Abstract
Emergency department (ED) patients presenting with acute heart failure (AHF) are commonly admitted to the hospital, though a significant subset are at relatively low risk for adverse outcomes. We recently developed a novel machine learning (ML)-based AHF risk tool that predicts risk of 30-day serious adverse events. Successful implementation hinges upon physician acceptance of the tool and careful integration into existing workflows. Our objectives are to 1) understand ED physicians’ hesitations towards use of an AHF risk prediction tool; 2) identify opportunities to optimize implementation and integration into workflows; and 3) probe physicians on use of a ML-based model in clinical practice.
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