JMIR Publications

ABSTRACT

Background:

Machine learning models have the potential to improve the diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known on how to best translate and implement these products as part of routine clinical care.

Objective:

This study aimed to explore the factors influencing the integration of a machine learning sepsis early warning system (“Sepsis Watch”) into clinical workflows.

Methods:

We conducted semi-structured interviews with fifteen front-line emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative

Results:

Three dominant themes emerged: (1) perceived utility and trust, (2) implementation of Sepsis Watch processes, and (3) workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application, and the communication strategies that nurses developed to share model outputs with physicians. Barriers included the flow of information between clinicians, and gaps in knowledge about the model itself and broader workflow processes.

Conclusions:

This study generated insights into how frontline clinicians perceive machine learning models and the barriers to integrating them into clinical workflows. Findings can inform future efforts to implement machine learning interventions in real-world settings and to maximize adoption of these interventions.