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

基本信息

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
项目类别：
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

项目摘要

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.

概括这笔赠款的目标是开发完全自动化的电子健康记录 (EHR) 可集成软件应用程序“DR-SCRN”，通过预测来提高患者对糖尿病视网膜病变（DR）筛查的依从性；基于对患者自身健康数据的分析来确定患者患 DR 的风险，并对患者进行教育通知提供者患者的 DR 风险和筛查需求，DR-SCRN 将分析现有的信息。来自 EHR 的患者健康数据，用于识别和预测未来 3 年内 DR 风险的趋势核心创新。该项目的主要内容是：a) 展示一种完全自动化的 EHR 集成工具来预测患者的 DR 风险。通过向患者提供视觉和数字数据教育来提高筛查依从性，c) 计算 DR 风险基于患者的健康数据（可从 EHR 轻松获取），d) 开发软件以通知提供者患者的健康状况筛查需求并协助规划推荐的筛查时间表。 DR-SCRN 的主要动机是通过预测患者的 DR 来提高患者对 DR 筛查的依从性风险使用自己的健康数据，并使自己和他们的护理提供者意识到潜在的 DR 风险。尽管 DR 筛查是可以通过定期筛查和及时干预进行早期发现来预防的。检查很容易进行，只有 18% 至 40% 的糖尿病患者接受建议的年度眼科检查。遵守 DR 筛查会导致视力丧失，并因视力丧失治疗给美国带来 43 亿美元的负担，手术，以及由于患者的无知、财务限制而导致的低依从性，缺乏获取途径，以及缺乏视力丧失与糖尿病相关的症状或知识。即使其他障碍被最小化，无知和缺乏知识仍然是最常见的障碍。 SCRN 试图通过促进患者和医疗服务提供者的健康教育来提高合规性状况和潜在风险，并为患者和提供者提供无缝系统，以确保有需要的人群可以轻松进行筛查检查。该项目的目标将通过三个具体目标来实现。在目标 1 中，我们将制定一个目标。自动算法根据患者的健康数据计算 DR 风险，使用 N=5000 的回顾性数据集在目标 2 中，我们将开发从 VisionQuest 专有的视网膜筛查数据库中筛选的糖尿病患者。一种外推或趋势估计算法，用于根据当前 DR 风险趋势预测未来 DR 风险，使用 N=2500 名糖尿病患者的独立纵向数据库在目标 3 中，我们将开发教育材料。患者和医疗服务提供者提高筛查依从性，这提供了： a) 数字 DR 风险预测未来 3 年，b) DR 风险如何变化的直观表示，c) 拟议的 DR 筛查时间表。在第二阶段，我们将追求三个目标：(1) 大规模、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- 软件将 DR-SCRN 与 EHR 系统集成，以演示我们的完整应用程序原型。第二阶段成功的主要衡量标准是将 DR-SCRN 集成到我们现有网络的 EHR 中墨西哥和 Retina Global 的诊所已经在使用我们的自动糖尿病视网膜病变筛查系统。