A model-based approach to the analysis of patterns of length of stay in institutional long-term care.
Understanding the pattern of length of stay in institutional long-term care has important practical implications in the management of long-term care. Furthermore, residents' attributes are believed to have significant effects on these patterns. In this paper, we present a model-based approach to extract, from a routinely gathered administrative social care dataset, high-level length-of-stay patterns of residents in long-term care. This approach extends previous work by the authors to incorporate residents' features. Two applications using data provided by a local authority in England are presented to demonstrate the potential use of this approach.
Xie
Haifeng
H
Health and Social Care Modeling Group, Department of Information Systems, Cavendish School of Computer Science, University of Westminster, London, UK. xieh@wmin.ac.uk
Chaussalet
Thierry J
TJ
Millard
Peter H
PH
D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D007902:Length of Stay / Q000706:statistics & numerical data*; D008134:Long-Term Care / Q000706:statistics & numerical data*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D016019:Survival Analysis; D006113:United Kingdom