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

Award ID: 2122850

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 (
NSF Program Director: Samir Iqbal