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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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要心力衰竭（HF）是美国老年人中最常见的出院诊断。引人注目的是，40% 的患者在首次心衰入院后 1 年内再次入院。这导致由于住院治疗会导致我们本已紧张的医疗保健系统承受巨大的潜在可避免的成本每年 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治疗用药优化。尽管人们普遍知道指南指导药物治疗 (GDMT) 可以减少入院率射血分数降低的心力衰竭（HFrEF，也称为收缩性心力衰竭）的死亡率和死亡率，药物在临床实践中往往没有得到优化。在 COVID-19 大流行期间，错过探视和无需体检的虚拟就诊进一步扰乱了 HFrEF 的护理。美国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 EHR 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 的优势。因此，迫切需要严格测试 EHR 工具可以增加 GDMT 的适当处方。该拟议项目将确定自适应 Web 应用程序促进 GDMT 的有效性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为疾病管理——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) 确定自适应药物优化网络的效果心力衰竭指南指导药物处方的应用，(2) 评估药物优化算法和处方药物提供的建议，以及 (3) 确定调节药物优化有效性的患者和提供者特征网络应用程序。