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

• 批准号: 10549804
• 负责人: Todd P Coleman
• 金额: $ 52.96万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 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/10549804
• 关键词: Adherence; Affect; African American; African American population; Algorithms; Artificial Intelligence; Blindness; Bluetooth; Caucasians; Chronic Disease; Clinical; 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; Life; Machine Learning; Measures; Medication Management; Methods; Minority; Minority Groups; Modeling; Monitor; Optical Coherence Tomography; Outcome; Patient Self-Report; Patients; Pattern; Performance; Persons; Pharmaceutical Preparations; Physicians; Physiologic Intraocular Pressure; Population; Prevalence; Process; Proliferating; Provider; Qualitative Evaluations; Quality of life; Recommendation; Reporting; Research; Risk Reduction; Screening procedure; Signal Transduction; Structure; Technology; Testing; Thick; Topical application; Underrepresented Minority; Variant; Vision; Visual Fields; adherence rate; blind; centralized portal; clinical decision-making; clinical risk; dashboard; data visualization; design; digital; dosage; effective intervention; electronic sensor; experience; field study; flexibility; flexible electronics; 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; participant interview; 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; transmission process; usability; wireless

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.

项目摘要/摘要 青光眼影响着全球7000多万人，是世界上不可逆转的主要原因 失明。目前延缓其发展和进展的唯一方法是降低眼球内压。 压力(IOP)，通过局部滴眼液达到。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 医疗保健成本增加了2倍以上。 与高加索人相比，非裔美国人和拉丁裔人的青光眼负担要高得多。 此外，还发现少数族裔的服药依从率低得不成比例。 以前研究的旨在改善青光眼依从性的干预措施具有关键限制，尤其是 影响少数群体，包括不可靠的自我报告的遵守措施，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 主要是高加索人。 在过去的十年里，随着电子健康的发展，医疗信息技术经历了快速的发展 电子病历(EHR)、附属移动医疗技术的激增以及人工智能的进步 智慧。尽管它们的集成为诊断和风险筛查工具带来了巨大的希望 预测，在现实世界中成功融入社会以帮助少数群体取决于：理解 收集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联 上下文；与医疗保健中心门户EHR的无缝信息交换和互操作性 交付；将算法结果集成到涉及提供者和患者交付的工作流中 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，跨越患者数十年的生活，需要持续监测 因此，它是利用卫生信息技术缩小种族差异的理想应用。在这 提案，我们的目标是通过以下方式实现这一点：展示灵活的电子眼药水传感器的有效性 要生成个人忠诚度的细粒度数字签名，并将此数据与社会- 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，以构建 预测眼压控制和增强临床风险分层的机器学习模型(目标2)和原型 用于干预协调的动态仪表盘(目标3)。总而言之，这一创新的、 全面、文化定制和可扩展的医疗IT框架将提高服药依从性和 在少数群体中，疾病进展缓慢，因此缩小了这一重要的种族健康差距。