A Model-Based Approach to the Analysis of Patterns of Length of Stay in Institutional Long-Term Care
| Autor: | Haifeng Xie, Thierry J. Chaussalet, Peter H. Millard |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
medicine.medical_specialty
Local authority Information Storage and Retrieval Pattern Recognition Automated Nursing care Artificial Intelligence Residential care Humans Medicine Computer Simulation Electrical and Electronic Engineering Practical implications Geriatrics Models Statistical Actuarial science business.industry General Medicine Length of Stay Long-Term Care Survival Analysis Markov Chains United Kingdom Computer Science Applications Long-term care Work (electrical) Social care business Biotechnology |
| Zdroj: | IEEE Transactions on Information Technology in Biomedicine. 10:512-518 |
| ISSN: | 1089-7771 |
| DOI: | 10.1109/titb.2005.863820 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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