Implementation of an Automated Sepsis Screening Tool in a Community Hospital Setting

Abstract

Background: Early identification of sepsis remains the greatest barrier to compliance with recommended evidence-based bundles.

Purpose: The purpose was to improve the early identification and treatment of sepsis by developing an automated screening tool.

Methods: Six variables associated with sepsis were identified. Logistic regression was used to weigh the variables, and a predictive model was developed to help identify patients at risk. A retrospective review of 10 792 records of hospitalizations was conducted including 339 cases of sepsis to retrieve data for the model.

Results: The final model resulted an area under the curve of 0.857 (95% CI, 0.850-0.863), suggesting that the screening tool may assist in the early identification of patients developing sepsis.

Conclusion: By using artificial intelligence capabilities, we were able to screen 100% of our inpatient population and deliver results directly to the caregiver without any manual intervention by nursing staff.