Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index.

Objectives: The objectives of this study were (1) to develop and validate a simulation model to estimate daily probabilities of healthcare-associated infections (HAIs), length of stay (LOS), and mortality using time varying patient- and unit-level factors including staffing adequacy and (2) to examine whether HAI incidence varies with staffing adequacy.

Setting: The study was conducted at 2 tertiary- and quaternary-care hospitals, a pediatric acute care hospital, and a community hospital within a single New York City healthcare network.

Patients: All patients discharged from 2012 through 2016 (N = 562,435).

Novel Strategies for Predicting Healthcare-Associated Infections at Admission: Implications for Nursing Care.

Background: Accurate, real-time models to predict hospital adverse events could facilitate timely and targeted interventions to improve patient outcomes. Advances in computing enable the use of supervised machine learning (SML) techniques to predict hospital-onset infections.

Objectives: The purpose of this study was to trial SML methods to predict urinary tract infections (UTIs) during inpatient hospitalization at the time of admission.