Evaluation of an augmented emergency department electronic medical record-based sepsis alert

Amith Shetty*, Margaret Murphy, Catriona Middleton-Rennie, Angelo Lancuba, Malcolm Green, Harvey Lander, Mary Fullick, Ling Li, Jonathan Iredell, Naren Gunja

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Objective: Electronic medical records-based alerts have shown mixed results in identifying ED sepsis. Augmenting clinical patient-flagging with automated alert systems may improve sepsis screening. We evaluate the performance of a hybrid alert to identify patients in ED with sepsis or in-hospital secondary outcomes from infection. Methods: We extracted a dataset of all patients with sepsis during the study period at five participating Western Sydney EDs. We evaluated the hybrid alert's performance for identifying patients with a discharge diagnosis related to infection and modified sequential sepsis-related organ functional assessment (mSOFA) score ≥2 in ED and also compared the alert to rapid bedside screening tools to identify patients with infection for secondary outcomes of all-cause in-hospital death and/or intensive care unit admission. Results: A total of 118 178 adult patients presented to participating EDs during study period with 1546 patients meeting ED sepsis criteria. The hybrid alert had a sensitivity – 71.2% (95% confidence interval 68.8–73.4), specificity – 96.4% (95% confidence interval 96.3–96.5) for identifying ED sepsis. Clinician flagging identified additional alert-negative 232 ED sepsis and 63 patients with secondary outcomes and 112 alert-positive patients with infection and ED mSOFA score <2 went on to die in hospital. Conclusion: The hybrid alert performed modestly in identifying ED sepsis and secondary outcomes from infection. Not all infected patients with a secondary outcome were identified by the alert or mSOFA score ≥2 threshold. Augmenting clinical practice with auto-alerts rather than pure automation should be considered as a potential for sepsis alerting until more reliable algorithms are available for safe use in clinical practice.

Original languageEnglish
Pages (from-to)848-856
Number of pages9
JournalEMA - Emergency Medicine Australasia
Issue number5
Early online date23 Feb 2021
Publication statusPublished - Oct 2021


  • algorithm
  • decision support system
  • emergency service
  • hospital
  • sepsis
  • systemic inflammatory response syndrome


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