title: Estimating the intensity of ward admission and its effect on emergency department access block.
abstract: Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.
authors.affiliation: School of Information Technology, Deakin University, VIC 3220, Australia. wei.luo@deakin.edu.au
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.forename: Jiguo
authors.initials: J
authors.lastname: Cao
authors.forename: Marcus
authors.initials: M
authors.lastname: Gallagher
authors.forename: Janet
authors.initials: J
authors.lastname: Wiles
mesh_terms: D001315:Australia; D056808:Biostatistics / Q000379:methods*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006297:Health Services Accessibility; D006786:Hospitals, Urban; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D025341:Principal Component Analysis; D013997:Time Factors