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Optimizing population health outcomes in diabetic retinopathy through personalized and scalable screening strategies

通过个性化和可扩展的筛查策略优化糖尿病视网膜病变的人群健康结果

基本信息

批准号：
10324935
负责人：
Elaine Wells-Gray
金额：
$ 93.17万
依托单位：
RETINAL CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10324935
关键词：
Address Adherence Adult Affect American Attention Behavior Behavior Therapy Behavioral Blindness Caring Clinical Communication Complex Computers Data Data Scientist Data Set Detection Diabetes Mellitus Diabetic Retinopathy Direct Costs Disease Early Diagnosis Early treatment Educational Intervention Electronic Health Record Engineering Ensure Evaluation Excision Eye Failure Goals Guidelines Health Health Benefit Health Insurance Health behavior Healthcare Individual Intelligence Intervention Language Logic Machine Learning Maps Marketing Medical Records Methods Modality Modeling National Eye Institute Natural Language Processing North Carolina Ophthalmic examination and evaluation Outcome Pain Patient Care Patient Preferences Patients Persons Plant Roots Population Population Characteristics Prevalence Provider Psychologist Public Health Quality-Adjusted Life Years Recommendation Records Resource Allocation Resources Retina Risk Scientist Service delivery model Software Tools Specialist Surveys Symptoms System Target Populations Techniques Technology Testing Time Training Transportation Universities Vision accurate diagnosis adjudicate base burden of illness care coordination care providers cost cost effective data repository design diabetic diagnostic accuracy disorder risk follow-up frontier high risk insurance claims interest intervention cost intervention effect machine learning method patient engagement patient population patient screening personalized care population based population health predictive modeling prevent programs retinal imaging risk prediction routine screening screening screening participation simulation software development telehealth therapy design tool tool development willingness

项目摘要

Project Summary / Abstract According to the National Eye Institute (NEI), early detection and timely treatment can reduce the risk of severe vision loss from diabetic retinopathy (DR) by 95%. Yet, DR remains the leading cause of blindness among American adults. By 2030, 54.9 million Americans are expected to have diabetes. The prevalence of vision-threatening diabetic retinopathy (VTDR) among people with diabetes is 4.4% and 10.2% in the US and worldwide, respectively, representing over 28 million people at risk of blindness. Because DR causes no pain, vision loss, or other symptoms at its early stages and only 50% of people with diabetes receive annual eye exams, many will be unaware of their disease until vision loss is irreversible. Retinal Care Inc. (RCI) believes that the root cause of this public health failure is more behavioral and educational than clinical; complete adherence to screening and treatment would prevent nearly all vision loss from DR. However, 100% adherence is not feasible, practical, or cost-effective. Eliminating blindness from DR requires a strategy shift that acknowledges that preventing vision loss from DR does not require a 100% annual screening rate; it requires only that all patients with VTDR are evaluated by an eye care provider and adhere to follow up recommendations. RCI's approach to cost-effectively eliminating blindness from DR is to prioritize patients who are most likely to require immediate attention and devote the resources necessary to ensure they are evaluated by an eye care provider. The proposed project is designed to accomplish this goal through VTDR risk prediction; targeted patient engagement, education, and behavioral interventions; and optimization of the full system to achieve maximum population benefit. In Aim 1, RCI will leverage our existing Data Repository and machine learning methods to predict VTDR risk using electronic health record (EHR) and healthcare insurance claims data, with the initial goal of correctly placing over 90% of patients with VTDR in the highest-risk half of the population when ordered by risk. In doing so, RCI can focus patient engagement resources on patients who are most likely to need immediate evaluation and treatment, rather than diverting resources to patients who are less likely to require immediate attention. In Aim 2, RCI will use mixed methods framed by the Integrated Behavior Model to identify barriers and motivators for screening and assess their relative importance, develop and implement a survey instrument to elicit willingness to participate in screening based on motivating factors and barrier removal, and use natural language processing to detect barriers and facilitators for diabetic eye screening from patient communications. In Aim 3, RCI will create an agent-based simulation tool to guide care coordination recommendations in a way that maximizes population health outcomes subject to constraints on time and cost. The tool will identify optimal intervention strategies and be adaptable to changes in cost, disease burden, patient preference, population characteristics, and other parameters. Decisions that will be optimized include resource allocation as a function of risk, intervention timing, screening modality, communication strategies, barrier removal strategies, and other modifiable aspects of the system.

项目摘要/摘要