Personalized Patient data and behavioral nudges to improve adherence to chronic cardiovascular medications

个性化的患者数据和行为提示，以提高对慢性心血管药物的依从性

• 批准号: 10200136
• 负责人: SHEANA S BULL
• 金额: $ 141.74万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10200136
• 关键词: Acute myocardial infarction; Adherence; Artificial Intelligence; Atrial Fibrillation; Attention; Behavior; Behavior Therapy; Behavioral; Behavioral Sciences; Body Weight decreased; Cardiac; Cardiovascular system; Cellular Phone; Chronic; Chronic Disease; Clinical; Colorado; Coronary Arteriosclerosis; Data; Decision Making; Diabetes Mellitus; Disease; Event; Goals; Health; Health Care Costs; Health Promotion; Health Technology; Health behavior; Health system; Healthcare Systems; Hospitalization; Hospitals; Human; Hyperlipidemia; Hypertension; Individual; Institutes; Integrated Delivery of Health Care; Intervention; Intervention Studies; Libraries; Measures; Medical center; Methods; Modification; Morbidity - disease rate; Outcome; Patient Education; Patient Noncompliance; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacists; Pharmacy facility; Phase; Pilot Projects; Population; Prevalence; Provider; Randomized; Reach, Effectiveness, Adoption, Implementation, and Maintenance; Recommendation; Research; Resolution; Resources; Self Management; Study Subject; System; Technology; Testing; Text Messaging; United States National Institutes of Health; Universities; base; behavior change; behavioral economics; blood pressure regulation; chatbot; copayment; cost; design; digital; evidence base; financial incentive; four-arm study; health care delivery; health care service utilization; implementation process; improved; individual response; medication compliance; medication nonadherence; mortality; primary outcome; programs; randomized trial; response; safety net; secondary outcome; smoking cessation; text messaging intervention; theories; treatment as usual

Abstract Our objective in this project is to employ population level pharmacy data and delivery of nudges via cell phone text messaging and artificially intelligent (AI) interactive chat bot to improve medication adherence and patient outcomes in 3 integrated healthcare delivery systems (HCS): University of Colorado Health System (UCHealth), VA Eastern Colorado Health Care System (VA), and Denver Health Medical Center (DH). We will identify patients with chronic cardiovascular (CV) conditions taking medications to treat hypertension, atrial fibrillation, coronary artery disease, diabetes and/or hyperlipidemia. We will leverage pharmacy refill data to identify episodes of non-adherence through gaps in medication refills and randomize individuals to 1 of 4 study arms when they have a first refill gap: 1) usual care; 2) generic text message reminder; 3) tailored and engaging text messages optimized to facilitate behavior change; or 4) optimized text messages plus a pre- programmed AI interactive chat bot designed to support identification and resolution of barriers to medication refill and adherence. In the UG3 phase (year 1), we will develop and program a theoretically informed technology-based (a) text message library and (b) chat bot content library using multiple and iterative N of 1 within subject studies to optimize content for a range of diverse patients. These outcomes will inform a pilot intervention to demonstrate feasibility of delivering the intervention and preliminary effects in all 3 HCS. We will also engage patient, provider and health systems stakeholders in designing, refining, and implementing the pilot intervention. In the UH3 phase (years 2-5), we will conduct a pragmatic patient-level randomized intervention across 3 HCS to improve adherence to chronic CV medications. We will evaluate the intervention using a mixed methods approach and apply the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. In addition, we will assess the context and implementation processes to inform local tailoring, adaptations and modifications, and eventual expansion of the intervention.

摘要 我们在这个项目中的目标是使用人口水平的药房数据和通过手机提供推送 文本消息和人工智能(AI)互动聊天机器人，以提高服药依从性和患者 3个综合医疗服务系统(HCS)的成果：科罗拉多大学医疗系统 (UCHealth)、弗吉尼亚州东科罗拉多州医疗保健系统(VA)和丹佛健康医疗中心(DH)。我们会 识别患有慢性心血管(CV)疾病的患者服用药物治疗高血压、心房 纤颤、冠状动脉疾病、糖尿病和/或高脂血症。我们将利用药房续装数据来 通过药物补充量的缺口确定未坚持服药的事件，并随机将个人纳入1/4研究 第一次充填缺口时的手臂：1)日常护理；2)一般短信提醒；3)量身定做和 使用优化的文本消息以促进行为改变；或4)优化的文本消息加上预 编程的人工智能互动聊天机器人，旨在支持识别和解决药物障碍 重新装满和坚持。在UG3阶段(第一年)，我们将开发和规划一个理论上知情的 基于技术的(A)文本消息库和(B)使用多个和迭代N个1的聊天机器人内容库 在主题研究中，为一系列不同的患者优化内容。这些结果将为飞行员提供信息 实施干预措施，以证明实施干预措施的可行性和在所有3个中心实施的初步效果。我们会 还让患者、提供者和卫生系统利益相关者参与设计、改进和实施 试点干预。在UH3阶段(2-5年)，我们将进行务实的患者水平随机 对3个HC进行干预，以提高对慢性心血管药物的依从性。我们将对干预措施进行评估 使用混合方法方法并应用RE-AIM(覆盖范围、有效性、采用、实施和 维护)框架。此外，我们将评估背景和实施过程，以告知当地 调整、调整和修改，并最终扩大干预范围。