Qualitative Assessment of Implementation of a Discharge Prediction Tool Using RE-AIM Framework

Abstract

The implementation process in the routine clinical care of a new predictive tool based on machine learning algorithms has been investigated using the RE-AIM framework. Semi-structured qualitative interviews have been conducted with a broad range of clinicians to elucidate potential barriers and facilitators of the implementation process across five major domains: Reach, Efficacy, Adoption, Implementation, and Maintenance. The analysis of 23 clinician interviews demonstrated a limited reach and adoption of the new tool and identified areas for improvement in implementation and maintenance. Future implementation efforts of machine learning tools should support the proactive engagement of a wide range of clinical users since the very initiation of the predictive analytics project, provide higher transparency of the underlying algorithms, employ broader onboarding of all potential users on a periodic basis, and collect feedback from clinicians on an ongoing basis.