Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study
| Autor: | Anthony L. Lin, Sahil Sandhu, Cara O'Brien, Nathan Brajer, Armando Bedoya, William Ratliff, Suresh Balu, Jessica Sperling, Mark Sendak |
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| Rok vydání: | 2020 |
| Předmět: |
Quality management
education Psychological intervention Health Informatics Qualitative property lcsh:Computer applications to medicine. Medical informatics Machine learning computer.software_genre 01 natural sciences Grounded theory Workflow sepsis Machine Learning 03 medical and health sciences 0302 clinical medicine emergency medicine Humans 030212 general & internal medicine hospital rapid response team 0101 mathematics Rapid response team Qualitative Research Original Paper business.industry lcsh:Public aspects of medicine 010102 general mathematics lcsh:RA1-1270 Workforce lcsh:R858-859.7 Artificial intelligence business Psychology computer Qualitative research |
| Zdroj: | Journal of Medical Internet Research, Vol 22, Iss 11, p e22421 (2020) Journal of Medical Internet Research |
| ISSN: | 1438-8871 |
| Popis: | BackgroundMachine 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.ObjectiveThis study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows.MethodsWe 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.ResultsA 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 nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes.ConclusionsThis study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions. |
| Databáze: | OpenAIRE |
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