Evaluating a digital sepsis alert in a London multi-site hospital network: a natural experiment using electronic health record data
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Objective: To determine the impact of a digital sepsis alert on patient outcomes in a UK multi-site hospital network. Methods:A natural experiment utilising the phased introduction (without randomisation) of a digital sepsis alert into a multi-site hospital network. Sepsis alerts were either visible to clinicans (patients in the ‘intervention’ group) or running silently and not visible (the control group). Inverse probability of treatment weighted multivariable logistic regression was used to estimate the effect of the intervention on individual patient outcomes.Outcomes:In-hospital 30-day mortality (all inpatients), prolonged hospital stay (≥7 days) and timely antibiotics (≤60minutes of the alert) for patients who alerted in the Emergency Department. Results: The introduction of the alert was associated with lower odds of death (OR:0.76; 95%CI:(0.70, 0.84) n=21183); lower odds of prolonged hospital stay ≥7 days (OR:0.93; 95%CI:(0.88, 0.99) n=9988); and in patients who required antibiotics, an increased odds of receiving timely antibiotics (OR:1.71; 95%CI:(1.57, 1.87) n=4622).Discussion: Current evidence that digital sepsis alerts are effective is mixed. In this large UK study a digital sepsis alert has been shown to be associated with improved outcomes, including timely antibiotics. It is not known whether the presence of alerting is responsible for improved outcomes, or whether the alert acted as a useful driver for quality improvement initiatives.Conclusions: These findings strongly suggest that the the introduction of a network-wide digital sepsis alert is associated with improvements in patient outcomes, demonstrating that digital based interventions can be successfully introduced and readily evaluated.
Date Issued
2020-02
Date Acceptance
2019-09-30
Citation
Journal of the American Medical Informatics Association, 2020, 27 (2), pp.274-283
ISSN
1067-5027
Publisher
Oxford University Press (OUP)
Start Page
274
End Page
283
Journal / Book Title
Journal of the American Medical Informatics Association
Volume
27
Issue
2
Copyright Statement
© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
National Institute for Health Research
Identifier
https://academic.oup.com/jamia/article/27/2/274/5607431
Grant Number
RDA02
HPRU-2012-10047
HPRU-2012-10047
Subjects
alerts
critical care
digital health
early warning scores
electronic health record
sepsis
Sepsis Big Room
Medical Informatics
08 Information and Computing Sciences
09 Engineering
11 Medical and Health Sciences
Publication Status
Published
Date Publish Online
2019-11-20