Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic
Open accessAbstract
This descriptive study evaluates the association between nursing reports of sepsis overalerting and alert volume by quantifying the number of alerts generated by the Epic Sepsis Model at 24 US hospitals before and during the COVID-19 pandemic.
AI snapshot
Written from the paper’s full text.
TL;DR
In a descriptive study of Epic Sepsis Model alerts at 24 US hospitals, the proportion of patients generating sepsis alerts more than doubled and total daily alerts rose 43% in the 3 weeks after each system’s first COVID-19 case, despite a 35% decline in hospital census. The authors conclude this illustrates the increased alerting burden imposed by COVID-19 and the need for rapid AI alert governance.
Key findings
- In the 3 weeks before and after the first COVID-19 case, the proportion of patients generating sepsis alerts per day more than doubled from 9% (953 of 10,159) to 21% (1,363 of 6,634).
- The total hospital census declined by 35% (from 10,159 to 6,634), yet the total number of alerts per day increased by 43% (from 953 to 1,363).
- Larger hospitals generally experienced an increase in the proportion of patients generating sepsis alerts, whereas changes were more heterogeneous across smaller hospitals.
- The pausing of sepsis alerts at the University of Michigan corresponded with the increase in alerting.
Methods
- Descriptive study using prospectively calculated Epic Sepsis Model scores from 24 hospitals across 4 geographically diverse health systems (University of Michigan, New York University Langone Health, Mass General Brigham, and BJC HealthCare) from November 3, 2019, to April 25, 2020, aligned to each hospital’s first COVID-19 case.
- The study compared total hospital census, proportion of patients generating sepsis alerts, and frequency of sepsis alerts before and during the COVID-19 pandemic, using an alerting threshold of 6 and a maximum of 1 alert per patient per day.
- Analysis was descriptive; further details were provided in the eMethods in the Supplement.
Limitations
- The paper states that it did not evaluate the model’s accuracy, and notes that even accurate alerts may be disruptive because many existing sepsis workflows are built around bacterial sepsis and may not be entirely appropriate in the context of COVID-19.
- The text makes evident that the descriptive design cannot establish causes of the alert increase, and findings are limited to 24 hospitals in 4 health systems over a short period around each system’s first COVID-19 case.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
5 of 6 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Between 3 and 4 weeks after its first COVID-19 hospitalization, the University of Michigan paused ESM-generated alerts in April 2020 after nursing reports of overalerting.
“Between 3 and 4 weeks after its first COVID-19 hospitalization, the University of Michigan paused ESM-generated alerts in April 2020 after nursing reports of overalerting.5”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [5] Finlayson SG, Subbaswamy A, Singh K,. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021; 385(3): 283- 286. doi: 10.1056/NEJMc2104626 34260843 PMC8665481 source
The “TO THE EDITOR” section says the University of Michigan Hospital deactivated Epic’s sepsis-alerting model in April 2020 because of spurious alerts during the COVID-19 pandemic. It does not give the interval since the hospital’s first COVID-19 hospitalization or say that nursing reports prompted the decision. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Findingown data
The total number of sepsis alerts per day increased by 43% (from 953 to 1363) despite the lower hospital census.
“The total number of alerts per day increased by 43% (from 953 to 1363) despite the lower hospital census.”
Quote in the paperSubstantial claim - Findingown data
Over the same period, the total hospital census declined by 35% (from 10 159 to 6634).
“However, the total hospital census declined by 35% (from 10 159 to 6634).”
Quote in the paperSubstantial claim - Findingown data
In the 3 weeks before and after the first case of COVID-19 in each US health system studied, the proportion of patients generating sepsis alerts per day more than doubled, from 9% (953 of 10 159) to 21% (1363 of 6634).
“In the 3 weeks before and after the first case of COVID-19 in each US health system in this study, the proportion of patients generating sepsis alerts per day more than doubled from 9% (953 of 10 159) to 21% (1363 of 6634), respectively (Table).”
Quote in the paperSubstantial claim - Findingown data
The pausing of sepsis alerts at the University of Michigan corresponded with the increase in alerting.
“The pausing of sepsis alerts at the University of Michigan corresponded with the increase in alerting (Figure).”
Quote in the paperNot a substantial claim
- Interpretationown data
The 43% increase in total alerts illustrates the increased alerting burden imposed by COVID-19 on a sepsis model.
“the 43% increase in total alerts illustrates the increased alerting burden imposed by COVID-19 on a sepsis model.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 6
- May combine two claims. 2
Classified as probably not a substantial claim.
PremisecitationConcerns have arisen recently regarding the potential for sepsis models to cause alert fatigue.
“Concerns have arisen recently regarding the potential for sepsis models to cause alert fatigue.2,3,4”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [2] Wong A, Otles E, Donnelly JP,. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021; 181(8): 1065- 1070. doi: 10.1001/jamainternmed.2021.2626 34152373 PMC8218233 source
- cites [3] Ginestra JC, Giannini HM, Schweickert WD,. Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Crit Care Med. 2019; 47(11): 1477- 1484. doi: 10.1097/CCM.0000000000003803 31135500 PMC6791738 source
- cites [4] Eilish MC, Watkins EA. Repairing innovation: a study of integrating AI in clinical care. Data & Society Research Institute. Published September 2020. Accessed August 5, 2021. https://datasociety.net/wp-content/uploads/2020/09/Repairing-Innovation-DataSociety-20200930-1.pdf
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMore than a quarter of hospitalized patients in the first wave of the COVID-19 pandemic in 2020 required a transfer to an intensive care unit or died.
