scholarly journals Casemix funding for a Specialist Paediatrics Hospital:A hedonic regression approach

2000 ◽  
Vol 23 (3) ◽  
pp. 171
Author(s):  
John F P Bridges ◽  
Ralph M Hanson
Keyword(s):  
Explanatory Variable ◽  
Diagnosis Related Groups ◽  
Cost Data ◽  
Hedonic Regression ◽  
Hedonic Models ◽  
Hedonic Analysis ◽  
Patient Level ◽  
Funding Model ◽  
Casemix Funding ◽  
Regression Approach

This paper inquires into the effects that Diagnosis Related Groups (DRGs) have had on the ability to explain patient-levelcosts in a specialist paediatrics hospital. Two hedonic models are estimated using 1996/97 New Children'sHospital (NCH) patient level cost data, one with and one without a casemix index (CMI). The results show thatthe inclusion of a casemix index as an explanatory variable leads to a better accounting of cost. The full hedonicmodel is then used to simulate a funding model for the 1997/98 NCH cost data. These costs are highly correlatedwith the actual costs reported for that year. In addition, univariate regression indicates that there has been inflationin costs in the order of 4.8% between the two years. In conclusion, hedonic analysis can provide valuable evidencefor the design of funding models that account for casemix.

A review of the complexity adjustment in the Korean Diagnosis-Related Group (KDRG)

2018 ◽  
Vol 49 (1) ◽  
pp. 62-68 ◽  
Author(s):  
Sujeong Kim ◽  
Chaiyoung Jung ◽  
Junheum Yon ◽  
Hyeonseon Park ◽  
Hunsik Yang ◽  
...  
Keyword(s):  
Diagnosis Related Groups ◽  
Claim Data ◽  
Hospital Charge ◽  
Diagnosis Related Group ◽  
Cost Data ◽  
Hospital Charges ◽  
Average Charge ◽  
Average Hospital ◽  
Adjustment Mechanism ◽  
Related Group

Background: The Korean Diagnosis-Related Groups (KDRG) was revised in 2003, modifying the complexity adjustment mechanism of the Australian Refined Diagnosis-Related Groups (AR-DRGs). In 2014, the Complication and Comorbidity Level (CCL) of the existing AR-DRG system was found to have very little correlation with cost. Objective: Based on the Australian experience, the CCL for KDRG version 3.4 was reviewed. Method: Inpatient claim data for 2011 were used in this study. About 5,731,551 episodes, which had one or no complication and comorbidity (CC) and met the inclusion criteria, were selected. The differences of average hospital charges by the CCL were analysed in each Adjacent Diagnosis-Related Group (ADRG) using analysis of variance followed by Duncan’s test. The patterns of differences were presented with R 2 in three patterns: The CCL reflected the complexity well (VALID); the average charge of CCL 2, 3, 4 was greater than CCL 0 (PARTIALLY VALID); the CCL did not reflect the complexity (NOT VALID). Results: A total of 114 (19.03%), 190 (31.72%) and 295 (49.25%) ADRGs were included in VALID, PARTIALLY VALID and NOT VALID, respectively. The average R 2 for hospital charge of CCL was 4.94%. The average R 2 in VALID, PARTIALLY VALID and NOT VALID was 4.54%, 5.21%, and 4.93%, respectively. Conclusion: The CCL, the first step of complexity adjustment using secondary diagnoses, exhibited low performance. If highly accurate coding data and cost data become available, the performance of secondary diagnosis as a variable to reflect the case complexity should be re-evaluated. Implications: Lack of reviewing the complexity adjustment mechanism of the KDRG since 2003 has resulted in outdated CC lists and levels that no longer reflect the current Korean healthcare system. Reliable cost data (vs. charge) and accurate coding are essential for accuracy of reimbursement.

scholarly journals Estimating Systematic Risk under Extremely Adverse Market Conditions*

2017 ◽  
Vol 17 (3) ◽  
pp. 432-461 ◽  
Author(s):  
Maarten R C van Oordt ◽  
Chen Zhou
Keyword(s):  
Mean Squared Error ◽  
Explanatory Variable ◽  
Asymptotic Properties ◽  
Estimation Method ◽  
Tail Dependence ◽  
Stock Portfolios ◽  
Squared Error ◽  
Regression Approach ◽  
Model Coefficient ◽  
Heavy Tailed

AbstractThis paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. Simulations show that our estimation method yields a lower mean-squared error than regressions conditional on tail observations. In an empirical application, we illustrate the better performance of our approach relative to the conditional regression approach in projecting the losses of industry-specific stock portfolios in the event of a market crash.

