Implementation Of An Automated Sepsis Screening Tool In A Community Hospital Setting

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.