The Impact of an Adaptive Patient-Centered Web Application on Medication Optimization in HFrEF Patients

项目总结 心力衰竭(HF)是美国老年人最常见的出院诊断。 引人注目的是，40%的患者在第一次入院后一年内再次入院。这将导致 对我们本已紧张的医疗体系来说，由于住院导致 每年HF管理成本的70%。心力衰竭再入院的最常见原因之一是缺乏慢性 优化用药。 尽管人们普遍认为，指南指导的药物治疗(GDMT)减少了住院人数 射血分数降低的心衰患者的死亡率(HFrEF，也称为收缩性心力衰竭)， 在临床实践中，药物往往没有得到优化。在新冠肺炎大流行期间，错过的访问和 没有体检的虚拟访问进一步扰乱了对HFrEF的护理。美国理工学院 心脏病学建议使用电子健康记录(EHR)来减少错误，改善决策支持， 并促进HFrEF的GDMT。然而，目前还没有有效的以患者为中心的电子病历工具来评估 临床特征，并提供适应性建议以优化GDMT。这代表着一个重要的 知识差距限制了GDMT的好处。因此，迫切需要进行严格的测试 可以增加GDMT适当处方的EHR工具。 这个拟议的项目将确定自适应网络应用程序的有效性，以促进GDMT 优化，并建立在我们以前研究工作的基础上。我们的中心假设是Web应用程序 可改善HFrEF患者的GDMT处方。这个项目的基本原理是一个新的模式 疾病管理--让患者控制自己的病情--将对心力衰竭产生重大影响 结果。我们的目标是：(1)确定适应性用药优化网络的效果 指南指导用药在心力衰竭中的应用，(2)评价两种方法之间的一致性 由用药优化算法和处方药物提供的建议，以及(3) 确定影响药物优化有效性的患者和提供者特征 Web应用程序。

PROJECT SUMMARY Heart failure (HF) is the most common hospital discharge diagnosis among older adults in the United States. Strikingly, 40% of patients are readmitted within 1-year following their first HF admission. This results in significant potentially avoidable costs to our already strained healthcare system, since hospitalizations result in 70% of yearly HF management costs. One of the most common causes of HF readmission is lack of chronic medication optimization. Despite widespread knowledge that guideline-directed medication therapy (GDMT) reduces hospital admission and mortality in HF with reduced ejection fraction (HFrEF, also referred to as systolic heart failure), medications are often not optimized in clinical practice. During the COVID-19 pandemic, missed visits and virtual visits without a physical exam have further disrupted care for HFrEF. The American College of Cardiology recommends the use of electronic health records (EHR) to reduce errors, improve decision support, and facilitate GDMT for HFrEF. Yet currently there are no effective patient centered EHR tools that can assess clinical characteristics and provide adaptive recommendations to optimize GDMT. This represents a significant gap in knowledge that limits the benefits of GDMT. Therefore, there is an immediate need to rigorously test EHR tools that can increase appropriate prescribing of GDMT. This proposed project will determine the effectiveness of an adaptive web application to facilitate GDMT optimization and builds on our work from previous research. Our central hypothesis is that the web application can improve the prescribing of GDMT in HFrEF patients. The rationale for this project is that a new model for disease management – placing patients in control of their condition – will have a substantial impact on HF outcomes. Our objectives are to: (1) determine the effects of an adaptive medication optimization web application on guideline-directed medication prescribing in HF, (2) assess concordance between the recommendations provided by the medication optimization algorithm and the medications prescribed, and (3) identify the patient and provider characteristics that moderate the effectiveness of the medication optimization web application.