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医院出院诊断。 令人惊讶的是，在第一次入院后的1年内，有40％的患者在1年内被入院。这导致 我们已经紧张的医疗保健系统可能会避免巨大的避免成本，因为住院导致 每年HF管理成本的70％。 HF再入院最常见的原因之一是缺乏慢性 药物优化。 尽管有宽泛的知识，即指导指导的药物治疗（GDMT）降低了医院入院 和HF的死亡率减少，射血分数降低（HFREF，也称为收缩性心力衰竭）， 在临床实践中通常不会优化药物。在Covid-19-19大流行期间，错过的访问和 没有身体检查的虚拟访问已进一步破坏了对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。但是目前尚无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。这代表着重要的 知识的差距限制了GDMT的好处。因此，需要立即进行严格的测试 EHR工具可以增加GDMT的适当处方。 该提议的项目将确定自适应Web应用程序的有效性以促进GDMT 优化并以我们先前的研究为基础。我们的中心假设是Web应用程序 可以改善HFREF患者的GDMT处方。该项目的理由是 疾病管理 - 置于患者控制状况 - 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影响 HF中针对指导的药物处方的申请，（2）评估一致性 药物优化算法和规定的药物提供的建议，（3） 确定缓和药物优化有效性的患者和提供者特征 Web应用程序。