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

Activity Based Funding (ABF) Australian Refined Diagnosis Related Groups (AR-DRG) Casemix Diagnosis Complexity Level (DCL) Episode Clinical Complexity Score (ECCS) Hospital resource utilisation

Journal

Health policy (Amsterdam, Netherlands)
ISSN: 1872-6054
Titre abrégé: Health Policy
Pays: Ireland
ID NLM: 8409431

Informations de publication

Date de publication:
11 2019
Historique:
received: 17 04 2019
revised: 07 08 2019
accepted: 19 08 2019
pubmed: 12 9 2019
medline: 17 9 2020
entrez: 12 9 2019
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 31506190
pii: S0168-8510(19)30202-7
doi: 10.1016/j.healthpol.2019.08.012
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Pagination

1049-1052

Informations de copyright

Copyright © 2019. Published by Elsevier B.V.

Auteurs

Vera Dimitropoulos (V)

National Centre for Classification in Health, University of Sydney, Australia.

Trent Yeend (T)

Australian Institute of Health Innovation, Macquarie University, Australia.

Qingsheng Zhou (Q)

National Centre for Classification in Health, University of Sydney, Australia. Electronic address: Qingsheng.zhou@sydney.edu.au.

Stuart McAlister (S)

Independent Consultant, Australia.

Michael Navakatikyan (M)

Population Wellbeing and Environment Research Lab., University of Wollongong, Australia.

Philip Hoyle (P)

Royal North Shore Hospital, Sydney, Australia.

John Pilla (J)

Independent Consultant, Australia.

Carol Loggie (C)

Centre for Health Service Development, University of Wollongong, Australia.

Anne Elsworthy (A)

Independent Hospital Pricing Authority, Australia.

Ric Marshall (R)

National Centre for Classification in Health, University of Sydney, Australia.

Richard Madden (R)

National Centre for Classification in Health, University of Sydney, Australia.

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