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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.

总结