“For example, more than a quarter of hospitalized patients in the first wave of the COVID-19 pandemic in 2020 required a transfer to an intensive care unit or died.6”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [6] Singh K, Valley TS, Tang S, Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19. Ann Am Thorac Soc. 2021; 18(7): 1129- 1137. doi: 10.1513/AnnalsATS.202006-698OC 33357088 PMC8328366 source
The Results report that 103 of 392 hospitalizations (26%) had a composite adverse outcome, and the Methods define that outcome to include ICU-level care or in-hospital death. But the study covered eligible admissions at one Michigan academic medical center from March to May 2020, excluded patients admitted directly to the ICU, and also included mechanical ventilation in the composite. Its 26% figure therefore does not establish the stated rate for hospitalized patients generally or precisely for ICU transfer or death alone. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationTo date, sepsis early warning systems have not been shown to reliably improve patient outcomes.
“To date, sepsis early warning systems have not been shown to reliably improve patient outcomes,1”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Downing NL, Rolnick J, Poole SF,. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Qual Saf. 2019; 28(9): 762- 768. doi: 10.1136/bmjqs-2018-008765 30872387 PMC6860967 source
May combine two claims.
Findingown dataLarger hospitals generally experienced an increase in the proportion of patients generating sepsis alerts, whereas the change in the alerting proportion was more heterogeneous across smaller hospitals.
“Larger hospitals generally experienced an increase in the proportion of patients generating sepsis alerts, whereas the change in the alerting proportion was more heterogeneous across smaller hospitals (Figure).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe study did not evaluate the sepsis model's accuracy.
“Our study was limited in that we did not evaluate the model's accuracy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenEven if the alerts were accurate, many existing sepsis workflows are built around bacterial sepsis and thus may not be entirely appropriate in the context of COVID-19.
“However, even if the alerts were accurate, many existing sepsis workflows are built around bacterial sepsis and thus may not be entirely appropriate in the context of COVID-19.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningThe increase in the proportion of patients generating sepsis alerts can be explained by the cancellation of elective surgeries and a higher average acuity among the remaining hospitalized patients.
“Although the increase in the proportion of patients generating sepsis alerts in this study can be explained by the cancellation of elective surgeries and a higher average acuity among the remaining hospitalized patients,”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Artificial intelligence systems such as the widely implemented Epic Sepsis Model are beginning to replace rule-based sepsis early warning systems.
“artificial intelligence (AI) systems such as the widely implemented Epic Sepsis Model (ESM) are beginning to replace them.”
The paper relies on this without support.
Even accurate alerts can be disruptive in the presence of resource constraints.
“However, even accurate alerts can be disruptive in the presence of resource constraints.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Vendors increasingly provide proprietary "turnkey" CDSS solutions for identifying clinical deterioration and sepsis.
“Vendors increasingly provide proprietary “turnkey” CDSS solutions for identifying clinical deterioration and sepsis [42–44].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [43]
The Introduction describes the widely implemented Epic Sepsis Model as an AI sepsis early-warning system beginning to replace older systems, and the Methods report its use across 24 hospitals. The paper thus supports an example of a vendor-associated sepsis alert system, but it does not establish that vendors increasingly offer such systems, describe them as “turnkey,” or study systems for identifying clinical deterioration. The supplied copy has no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Overlapping calendar-week ranges in the table
MinorThe first row covers days -56 through -48, which includes day -49. The next row separately reports day -49 with different values, even though the table says its data are reported by calendar week.
“| –56 (to –48) | 10 302 | 881 | 9 |”
“| –49 | 10 105 | 863 | 9 |”
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- Authors
- Wong, A., Cao, J., Lyons, P. G., Dutta, S., Major, V. J., Ötleş, E., & Singh, K.
- Affiliations
- University of Michigan (US)
- Washington University in St. Louis (US)
- Massachusetts General Hospital (US)
- New York University (US)
- Corresponding author
- Singh, Karandeep
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2021-11-19
- Volume
- 4
- Issue
- 11
- Pages
- e2135286
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC8605481
- OpenAlex
- W3211709392
- Cited by
- 57
- References
- 6
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 11 · 2023: 12 · 2022: 16
- FWCI
- 7.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
- Funders
- Doris Duke Charitable Foundation
- Teva Pharmaceutical Industries
- University of Michigan
- National Institutes of Health (KL2TR002346)
- National Institute of General Medical Sciences (T32GM007863)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- sepsis, clinical decision support alerts, nursing reports, COVID-19 pandemic
- MeSH terms
- COVID-19, SARS-CoV-2, Emergency Service, Hospital, Hospitals, Humans, Population Surveillance, Retrospective Studies, United States, Sepsis, Decision Support Systems, Clinical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-02.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-02.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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