2019 John M. Eisenberg Patient Safety and Quality Awards: SPOTting Sepsis to Save Lives: A Nationwide Computer Algorithm for Early Detection of Sepsis
Abstract
Background In recognition of the potential of data to drive care and the need for early identification of patients with sepsis, HCA Healthcare developed an automated sepsis detection algorithm-SPOT (Sepsis Prediction and Optimization of Therapy). The algorithm was deployed at scale and served as a mechanism to reduce the time to detection and improve sepsis mortality in 173 hospitals across the United States. Methods The SPOT algorithm was designed as a rules-based detection of defined criteria that would interpret available electronic and basic laboratory data in near real time. Working from an organizational recognition of the need to construct a national clinical data warehouse to allow for the aggregation and analysis of data streams, HCA Healthcare designed and deployed SPOT and delivered the alert from the algorithm to the bedside to initiate existing clinical workflows for patients with sepsis. Results SPOT improved the timeliness of sepsis detection by providing alerts when signals of sepsis become available, triggering initiation of sepsis screens. This gave an advantage of about six hours over the legacy practice of sepsis screening at shift change. When deployed alongside existing sepsis improvement initiatives, SPOT was associated with an acceleration of improvement in mortality-particularly in the not-present-on-admission (NPOA) septic shock population, the patients at greatest risk for mortality. This population had seen little improvement with prior initiatives…
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Past deployed systems have reported significantly lower predictive performance on similar hospital populations.
“Past deployed systems have reported significantly lower predictive performance on similar hospital populations 20,42,45 .”
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 [45]
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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 127 · see citing works
Wan, Y.-K. J., Wright, M. C., McFarland, M. M., Dishman, D., Nies, M. A., Rush, A., Madaras-Kelly, K., Jeppesen, A., & Del Fiol, G. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad203 · Cited by 11 · see citing works
- Authors
- Perlin, J. B., Jackson, E., Hall, C., Mindick, A., Berberich, G., Korwek, K., Poland, R. E., Roach, J., Novak, A., & Guy, J. S.
- Corresponding author
- Guy, Jeffrey S.
- Venue
- The Joint Commission Journal on Quality and Patient Safety
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 46
- Issue
- 7
- Pages
- 381-391
- ISSN
- 1553-7250 (ISSN-L)
- OpenAlex
- W3016654280
- Cited by
- 7
- References
- 2
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 3 · 2021: 1
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Joint Commission Journal on Quality and Patient Safety (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- sepsis, early detection, electronic health records, sepsis mortality, septic shock, clinical data warehouse
- MeSH terms
- Algorithms, Awards and Prizes, Computers, Humans, United States, Sepsis, Patient Safety
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