Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study
| Autor: | Faraz Yashar, Dina Sarro, Mike Revoir, Joseph Futoma, Michael Gao, Sahil Sandhu, Christelle Tan, Corinne Miller, Cara O'Brien, Katherine Heller, Nathan Brajer, R. Donohoe, William Ratliff, Eric G. Poon, Kristin M. Corey, Armando Bedoya, Marshall Nichols, Tres Brown, Kelly Kester, Madeleine Clare Elish, Kevin J. Anstrom, Anthony Lin, Jason Theiling, Elizabeth Alderton, Suresh Balu, Susan Engelbosch, Mark Sendak |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Quality management
Process management Computer science Computer applications to medicine. Medical informatics education R858-859.7 Health Informatics Plan (drawing) 01 natural sciences Sepsis sepsis 03 medical and health sciences 0302 clinical medicine translational medicine Health Information Management medicine change 030212 general & internal medicine 0101 mathematics Clinical care innovation organizational Original Paper business.industry Deep learning 010102 general mathematics Translational medicine deep learning medicine.disease Workflow machine learning System framework Artificial intelligence business |
| Zdroj: | JMIR Medical Informatics JMIR Medical Informatics, Vol 8, Iss 7, p e15182 (2020) |
| ISSN: | 2291-9694 |
| Popis: | Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems. |
| Databáze: | OpenAIRE |
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