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Accelerating the Clinical Workflow Using the Sepsis Prediction and Optimization of Therapy (SPOT) Tool for Real-Time Clinical Monitoring
Abstract
SummaryThe early identification and diagnosis of sepsis remains a challenge in most health care settings. To promote identification and treatment of sepsis earlier in the progression of the disease, HCA Healthcare implemented a series of performance improvement initiatives and augmented this with an automated, real-time algorithm to detect sepsis. We leveraged the scale of our data to create the algorithm and then implemented it into the workflows of 173 distinct acute care hospitals, demonstrating reproducibility and contributing to a decline in sepsis-related mortality over a 6-year period. This algorithm, referred to as SPOT — Sepsis Prediction and Optimization of Therapy — can be incorporated into ongoing sepsis detection, prevention, and treatment initiatives.
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Previous studies of deployed systems have used post-hoc surveys or interviews to gauge provider impressions.
“In order to better understand factors impacting the adoption of CDS, previous studies of deployed systems have used post-hoc surveys or interviews to gauge provider impressions 28,44,53 ; however, provider impressions can differ from their actual use 4,18,27 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [44]
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Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01894-0 · Cited by 365
Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01895-z · Cited by 144
Metadata
- Authors
- Guy, J. S., Jackson, E., & Perlin, J. B.
- Affiliations
- HCA Healthcare (US)
- ORCID
- Perlin, Jonathan B.
- Venue
- NEJM Catalyst
- Publisher
- Massachusetts Medical Society
- Published
- 2020-05
- Volume
- 1
- Issue
- 3
- ISSN
- 2642-0007 (ISSN-L)
- OpenAlex
- W3016690282
- Cited by
- 4
- References
- 0
- Citations by year
- 2025: 1 · 2022: 2 · 2021: 1
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM Catalyst (publishedVersion)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- sepsis, sepsis prediction, clinical workflow, sepsis mortality, acute care hospitals, performance improvement, early diagnosis
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