Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

ABSTRACT:

Objective

To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).

Design

Prospective observational study.

Setting

Tertiary teaching hospital in Philadelphia, PA.

Patients

Non-ICU admissions November-December 2016.

Interventions

During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert.

Measurements and main results

For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first s

SUBMITTER: Ginestra JC 

PROVIDER: S-EPMC6791738 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature