Emergency department triaging of admitted stroke patients—A Bayesian Network analysis

Abstract

This study uses hospital administrative data to ascertain the differences in the patient characteristics, process and outcomes of care between the Emergency Department (ED) triage categories of patients admitted from an ED presentation into a large metropolitan teaching hospital with a Stroke Care Unit. Bayesian Networks (BNs) derived from the administrative data were used to provide the descriptive models. Nearly half the patients in each stroke subtype were triaged as 'Urgent' (to be seen within 30 minutes). With a decrease in the urgency of triage categories, the proportion admitted within 8 hours decreased dramatically and the proportion of formal discharge increased. Notably, 45% of transient ischaemic attacks (TIAs) were categorized as 'Semi-urgent' (to be attended within 60 minutes), indicating an opportunity to improve emergency assessment of TIAs. The results illustrate the utility of hospital administrative data and the applicability of BNs for review of the current triage practices and subsequent impact.

Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis

“…What-If analysis can be used to solve several complicated problems, especially for highly complex research models where the number of variables would make it difficult for general classification models to find patterns [37]. Furthermore, if the What-If analysis is integrated with a GBN model, important matters can be easily perceived by researchers through the graphical presentation [38].…”