A Team-Based and Technology Driven Adherence Intervention to Improve Chronic Disease Outcomes

项目摘要 在接受糖尿病、高血压或血脂紊乱治疗的成年人中，超过50%的人服用的药物不够理想 坚持，这是美国继续改善慢性病护理的一个突出障碍。主要 护理提供者(PCP)经常无法识别药物不依从和/或没有足够的时间和培训 以解决其潜在原因。在这个项目中，我们提出了以患者为中心，以技术为驱动的 确定有依从性问题的患者的策略，并应用团队方法帮助他们获得证据- 基于个性化的血糖、血压或血脂目标。这种干预扩展了广泛使用的 提供临床决策支持(CDS)基础设施，以支持首次在外部提供的护理模式 将把零售药剂师整合到初级保健团队中。这个 干预依赖于一个持续的健康信息学循环来完成以下工作：(A)识别高危患者 通过扩展电子病历的能力来解决护理点的坚持问题 (EMR)链接CDS，以获取药房索赔数据并衡量遵守情况；(B)建立和维护 自动填写零售药房主动药剂师外展的最新患者登记表 地点；(C)以信息-动机-行为为基础实施药剂师外联战略 世界卫生组织建议的框架，证明有能力影响遵守 跨各种临床应用；以及(D)通过以下方式协调护理和依从性信息 在随后的办公室会议上，将药剂师评估和行动计划纳入CDS。干预措施是 通过PCP的广泛投入并与药房负责人合作进行概念化和设计，以 确保与现有诊所员工和工作流程的有效集成。我们将测试这一方法的有效性 在约50,000家初级保健诊所中使用整群随机对照试验进行干预 患有次优控制的糖尿病、高血压或血脂紊乱的成年人。我们将评估干预措施 对遵守的客观指标(覆盖天数的比例)和高血压控制的变化的影响， 血糖控制和他汀类药物的使用。我们将评估医疗保健成本和长期成本效益，使用 微观模拟建模，并在二次分析中，评估干预对初级不坚持的影响， 10年的心血管风险、提供者、患者和关键利益相关者的护理经验， 以及患者报告的服药依从性措施。这个项目建立在以前的国家研究院的基础上 由我们的团队资助的研究和(A)扩展已经用于 为数千万美国人提供护理，(B)以电子方式将药剂师整合到初级保健团队中，(C) 有可能显著改善慢性病护理，并将大规模的医疗投资转化为 信息系统将为大量患者带来切实的健康益处。

Project Summary More than 50% of adults treated for diabetes, hypertension, or lipid disorders have suboptimal medication adherence, a prominent barrier to continued improvement in chronic disease care in the United States. Primary care providers (PCPs) often fail to identify medication nonadherence and/or have insufficient time and training to address underlying reasons for it. In this project, we propose a patient-centered and technology-driven strategy to identify patients with adherence issues and apply a team approach to help them achieve evidence- based personalized goals for glucose, blood pressure, or lipids. This intervention extends the use of a widely available clinical decision support (CDS) infrastructure to support a model of care that, for the first time outside of a fully integrated care environment, will integrate retail pharmacists within the primary care team. The intervention relies on a continuous health informatics loop to do the following: (a) identify high-risk patients with adherence problems at the point of care by expanding the capability of an electronic medical record (EMR)-linked CDS to capture pharmacy claims data and measure adherence; (b) establish and maintain an auto-populating up-to-date registry of patients identified for proactive pharmacist outreach at retail pharmacy locations; (c) implement a pharmacist outreach strategy based on an information-motivation-behavioral framework recommended by the World Health Organization with demonstrated ability to influence adherence across a variety of clinical applications; and (d) coordinate care and adherence information by incorporating pharmacist assessment and action plans into CDS at subsequent office encounters. The intervention was conceptualized and designed with extensive input from PCPs and in collaboration with pharmacy leaders to ensure efficient integration with existing clinic staff and workflows. We will test the effectiveness of this intervention using a cluster randomized controlled trial in 20 primary care clinics with approximately 50,000 adults with suboptimally controlled diabetes, hypertension, or lipid disorders. We will evaluate intervention impact on objective measures of adherence (proportion of days covered) and changes in hypertension control, glycemic control, and statin use. We will assess health care costs and long-term cost-effectiveness using microsimulation modeling, and in secondary analysis, assess intervention impact on primary nonadherence, 10-year cardiovascular risk, the care experience from the provider, patient, and key stakeholder perspectives, and on patient-reported medication adherence measures. This project builds upon previous National Institutes of Health funded research conducted by our team and (a) extends the capabilities of EMRs already used in the care of tens of millions of Americans, (b) electronically integrates pharmacists within the primary care team, (c) has the potential to significantly improve chronic disease care and translate massive investments in health information systems into tangible health benefits for large numbers of patients.