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

以患者为中心的自适应 Web 应用程序对 HFrEF 患者药物优化的影响

• 批准号: 10708814
• 负责人: Michael Dorsch
• 金额: $ 100万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708814
• 关键词: Impact; Adaptive; Patient; Centered; Web

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.

项目摘要 心力衰竭（HF）是美国老年人中最常见的出院诊断。 引人注目的是，40%的患者在首次HF入院后1年内再次入院。这导致 对我们已经紧张的医疗保健系统来说，这是一笔巨大的潜在可避免的费用，因为住院会导致 每年HF管理成本的70%。HF再入院的最常见原因之一是缺乏慢性 药物优化 尽管人们普遍知道指南指导的药物治疗（GDMT）可以减少住院人数 和射血分数降低的HF（HFrEF，也称为收缩性心力衰竭）的死亡率， 药物在临床实践中常常不是最佳的。在COVID-19大流行期间，错过的访问和 没有身体检查的虚拟访问进一步干扰了HFrEF的护理。The American College of 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EHR工具，可以评估 临床特征，并提供适应性建议，以优化GDMT。这是一个重大的 知识上的差距限制了GDMT的益处。因此，迫切需要严格测试 EHR工具可以增加GDMT的适当处方。 这个拟议的项目将确定一个自适应网络应用程序的有效性，以促进GDMT 优化并建立在我们之前研究的工作基础上。我们的中心假设是，Web应用程序 可以改善HFrEF患者的GDMT处方。该项目的基本原理是， 疾病管理--使患者控制病情--将对HF产生重大影响 结果。我们的目标是：（1）确定自适应药物优化网络的效果 指南指导的药物处方在HF中的应用，（2）评估 由药物优化算法提供的推荐和处方药物，以及（3） 识别调节药物优化的有效性的患者和提供者特征 Web应用程序。