@article{585b45fbebb54da395487b05a8abfedf,
title = "Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing",
abstract = "Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments at early time points, which is critical for improving sepsis outcomes. In view of the increasing use of such systems, better understanding of how they are adopted and used by healthcare providers is needed. Here, we analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Adjusting for patient presentation and severity, patients with sepsis whose alert was confirmed by a provider within 3 h had a 1.85-h (95% CI 1.66–2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3 h after the alert or never addressed in the system. Finally, we found that emergency department providers and providers who had previous interactions with an alert were more likely to interact with alerts, as well as to confirm alerts on retrospectively identified patients with sepsis. Beyond efforts to improve the performance of early warning systems, efforts to improve adoption are essential to their clinical impact and should focus on understanding providers{\textquoteright} knowledge of, experience with and attitudes toward such systems.",
author = "Henry, {Katharine E.} and Roy Adams and Cassandra Parent and Hossein Soleimani and Anirudh Sridharan and Lauren Johnson and Hager, {David N.} and Cosgrove, {Sara E.} and Andrew Markowski and Klein, {Eili Y.} and Chen, {Edward S.} and Saheed, {Mustapha O.} and Maureen Henley and Sheila Miranda and Katrina Houston and Linton, {Robert C.} and Ahluwalia, {Anushree R.} and Wu, {Albert W.} and Suchi Saria",
note = "Funding Information: Under a license agreement between Bayesian Health and the Johns Hopkins University, K.E.H., S.S. and Johns Hopkins University are entitled to revenue distributions. Additionally, the University owns equity in Bayesian Health. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. S.S. also has grants from Gordon and Betty Moore Foundation, the National Science Foundation, the National Institutes of Health, Defense Advanced Research Projects Agency, the Food and Drug Administration and the American Heart Association; she is a founder of and holds equity in Bayesian Health; she is the scientific advisory board member for PatientPing; and she has received honoraria for talks from a number of biotechnology, research and health-tech companies. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. D.N.H. discloses salary support and funding to his institution from the Marcus Foundation for the conduct of the vitamin C, thiamine and steroids in sepsis trial. S.E.C. declares consulting fees from Basilea for work on an infection adjudication committee for an S. aureus bacteremia trial. The other authors declare no competing interests. Funding Information: The authors thank Y. Ahmad, A. Zhang, M. Yeo and Y. Karklin whose work significantly contributed to early iterations of the development of the deployed system. We also thank R. Demski, K. D{\textquoteright}Souza, A. Kachalia, A. Chen and clinical and quality stakeholders who contributed to tool deployment, education and championing the work. The authors gratefully acknowledge the following sources of funding: the Gordon and Betty Moore Foundation (award 3926), the National Science Foundation Future of Work at the Human-technology Frontier (award 1840088) and the Alfred P. Sloan Foundation research fellowship (2018). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by the NSF the US Government. Funding Information: The authors thank Y. Ahmad, A. Zhang, M. Yeo and Y. Karklin whose work significantly contributed to early iterations of the development of the deployed system. We also thank R. Demski, K. D{\textquoteright}Souza, A. Kachalia, A. Chen and clinical and quality stakeholders who contributed to tool deployment, education and championing the work. The authors gratefully acknowledge the following sources of funding: the Gordon and Betty Moore Foundation (award 3926), the National Science Foundation Future of Work at the Human-technology Frontier (award 1840088) and the Alfred P. Sloan Foundation research fellowship (2018). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by the NSF the US Government. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.",
year = "2022",
month = jul,
doi = "10.1038/s41591-022-01895-z",
language = "English (US)",
volume = "28",
pages = "1447--1454",
journal = "Nature medicine",
issn = "1078-8956",
publisher = "Nature Publishing Group",
number = "7",
}