scholarly journals HOW WELL DO DIAGNOSIS-RELATED GROUPS FOR APPENDECTOMY EXPLAIN VARIATIONS IN RESOURCE USE? AN ANALYSIS OF PATIENT-LEVEL DATA FROM 10 EUROPEAN COUNTRIES

2012 ◽  
Vol 21 ◽  
pp. 30-40 ◽  
Author(s):  
Anne Mason ◽  
Zeynep Or ◽  
Thomas Renaud ◽  
Andrew Street ◽  
Josselin Thuilliez ◽  
...  
Keyword(s):  
Resource Use ◽  
Diagnosis Related Groups ◽  
European Countries ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Funding issues for Victorian hospitals:the risk-adjusted vision beyond casemix funding

2000 ◽  
Vol 23 (3) ◽  
pp. 145 ◽  
Author(s):  
Kathryn Antioch ◽  
Michael Walsh
Keyword(s):  
Cystic Fibrosis ◽  
State Government ◽  
Health Maintenance Organisation ◽  
Teaching Hospitals ◽  
Averaging Principle ◽  
Average Price ◽  
Health Maintenance ◽  
Australian Health ◽  
Funding Model ◽  
Casemix Funding

This paper discusses casemix funding issues in Victoria impacting on teaching hospitals. For casemix payments to beacceptable, the average price and cost weights must be set at an appropriate standard. The average price is based ona normative, policy basis rather than benchmarking. The 'averaging principle' inherent in cost weights has resulted insome AN-DRG weights being too low for teaching hospitals that are key State-wide providers of high complexityservices such as neurosurgery and trauma. Casemix data have been analysed using international risk adjustmentmethodologies to successfully negotiate with the Victorian State Government for specified grants for several highcomplexity AN-DRGs. A risk-adjusted capitation funding model has also been developed for cystic fibrosis patientstreated by The Alfred, called an Australian Health Maintenance Organisation (AHMO). This will facilitate thedevelopment of similar models by both the Victorian and Federal governments.

scholarly journals WINDFARMS AND RESIDENTIAL PROPERTY VALUES

2014 ◽  
Vol 18 (2) ◽  
pp. 116-124 ◽  
Author(s):  
Iona McCarthy ◽  
Hatice Ozer Balli
Keyword(s):  
Property Values ◽  
Residential Property ◽  
Hedonic Regression ◽  
Open Market ◽  
Hedonic Models ◽  
Property Value ◽  
Viewshed Analysis ◽  
Residential Property Values ◽  
Visual Impact ◽  
The Impact

This study examines the effect that windfarm visibility has on residential property values using a hedonic regression model. The study area is Ashhurst, New Zealand, a township of approximately 900 dwellings. Ashhurst is located within eight kilometres of two separate windfarms that were developed between 1998 and 2007 comprising 103x660kW turbines, 31x3MW turbines, and 55x1.65MW turbines. The analysis uses the 945 open market house sales that occurred in Ashhurst between 1995 and 2008. Visual impact of turbines is studied to capture the impact of windfarms and it is assessed using GIS viewshed analysis and by field inspection. The hedonic models had satisfactory explanatory performance and in each case indicated that the turbines located between 2.5 and 6 kilometres from the township of Ashhurst had no significant impact on property value.

The Distribution of Health Care Costs and Their Statistical Analysis for Economic Evaluation

1998 ◽  
Vol 3 (4) ◽  
pp. 233-245 ◽  
Author(s):  
Andrew Briggs ◽  
Alastair Gray
Keyword(s):  
Health Care ◽  
Health Care Costs ◽  
Health Care Cost ◽  
Cost Data ◽  
Parametric Methods ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data ◽  
Care Costs ◽  
Non Parametric

Objective: Where patient level data are available on health care costs, it is natural to use statistical analysis to describe the differences in cost between alternative treatments. Health care costs are, however, commonly considered to be skewed, which could present problems for standard statistical tests. This review examines how authors report the distributional form of health care cost data and how they have analysed their results. Method: A review of cost-effectiveness studies that collected patient-level data on health care costs. To supplement the review, five datasets on health care costs are examined. Consideration is given to the use of parametric methods on the transformed scale and to non-parametric methods of analysing skewed cost data. Results: Since economic analysis requires estimation in monetary units, the usefulness of transformation-based methods is limited by the inability to retransform cost differences to the original scale. Non-parametric rank sum methods were also found to be of limited use for economic analysis, partly due to the focus on hypothesis testing rather than estimation. Overall, the non-parametric approach of bootstrapping was found to offer a useful test of the appropriateness of parametric assumptions and an alternative method of estimation where those assumptions were found not to hold. Conclusions: Guidelines for the analysis of skewed health care cost data are offered.

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