The Australian National Aged Care Classification (AN-ACC): a new casemix classification for residential aged care.

OBJECTIVE: To develop a casemix classification to underpin a new funding model for residential aged care in Australia. DESIGN, SETTING: Cross-sectional study of resident characteristics in thirty non-government residential aged care facilities in Melbourne, the Hunter region of New South Wales, and northern Queensland, March 2018 - June 2018. PARTICIPANTS: 1877 aged care residents and 1600 residential aged care staff. MAIN OUTCOME MEASURES: The Australian National Aged Care Classification (AN-ACC), a casemix classification for residential aged care based on the attributes of aged care residents that best predict their need for care: frailty, mobility, motor function, cognition, behaviour, and technical nursing needs. RESULTS: The AN-ACC comprises 13 aged care resident classes reflecting differences in resource use. Apart from the class that included palliative care patients, the primary branches were defined by the capacity for mobility; further classification is based on physical capacity, cognitive function, mental health problems, and behaviour. The statistical performance of the AN-ACC was good, as measured by the reduction in variation statistic (RIV; 0.52) and class-specific coefficients of variation. The statistical performance and clinical acceptability of AN-ACC compare favourably with overseas casemix models, and it is better than the current Australian aged care funding model, the Aged Care Funding Instrument (64 classes; RIV, 0.20). CONCLUSIONS: The care burden associated with frailty, mobility, function, cognition, behaviour and technical nursing needs drives residential aged care resource use. The AN-ACC is sufficiently robust for estimating the funding and staffing requirements of residential aged care facilities in Australia.

A new clinical complexity model for the Australian Refined Diagnosis Related Groups.

BACKGROUND: The Australian Refined Diagnosis Related Groups (AR-DRG) underwent a major review in 2014 with changes implemented in Version 8.0 of the classification. The core to the changes was the development of a new methodology to estimate the Diagnosis Complexity Level (DCL) and to aggregate the complexity level of individual diagnoses to the complexity of an entire episode, resulting in an Episode Clinical Complexity Score (ECCS). This paper provides an overview of the new methodology and its application in Version 8.0. METHOD: The AR-DRG V8.0 refinement project was overseen by a Classifications Clinical Advisory Group and a Diagnosis Related Groups (DRG) Technical Group. Admitted Patient Care National Minimum Dataset and the National Hospital Cost Data Collection were used for complexity modelling and analysis. RESULT: In total, Version 8.0 comprised 807 DRGs, including 3 error DRGs. Of the 321 Adjacent DRGs (ADRGs) that had a split, 315 ADRGs used ECCS as the only splitting variable while the remaining 6 ADRGs used splitting variables other than ECCS: 2 used age and 4 used transfer. DISCUSSION AND CONCLUSION: A new episode clinical complexity (ECC) model was developed and introduced in AR-DRG V8.0, replacing the original model introduced in the 1990s. Clear AR-DRG structure principles were established for revising the system. The new complexity model is conceptually based and statistically derived, and results in an improved relationship with actual variations in resource use due to episode complexity.