a separate longitudinal database of N=2500 diabetic patients. In aim 3, we will develop educational material for patients and providers to improve screening compliance, that provides: a) Numerical DR risk prediction over next 3 years, b) Visual representation of how the DR risk may change, c) Proposed DR screening schedule. Further in Phase II, we will pursue three objectives: (1) a large-scale, longitudinal DR screening clinical study, (2) validation of the algorithms and software application using the clinical study data, (3) developing a bridging- software to integrate DR-SCRN with an EHR system, to demonstrate a complete application prototype. Our primary measure of success in Phase II will be the integration of DR-SCRN into the EHR of our existing network of clinics in Mexico and at Retina Global, which are already using our automatic diabetic retinopathy screening system.

概括 该赠款的目的是开发完全自动化的电子健康记录（EHR） - 可整合软件 应用“ DR-SCRN”，以提高患者遵守糖尿病性视网膜病（DR）筛查；通过预测 患者基于对患者自己的健康数据的分析以及对患者的教育以及 将患者的DR风险和筛查需求通知提供者。 DR-SCRN将分析随时可用的 EHR患者的健康数据在未来3年内识别和预测DR风险的趋势。核心创新 该项目的内容是：a）展示一种完全自动化和EHR的综合工具，以预测患者的DR风险。 b） 通过对患者进行视觉和数值数据教育，改善筛选合规性，c）计算DR风险 根据患者的健康数据，EHR很容易获得，d）开发软件以通知提供者患者的 筛选需求并协助计划推荐的筛选时间表。 DR-SCRN的主要动机是通过预测其DR来提高患者对DR筛查的遵守 冒险使用自己的健康数据，并使自己和护理人员意识到潜在的风险。博士 虽然筛查博士 考试很容易获得，只有18％至40％的糖尿病患者参加了建议的年度眼科检查。低的 遵守博士筛查会导致视力丧失，并且由于视力丧失待遇，对美国的43亿美元烧毁， 手术，生产力降低。依从性低来自患者的无知，财务限制， 缺乏通道，缺乏症状或知识，即视力丧失与糖尿病有关。患者 即使将其他知识最小化，无知和知识的缺乏也是最常见的障碍。博士 SCRN试图通过促进患者和提供者的健康教育来提高依从性 条件和潜在风险，为患者和提供者提供无缝系统 筛查考试很容易被有需要的人群访问。 该项目的目标将通过三个具体目标来实现。在AIM 1中，我们将开发一个 使用n = 5000的回顾性数据集，根据患者的健康数据来计算DR风险的自动算法 糖尿病患者从VisionQuest的专有视网膜筛查数据库中选择。在AIM 2中，我们将发展 推断或趋势估计算法，以预测基于未来DR风险的当前DR风险趋势，使用 n = 2500名糖尿病患者的单独纵向数据库。在AIM 3中，我们将为 提高筛查合规性的患者和提供者提供：a）数值DR风险预测 接下来的3年，b）DR风险可能如何改变的视觉表示，c）提出的DR筛查时间表。 在第二阶段，我们将追求三个目标：（1）一项大规模的纵向DR筛查临床研究， （2）使用临床研究数据验证算法和软件应用程序，（3）开发桥接 将DR-SCRN与EHR系统集成的软件，以演示完整的应用程序原型。我们的 第二阶段成功的主要测量将是将DR-SCRN集成到我们现有网络的EHR中 墨西哥和Retina Global的诊所，这些诊所已经在使用我们的自动糖尿病性视网膜病变筛查 系统。