# Automated system to improve compliance to diabetic retinopathy screening

> **NIH NIH R43** · VISIONQUEST BIOMEDICAL INC · 2024 · $54,709

## Abstract

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 screen...

## Key facts

- **NIH application ID:** 11020835
- **Project number:** 3R43EY035206-01S1
- **Recipient organization:** VISIONQUEST BIOMEDICAL INC
- **Principal Investigator:** Vinayak S Joshi
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $54,709
- **Award type:** 3
- **Project period:** 2023-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11020835

## Citation

> US National Institutes of Health, RePORTER application 11020835, Automated system to improve compliance to diabetic retinopathy screening (3R43EY035206-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11020835. Licensed CC0.

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