iGLAMOUR Study: Innovations in Glaucoma Adherence and monitoring Of Under-Represented minorities

iGLAMOUR 研究：青光眼依从性和监测代表性不足的少数群体的创新

基本信息

批准号：
10330002
负责人：
Todd P Coleman
金额：
$ 52.64万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
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：
2021-01-15 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10330002
关键词：
Adherence Affect African American African American population Algorithms Artificial Intelligence Blindness Bluetooth Caucasians Chronic Disease Clinical Clinical Data Clinical Trials Complex Counseling Data Development Diagnosis Disease Progression Early Intervention Early identification Effectiveness Electronic Health Record Electronics Evaluation Eye Eye diseases Eyedrops Glaucoma Health Health Care Costs Health Technology Individual Intervention Intervention Studies Interview Knowledge Label Latino Latino Population Lead Life Machine Learning Measures Medication Management Methods Minority Minority Groups Modeling Monitor Optical Coherence Tomography Outcome Participant Patient Self-Report Patients Pattern Performance Persons Pharmaceutical Preparations Physicians Physiologic Intraocular Pressure Population Prevalence Process Provider Qualitative Evaluations Quality of life Recommendation Reporting Research Risk Screening procedure Signal Transduction Structure Technology Testing Thick Topical application Underrepresented Minority Variant Vision Visual Fields adherence rate base blind clinical decision-making clinical risk dashboard data visualization design digital dosage effective intervention electronic sensor experience field study flexibility glaucoma test health care delivery health information technology high risk human centered design improved innovation interest interoperability mHealth machine learning model machine learning prediction medication compliance medication nonadherence mobile application mobile computing patient engagement personalized care predictive modeling primary outcome prototype racial disparity racial health disparity retinal nerve fiber layer risk prediction risk stratification secondary outcome sensor sensor technology social culture social health determinants success support tools therapeutic target wireless

项目摘要

PROJECT SUMMARY/ABSTRACT Glaucoma affects more than 70 million people worldwide and is the world's leading cause of irreversible blindness. The only current method to delay its development and progression is by lowering intraocular pressure (IOP), achieved with topical administration of eyedrops. Adherence rates for glaucoma eyedrop administration are poor, in many cases below 50%, resulting in disease progression, eventual blindness, and a more than 2-fold increase in healthcare costs. African Americans and Latinos carry a significantly higher glaucoma burden compared with Caucasians. Minorities have additionally been found to have disproportionately lower rates of medication adherence. Previously studied interventions aimed at improving glaucoma adherence had key limitations that particularly affect minorities, including unreliable self-reported measures of adherence, lack of consideration of individual circumstances influencing glaucoma medication management, and developing/testing interventions in predominantly Caucasian populations. Health information technology has experienced rapid advancement in the last decade with the electronic health record (EHR), the proliferation of accessory mobile health technologies, and the advancement of artificial intelligence. Although their integration holds great promise to enable screening tools for diagnosis and risk prediction, successful integration to aid minority populations in real-world settings depends on: understanding how the collected information relates to the patient's other (e.g. clinical) data and the patient's socio-cultural context; seamless information exchange and interoperability with the EHR, the central portal of healthcare delivery; and integration of algorithmic findings into workflows involving both providers and patients to deliver information and/or recommendations in a simple, actionable manner. Glaucoma is a complex chronic disease, spanning decades of patients' lives and requiring ongoing monitoring and evaluation, thus making it an ideal application for the use of health IT to reduce racial disparities. In this proposal, we aim to accomplish this by: demonstrating the effectiveness of a flexible electronic eyedrop sensor to generate granular digital signatures of an individual's adherence and contextualizing this data in a socio- cultural context with patient interviews (Aim 1), combining adherence data with EHR variables to construct machine learning models to predict IOP control and enhance clinical risk stratification (Aim 2), and prototyping a dynamic dashboard for intervention coordination (Aim 3). Altogether, success of this innovative, comprehensive, culturally-tailored, and scalable health IT framework will improve medication adherence and slow disease progression among minorities, therefore narrowing this important racial health disparity.

项目概要/摘要青光眼影响着全球超过 7000 万人，是世界上不可逆转的首要原因失明。目前唯一延缓其发生和进展的方法是降低眼内压眼压（IOP），通过局部滴眼药水来实现。青光眼滴眼液的依从率管理不善，在许多情况下低于 50%，导致疾病进展，最终失明，并导致医疗费用增加超过2倍。与白种人相比，非裔美国人和拉丁裔的青光眼负担明显更高。此外还发现少数族裔的药物依从率特别低。先前研究的旨在改善青光眼依从性的干预措施存在关键局限性，特别是影响少数群体，包括自我报告的遵守措施不可靠、缺乏对个人的考虑影响青光眼药物管理的情况，以及开发/测试干预措施主要是白种人群体。随着电子医疗的发展，医疗信息技术在过去十年中经历了快速发展记录（EHR）、辅助移动医疗技术的普及以及人工技术的进步智力。尽管它们的集成有望实现诊断和风险筛查工具预测，在现实世界环境中成功融入援助少数民族人口取决于：理解收集到的信息如何与患者的其他（例如临床）数据和患者的社会文化相关语境;与医疗保健中央门户 EHR 进行无缝信息交换和互操作送货;并将算法结果整合到涉及提供者和患者的工作流程中以交付以简单、可操作的方式提供信息和/或建议。青光眼是一种复杂的慢性疾病，影响患者数十年的生命，需要持续监测和评估，从而使其成为利用卫生信息技术减少种族差异的理想应用。在这个建议，我们的目标是通过以下方式实现这一目标：展示柔性电子滴眼剂传感器的有效性生成个人依从性的精细数字签名，并将这些数据置于社会背景中文化背景与患者访谈（目标 1），将依从性数据与 EHR 变量相结合来构建用于预测 IOP 控制并增强临床风险分层（目标 2）的机器学习模型和原型设计用于协调干预的动态仪表板（目标 3）。总而言之，这一创新的成功，全面、根据文化定制且可扩展的健康 IT 框架将提高用药依从性和减缓少数族裔疾病的进展，从而缩小这一重要的种族健康差距。