Accuracy of the Epic Sepsis Prediction Model in a Regional Health System

TL;DR

This study evaluates the accuracy of Epic's proprietary sepsis prediction model (ESPM) in a regional health system, comparing it to existing early warning scores to determine its effectiveness in real-world clinical settings.

Contribution

It provides a comparative analysis of ESPM's predictive performance against traditional early warning scores within a regional health system.

Findings

ESPM's predictive accuracy was assessed against existing scores.

The study offers insights into the model's practical utility in clinical settings.

Results inform potential adoption of ESPM for sepsis detection.

Abstract

Interest in an electronic health record-based computational model that can accurately predict a patient's risk of sepsis at a given point in time has grown rapidly in the last several years. Like other EHR vendors, the Epic Systems Corporation has developed a proprietary sepsis prediction model (ESPM). Epic developed the model using data from three health systems and penalized logistic regression. Demographic, comorbidity, vital sign, laboratory, medication, and procedural variables contribute to the model. The objective of this project was to compare the predictive performance of the ESPM with a regional health system's current Early Warning Score-based sepsis detection program.