Early identification of sepsis remains the greatest barrier to compliance with recommended evidence-based bundles.
The purpose was to improve the early identification and treatment of sepsis by developing an automated screening tool.
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.
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.
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.