Prehospital prediction of hospital admission for emergent acuity patients transported by paramedics: A population-based cohort study using machine learning.

ABSTRACT:

Introduction

The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED's can provide specialized care or are less overcrowded. Machine learning (ML) predictions may support paramedic decision-making to transport a subgroup of emergent patients to a more suitable, albeit more distanced, ED if hospital admission is unlikely. We examined whether characteristics known to paramedics in the prehospital setting were predictive of hospital admission in emergent acuity patients.

Materials and methods

We conducted a population-level cohort study using four ML algorithms to analyze ED visits of the National Ambulatory Care Reporting System from January 1, 2018 to December 31, 2019 in Ontario, Canada. We

SUBMITTER: Strum RP 

PROVIDER: S-EPMC10449470 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature