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Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study.
Sandhu, Sahil; Lin, Anthony L; Brajer, Nathan; Sperling, Jessica; Ratliff, William; Bedoya, Armando D; Balu, Suresh; O'Brien, Cara; Sendak, Mark P.
Afiliación
  • Sandhu S; Trinity College of Arts & Sciences, Duke University, Durham, NC, United States.
  • Lin AL; Duke University School of Medicine, Durham, NC, United States.
  • Brajer N; Duke University School of Medicine, Durham, NC, United States.
  • Sperling J; Social Science Research Institute, Duke University, Durham, NC, United States.
  • Ratliff W; Duke Institute for Health Innovation, Durham, NC, United States.
  • Bedoya AD; Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, United States.
  • Balu S; Duke Institute for Health Innovation, Durham, NC, United States.
  • O'Brien C; Department of Medicine, Duke University School of Medicine, Durham, NC, United States.
  • Sendak MP; Duke Institute for Health Innovation, Durham, NC, United States.
J Med Internet Res ; 22(11): e22421, 2020 11 19.
Article en En | MEDLINE | ID: mdl-33211015
BACKGROUND: 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. OBJECTIVE: This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. METHODS: 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. RESULTS: 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 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. CONCLUSIONS: This 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Flujo de Trabajo / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Flujo de Trabajo / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá