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Planning for subacute care: predicting demand using acute activity data

Citation

Green, JP and McNamee, JP and Kobel, C and Seraji, MHR and Lawrence, SJ, Planning for subacute care: predicting demand using acute activity data, Australian Health Review, 40 pp. 686-690. ISSN 0156-5788 (2016) [Refereed Article]

Copyright Statement

Copyright 2016 AHHA

DOI: doi:10.1071/AH15192

Abstract

Objective: The aim of the present study was to develop a robust model that uses the concept of ‘rehabilitationsensitive’ Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals.

Methods: The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre.

Results: The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs.

Conclusions: The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning.

Item Details

Item Type:Refereed Article
Keywords:diagnostic related groups; subacute care; health services; regression analysis
Research Division:Medical and Health Sciences
Research Group:Public Health and Health Services
Research Field:Health Care Administration
Objective Division:Health
Objective Group:Health and Support Services
Objective Field:Evaluation of Health Outcomes
Author:Lawrence, SJ (Dr Suanne Lawrence)
ID Code:108169
Year Published:2016
Deposited By:Health Sciences
Deposited On:2016-04-08
Last Modified:2017-03-16
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