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Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration
Open accessAbstract
BACKGROUND: Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported. METHODS: On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions. RESULTS: The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001). CONCLUSIONS: The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).
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“Translation of these approaches in clinical intervention studies has improved outcomes [16, 34, 35].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [34]
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“Pragmatic randomized trials and quasi-experimental designs offer advantages over pre-post designs or comparisons against historical controls [34, 52].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [34]
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“Other EWSs have influenced in-hospital mortality, sepsis or LOS, but not across the breadth of outcomes we observed4,5,26,27.”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [27]
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“Additionally, ease of use and integration into the workflow have been noted as important factors influencing adoption 26,28,47,48 .”
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“While there are other existing multifactorial machine learning clinical decision support implementations that share similarities to ours27–29, the CONCERN intervention is distinct in its modeling approach by using metadata of nursing documentation as early predictors of patient deterioration in acute and intensive care settings, being fully integrated as a real-time EWS into the EHR and undergoing”
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Metadata
- Authors
- Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P.
- Affiliations
- Kaiser Permanente (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2020-11-12
- Volume
- 383
- Issue
- 20
- Pages
- 1951-1960
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC7787261
- OpenAlex
- W3106297998
- Cited by
- 314
- References
- 39
- Citations by year
- 2026: 52 · 2025: 63 · 2024: 51 · 2023: 47 · 2022: 56 · 2021: 42 · 2020: 3
- FWCI
- 33.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Sidney Garfield Memorial Fund
- Gordon and Betty Moore Foundation (2663, 2663.01)
- The Permanente Medical Group, Inc.
- Kaiser Foundation Hospitals, Inc.
- National Institutes of Health (K23GM112018, R35GM128672)
- Agency for Healthcare Research and Quality (1R01HS018480-01)
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Keywords
- clinical deterioration, early warning systems, rapid response teams, remote patient monitoring, 30-day mortality, electronic health records, hospitalized adults
- MeSH terms
- Alert Fatigue, Health Personnel, Laboratory Critical Values, Clinical Deterioration, Adult, Aged, Automation, Female, Hospitalization, Humans, Length of Stay, Male, Middle Aged, Models, Theoretical, Nursing Staff, Hospital, Patient Readmission, Telemetry, Multivariate Analysis, Hospital Mortality, Risk Assessment, Electronic Health Records
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.
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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