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Related Experiment Video

Updated: Mar 23, 2026

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

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Planning for subacute care: predicting demand using acute activity data.

Janette P Green1, Jennifer P McNamee1, Conrad Kobel1

  • 1Australian Health Services Research Institute, University of Wollongong, Building 234 (iC Enterprise 1), Innovation Campus, University of Wollongong, Wollongong, NSW 2522, Australia. Email: ;

Australian Health Review : a Publication of the Australian Hospital Association
|April 7, 2016
PubMed
Summary

A new model predicts demand for rehabilitation and geriatric evaluation and management (GEM) care using updated rehabilitation-sensitive Diagnosis Related Groups (DRGs) and patient age. This tool aids Australian health service planning by improving subacute care demand forecasting.

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Last Updated: Mar 23, 2026

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06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

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Area of Science:

  • Health Services Research
  • Geriatric Medicine
  • Health Informatics

Background:

  • Accurate health service planning is crucial, especially with aging populations and recognized underservicing of subacute care.
  • Traditional methods using age-sex standardized utilization rates for Diagnosis Related Groups (DRGs) have limitations in predicting future subacute service needs.
  • The concept of 'rehabilitation-sensitive' DRGs improved prediction but required updating.

Purpose of the Study:

  • To develop a robust predictive model for rehabilitation and geriatric evaluation and management (GEM) care demand.
  • To utilize the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) within this model.
  • To enhance demand prediction for post-acute care in Australian hospitals.

Main Methods:

  • Statistical analysis of national datasets, including acute in-patient data and Australasian Rehabilitation Outcomes Centre data.
  • Logistic regression analysis informed by expert clinicians and jurisdictional advice.
  • Revision and extension of the 'rehabilitation-sensitive' DRG concept to AR-DRG Version 7.0, incorporating patient age.

Main Results:

  • A predictive model was developed, presented as probability tables based on age group and grouped Australian Refined (AR)-DRGs.
  • The model provided a conservative estimate, accounting for 83% of actual rehabilitation or GEM activity observed in national data.
  • The model's application for service planning was demonstrated with an example.

Conclusions:

  • The revised and extended 'rehabilitation-sensitive' DRG model offers an improved tool for predicting demand for rehabilitation and GEM services.
  • The model's incorporation of patient age and updated AR-DRGs enhances prediction accuracy for subacute care needs.
  • This tool enables service planners to improve forecasting by applying patient-level characteristics to acute in-patient administrative datasets.