The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial.

Objectives: To determine the impact of a machine learning early warning risk score, electronic Cardiac Arrest Risk Triage (eCART), on mortality for elevated-risk adult inpatients.

Design: A pragmatic pre- and post-intervention study conducted over the same 10-month period in 2 consecutive years.

Setting: Four-hospital community-academic health system.

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models.

Background: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.

Objective: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.

Reporting catheter-associated urinary tract infections: denominator matters.

Objective: To evaluate two different methods of measuring catheter-associated urinary tract infection (CAUTI) rates in the setting of a quality improvement initiative aimed at reducing device utilization.

Design, Setting, And Patients: Comparison of CAUTI measurements in the context of a before-after trial of acute care adult admissions to a multicentered healthcare system.

Methods: CAUTIs were identified with an automated surveillance system, and device-days were measured through an electronic health record.