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Published in: The European Journal of Health Economics 2/2004

01-05-2004 | Original Papers

The risk-adjusted vision beyond casemix (DRG) funding in Australia

International lessons in high complexity and capitation

Authors: Kathryn M. Antioch, Michael K. Walsh

Published in: The European Journal of Health Economics | Issue 2/2004

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Abstract

Hospitals throughout the world using funding based on diagnosis-related groups (DRG) have incurred substantial budgetary deficits, despite high efficiency. We identify the limitations of DRG funding that lack risk (severity) adjustment for State-wide referral services. Methods to risk adjust DRGs are instructive. The average price in casemix funding in the Australian State of Victoria is policy based, not benchmarked. Average cost weights are too low for high-complexity DRGs relating to State-wide referral services such as heart and lung transplantation and trauma. Risk-adjusted specified grants (RASG) are required for five high-complexity respiratory, cardiology and stroke DRGs incurring annual deficits of $3.6 million due to high casemix complexity and government under-funding despite high efficiency. Five stepwise linear regressions for each DRG excluded non-significant variables and assessed heteroskedasticity and multicollinearlity. Cost per patient was the dependent variable. Significant independent variables were age, length-of-stay outliers, number of disease types, diagnoses, procedures and emergency status. Diagnosis and procedure severity markers were identified. The methodology and the work of the State-wide Risk Adjustment Working Group can facilitate risk adjustment of DRGs State-wide and for Treasury negotiations for expenditure growth. The Alfred Hospital previously negotiated RASG of $14 million over 5 years for three trauma and chronic DRGs. Some chronic diseases require risk-adjusted capitation funding models for Australian Health Maintenance Organizations as an alternative to casemix funding. The use of Diagnostic Cost Groups can facilitate State and Federal government reform via new population-based risk adjusted funding models that measure health need.
Appendix
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Footnotes
1
Consideration of the relative disadvantage of The Alfred vis a vis other teaching hospitals and the size of any other Risk Adjusted Specified Grants for other teaching hospitals can be further explored using the following formulae in the case of COPD, where severity markers are included into the equation, along with teaching hospital dummy variables for each teaching hospital and all other variables. This specification can be used both to predict why certain hospitals are more expensive than others, as well as to understand whether some factors systematically vary or are the same across all hospitals.
Y
βo11*D1BR+β12*D1LPA+β13*D1HLT+β14*D1LT+β15*D1BIBAP+β16 1AGE+ß18*D1SEX+ß19*D1PROC+ß110*D1DIAG+ß111*D1DISEASE *D1LVF+β17*D1AGE+β18*D1SEX+β19*D1PROC+β110*D1DIAG+β111*D1DISEASE TYPES+β112*D1COMPLEX+β113*D1OUTLIER+β114*D1EMERG+β21*D2BR+β22*D2LPA+β23SEX+ß29*D2PROC+ß210*D2DIAG+ß211*D2DISEASE *D2HLT+β24*D2LT+β25*D2BIPAP+β26*D2LVF+β27*D2AGE+β28*D2SEX+β29 N5*DNBIPAP+ßN6*DNLVF+ßN7*DNAGE+ßN8*DNSEX+ßN9*DNPROC+ßN10*DNDIAG+ßN11*DNDISEASE *D2PROC+β210*D2DIAG+β211*D2DISEASE TYPES+β212*D2COMPLEX+β213*D2OUTLIER+β214*D2EMERG+...βN1*DNBR+βN2*DNLPA+βN3*D*** *DNHLT+βN4*DNLT+βN5*DNBIPAP+βN6*DNLVF+βN7*DNAGE+βN8*DNSEX+β N9*DNPROC+βN10*DNDIAG+βN11 *D NDISEASE TYPES+βN12*DNCOMPLEX+βN13*DNOUTLIER+βN14*DNEMERG+E
Where
Y
=Per patient costs
βo
=Y intercept
βij
=array of coefficients, one set for each of j hospitals
D1BR
=Dummy variable bronchiectasis teaching hospital D1=1, other=0.
D1LPA
=Dummy variable lung part absence teaching hospital D1=1, other=0
D1HLT
=Dummy variable heart and lung transplantation teaching hospital D1, =1, other=0
D1LT
=Dummy variable lung transplantation teaching hospital D1=1, other=0.
D1BIPAP
=Dummy variable Bilevel Positive Airway pressure (BIPAP) teaching hospital D1=1, other=0.
D1LVF
=Dummy variable Left Ventricular Failure teaching hospital D1=1, other=0.
D1AGE
=Patient age, teaching hospital D1=1.
D1SEX
=Dummy variable 1 if male, other=0 (gender of patient), teaching hospital D1
D1PROC
=Number of procedures at teaching hospital D1
D1DIAG
=Number of diagnoses at teaching hospital D1
D1DISEASE TYPES
=Number of body systems at teaching hospital D1
D1COMPLEX
=Dummy variable at teaching hospital D1, 1 of patient classified as high complexity case (PCCL) level 1 if 4. 0 if 3.
D1OUTLIER
=Dummy variable at teaching hospital D1, 1 if patient an outlier on length of stay, otherwise 0
D1EMERG
=Dummy variable at teaching hospital D1, 1 if patient admitted through emergency department, otherwise 0
 
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Metadata
Title
The risk-adjusted vision beyond casemix (DRG) funding in Australia
International lessons in high complexity and capitation
Authors
Kathryn M. Antioch
Michael K. Walsh
Publication date
01-05-2004
Publisher
Springer-Verlag
Published in
The European Journal of Health Economics / Issue 2/2004
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-003-0208-z

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