Objective The aim of the present study was to develop a robust model that uses the concept of ‘rehabilitation-sensitive’ 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.
What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age–sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated ‘rehabilitation-sensitive’. This improved methodology has been used in several Australian jurisdictions.
What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the methodology based on rehabilitation-sensitive AR-DRGs has been extended by updating them to AR-DRG Version 7.0, quantifying the level of ‘sensitivity’ and incorporating the patient’s age to improve the prediction of demand for subacute services.
What are the implications for practitioners? The predictive model takes the form of tables of probabilities that patients will require rehabilitation or GEM care after an acute episode and can be applied to acute in-patient administrative datasets in any Australian jurisdiction or local area. The use of patient-level characteristics will enable service planners to improve their forecasting of demand for these services. Clinicians and jurisdictional representatives consulted during the project regarded the model favourably and believed that it was an improvement on currently available methods.