Estimating the intensity of ward admission and its effect on emergency department access block.

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.

[1]  D. King,et al.  Clinical process redesign for unplanned arrivals in hospitals , 2008, The Medical journal of Australia.

[2]  B. Silverman,et al.  Estimating the mean and covariance structure nonparametrically when the data are curves , 1991 .

[3]  D. Gervini Robust functional estimation using the median and spherical principal components , 2008 .

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  Jeffrey S. Morris,et al.  Wavelet‐based functional mixed models , 2006, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[6]  D. Richardson,et al.  Myths versus facts in emergency department overcrowding and hospital access block , 2009, The Medical journal of Australia.

[7]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[8]  J. Ramsay,et al.  Some Tools for Functional Data Analysis , 1991 .

[9]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[10]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[11]  Z. Q. John Lu,et al.  Nonparametric Functional Data Analysis: Theory And Practice , 2007, Technometrics.

[12]  P. Sarda,et al.  SPLINE ESTIMATORS FOR THE FUNCTIONAL LINEAR MODEL , 2003 .

[13]  Wensheng Guo Functional Mixed Effects Models , 2002 .

[14]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[15]  H. Müller,et al.  Functional Data Analysis for Sparse Longitudinal Data , 2005 .

[16]  ACCESS BLOCK AND OVERCROWDING: A LITERATURE REVIEW , 2008 .

[17]  P. Sprivulis,et al.  Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia , 2005, Emergency Medicine Journal.

[18]  G. Jelinek,et al.  The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments , 2006, The Medical journal of Australia.

[19]  B. Silverman,et al.  Smoothed functional principal components analysis by choice of norm , 1996 .

[20]  J. Ramsay,et al.  Principal components analysis of sampled functions , 1986 .

[21]  M. Wand,et al.  Simple fitting of subject‐specific curves for longitudinal data , 2005, Statistics in medicine.

[22]  S. Wood Modelling and smoothing parameter estimation with multiple quadratic penalties , 2000 .

[23]  Spencer Graves,et al.  Functional Data Analysis with R and MATLAB , 2009 .

[24]  C. Denaro Access block: it's all about available beds , 2009, The Medical journal of Australia.

[25]  Xiaotong Shen,et al.  Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes , 2001 .

[26]  E. A. Sylvestre,et al.  Principal modes of variation for processes with continuous sample curves , 1986 .

[27]  J. Miller Numerical Analysis , 1966, Nature.

[28]  D. Richardson,et al.  Increase in patient mortality at 10 days associated with emergency department overcrowding , 2006, The Medical journal of Australia.

[29]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[30]  P. Sarda,et al.  Functional linear model , 1999 .