Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System

Autor: Sara E. Cosgrove, Sridharan A, Katharine Henry, Roy Adams, Parent C, David N. Hager, Houston K, Linton Rc, Markowski A, Suchi Saria, Eili Y. Klein, Miranda S, Johnson L, Edward S. Chen, Ahluwalia Ar, Albert W. Wu, Henley M

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments earlier, an important step in improving sepsis outcomes. Increasing use of such systems means quantifying and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f26aa3b728a9cdb4974d5c19c0db401b
https://doi.org/10.1101/2021.07.02.21259941

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Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

Autor: Henry KE; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Kornfield R; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.; Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, USA., Sridharan A; Howard County General Hospital, Columbia, MD, USA., Linton RC; Howard County General Hospital, Columbia, MD, USA., Groh C; Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI, USA., Wang T; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Wu A; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Mutlu B; Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI, USA. bilge@cs.wisc.edu.; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA. bilge@cs.wisc.edu., Saria S; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Bayesian Health, New York, NY, 10005, USA. ssaria@cs.jhu.edu.

Publikováno v: NPJ digital medicine [NPJ Digit Med] 2022 Jul 21; Vol. 5 (1), pp. 97. Date of Electronic Publication: 2022 Jul 21.

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Autor: Adams R; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA., Henry KE; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Sridharan A; Howard County General Hospital, Columbia, MD, USA., Soleimani H; Health Informatics, University of California, San Francisco, CA, USA., Zhan A; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, USA.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Rawat N; Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA., Johnson L; Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA., Hager DN; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA., Cosgrove SE; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA., Markowski A; Suburban Hospital, Bethesda, MD, USA., Klein EY; Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA., Chen ES; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA., Saheed MO; Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA., Henley M; Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA., Miranda S; Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA., Houston K; Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA., Linton RC; Howard County General Hospital, Columbia, MD, USA., Ahluwalia AR; Department of Quality Improvement, Johns Hopkins Hospital, Baltimore, MD, USA., Wu AW; Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, USA. awu@jhu.edu.; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. awu@jhu.edu.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. awu@jhu.edu.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. awu@jhu.edu.; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. awu@jhu.edu., Saria S; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Bayesian Health, New York, NY, USA. ssaria@cs.jhu.edu.

Publikováno v: Nature medicine [Nat Med] 2022 Jul; Vol. 28 (7), pp. 1455-1460. Date of Electronic Publication: 2022 Jul 21.

Medial malleolar fracture in a child. A case report.

Autor: Beaty JH; Campbell Foundation, University of Tennessee, Memphis., Linton RC

Publikováno v: The Journal of bone and joint surgery. American volume [J Bone Joint Surg Am] 1988 Sep; Vol. 70 (8), pp. 1254-5.