Value and Acceptability of a Novel Machine Learning Technology for Heart Failure Readmission Reduction: Qualitative Analysis of Clinical Roles and Workflows

Abstract

Background: Despite widespread adoption and demonstrated value in a range of industries, machine learning predictive algorithms are yet to be routinely used in frontline medical care. Significant health system and industry-based resources are allocated towards validating and refining predictive algorithms for a range of applications to ensure accuracy and reliability. For these algorithms to be useful and useable, further work is required to understand how and why they might fit into, and augment existing clinical workflows.