PFI-RP: Clinical Decision Support Tool to Identify Patients Diagnosed with Heart Failure Who are at High Risk of 30-day Hospital Readmission

这个创新研究伙伴关系（PFI-RP）项目的更广泛的影响/商业潜力是减轻整个医疗系统心力衰竭的负担。心力衰竭是美国住院和再入院的最常见原因（每年有100万例患者）。许多心力衰竭再入院被认为是可以预防的。为了减少再入院率，医疗保健机构会因30天再入院率高而受到处罚。2020年，美国所有医院的超额再入院罚款约为5.6亿美元，约有2500家医院受到处罚。与医疗保健组织的众多利益相关者进行的客户发现访谈表明，客户对临床决策支持工具有浓厚兴趣，该工具可识别被诊断为心力衰竭的患者，以及30天再入院风险很高的患者。通过对30天住院再入院的风险进行准确分层，该软件工具使参与出院计划的临床医生能够在出院时间和有效利用出院后随访服务方面做出更明智的决定，包括分配远程监控硬件。此解决方案可以改善患者的治疗效果，同时降低与可避免的住院治疗相关的成本以及对医院的相应处罚。拟议的项目重点是开发和商业化一种新颖的、基于机器学习的临床决策支持软件工具，以预测被诊断为心力衰竭的住院患者30天的再入院率。所提出的技术包括预测算法中更高频率的生理数据，软件工具将能够结合低延迟（3秒）的临床变量处理这些数据，以量化每个患者在护理点再次住院30天的风险。这项技术有潜力帮助识别和管理被诊断为心力衰竭的高危患者，并提高对这一弱势患者群体的护理质量。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to reduce the burden of heart failure across the healthcare system. Heart failure is the most common cause for hospital admission and readmission in the US ( one million patients annually). Many heart failure readmissions are thought to be preventable. To reduce readmissions, healthcare organizations are penalized for high rates of 30-day hospital readmission. Excess readmission penalties were ~$560 million across all hospitals in the US in 2020, with ~2,500 hospitals incurring penalties. Customer discovery interviews, conducted with numerous stakeholders of healthcare organizations, suggested a strong interest in a Clinical Decision Support tool to identify patients diagnosed with heart failure and who are at high risk of 30-day hospital readmission. By accurately stratifying risk for 30-day hospital readmission, this software tool empowers clinicians involved in discharge planning to make more informed decisions about the timing of hospital discharge and the efficient use of post-discharge follow-up services, including allocation of remote monitoring hardware. This solution can improve patient outcomes while reducing costs associated with avoidable hospitalizations and the corresponding penalties for hospitals.The proposed project focuses on the development and commercialization of a novel, machine learning-based clinical decision support software tool to predict 30-day readmissions for hospitalized patients diagnosed with heart failure. The proposed technology includes higher frequency physiologic data in the predictive algorithm and the software tool will be capable of processing this data in combination with clinical variables with low latency (3 seconds) to quantify each patient’s risk of 30-day hospital readmission at the point of care. This technology has the potential to assist with the identification and management of high-risk patients diagnosed with heart failure and improve the quality of care for this vulnerable patient population.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.