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: 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
• 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

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.

项目总结/摘要 青光眼影响全世界超过7000万人，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内压， 眼压（IOP），通过局部滴眼液实现。青光眼滴眼液的依从率 给药效果差，在许多情况下低于50%，导致疾病进展，最终失明， 医疗费用增加了两倍多。 非洲裔美国人和拉丁美洲人的青光眼负担明显高于白人。 此外，还发现少数民族的药物依从率不成比例地低。 以前研究的旨在改善青光眼依从性的干预措施有关键的局限性， 影响少数群体的问题，包括自我报告的遵守措施不可靠， 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，健康信息技术得到了迅速的发展。 电子健康记录（EHR），辅助移动的健康技术的扩散，以及人工智能的进步， 智能虽然它们的整合有很大的希望，使筛查工具的诊断和风险 预测，在现实世界环境中成功融入以帮助少数民族人口取决于： 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 环境;与医疗保健的中央门户EHR进行无缝信息交换和互操作 交付;以及将算法发现整合到涉及提供者和患者的工作流程中， 以简单、可行的方式提供信息和/或建议。 青光眼是一种复杂的慢性疾病，跨越患者几十年的生命，需要持续监测 和评估，从而使其成为一个理想的应用程序，使用卫生信息技术，以减少种族差异。在这 建议，我们的目标是实现这一点，通过：证明灵活的电子眼药水传感器的有效性 生成个人遵守的粒度数字签名，并将此数据置于社会背景中， 文化背景与患者访谈（目标1），结合依从性数据与EHR变量， 机器学习模型，用于预测IOP控制和增强临床风险分层（目标2），以及原型设计 干预协调动态仪表板（目标3）。总之，这一创新的成功， 一个全面的、文化定制的、可扩展的健康IT框架将提高药物依从性， 减缓少数民族的疾病进展，从而缩小这一重要的种族健康差距。