Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study.

Journal of Medical Internet Research
Sahil SandhuMark P Sendak

Abstract

Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. We conducted semistructured interviews with 15 frontline 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 data. A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the 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 communication strategies that were developed by...Continue Reading

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Citations

Apr 2, 2021·Journal of Medical Internet Research·Rossana CastaldoMonica Franzese
Aug 24, 2021·Journal of the American Medical Informatics Association : JAMIA·Sehj KashyapNigam H Shah
Sep 10, 2021·Journal of the American Medical Informatics Association : JAMIA·Mark P SendakSuresh Balu
Oct 16, 2021·Journal of Medical Internet Research·Luisa PumplunPeter Buxmann

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Software Mentioned

Sepsis Watch Watch
Sepsis Watch
NVivo Qualitative Data Analysis
Sepsis

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