Planning for subacute care: predicting demand using acute activity data

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.