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: 9750927
• 负责人: SHEANA S BULL
• 金额: $ 145.59万
• 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/9750927
• 关键词: Acute myocardial infarction; Adherence; Adoption; 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; Effectiveness; 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; Maintenance; 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; Process; Provider; Randomized; Recommendation; Research; Resolution; Resources; Self Management; Study Subject; System; Technology; Testing; Text Messaging; United States National Institutes of Health; Universities; arm; base; behavior change; behavioral economics; blood pressure regulation; chatbot; copayment; cost; design; digital; evidence base; financial incentive; health care delivery; health care service utilization; improved; individual response; medication compliance; medication nonadherence; mortality; primary outcome; programs; randomized trial; response; safety net; secondary outcome; smoking cessation; 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采用人口水平的药房数据和通过手机提供轻推 短信和人工智能（AI）交互式聊天机器人，以提高药物依从性和患者 3个综合医疗保健提供系统（HCS）的结果：科罗拉多大学卫生系统 （UCHealth）、弗吉尼亚州东部科罗拉多卫生保健系统（VA）和丹佛健康医疗中心（DH）。我们将 识别患有慢性心血管（CV）疾病的患者，这些患者正在服用药物治疗高血压、心房纤颤和心血管疾病。 纤维性颤动、冠状动脉疾病、糖尿病和/或高脂血症。我们将利用药房续药数据， 通过药物再填充间隙识别不依从事件，并将个体随机分配至4项研究中的1项 当他们有第一个再填充间隙时，他们的手臂：1）日常护理; 2）通用短信提醒; 3）定制和 经过优化的吸引人的短信，以促进行为改变;或4）优化的短信加上预 编程的人工智能交互式聊天机器人，旨在支持识别和解决药物障碍 再填充和粘附。在UG 3阶段（第1年），我们将开发和编程一个理论上知情的 基于技术（a）文本消息库和（B）聊天机器人内容库 在受试者研究中，为各种不同的患者优化内容。这些结果将告知飞行员 干预，以证明在所有3个HCS中实施干预的可行性和初步效果。我们将 还让患者、提供者和卫生系统利益相关者参与设计、完善和实施 飞行员干预。在UH 3阶段（2-5年），我们将进行一项务实的患者水平随机化研究， 在3个HCS中进行干预，以改善对慢性CV药物的依从性。我们将评估干预措施 使用混合方法，并应用RE-AIM（覆盖范围、有效性、采用率、实施和 维护）框架。此外，我们将评估背景和实施过程， 调整、改编和修改，并最终扩大干预。