Abstract
In this study, we utilize various approaches for efficiency analysis to explore the state of efficiency of public hospitals in Queensland, Australia, in the year 2016/17. Besides the traditional nonparametric approaches like DEA and FDH, we also use a more recent and very promising robust approach–order-\(\alpha \) quantile frontier estimators (Aragon et al. 2005). Upon obtaining the individual estimates from various approaches, we also analyze performance on a more aggregate level—the level of Local Hospital Networks by using an aggregate efficiency measure constructed from the estimated individual efficiency scores. Our analysis suggests that the relatively low efficiency of some Local Hospital Networks in Queensland can be partially explained by the fact that the majority of their hospitals are small and located in remote areas.
We dedicate our modest contribution to Professor Christine Thomas-Agnan–a great Scholar who together with various colleagues have originated, developed and inspired many interesting directions in research, among which is the concept of partial \(\alpha \)-frontier modelling that we use in this work.
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Notes
- 1.
Under Activity Based Funding, hospitals are reimbursed based on the number and the complexity of patient care episodes they provide. Hospitals receive a fixed rate for each episode, and the value of the fixed rate is determined by the DRG to which the episode belongs.
- 2.
In the fiscal year 2016/17, Australia spent $181 billion on healthcare (more than $7,400 per person and 10% of its GDP), about a 57% increase since 2006/07 (after adjusting for inflation). This turns out to be an average annual growth rate of 4.67% over the decade: around 2% higher than average growth of GDP (Australian Institute of Health andWelfare 2018).
- 3.
Although the order-\(\alpha \) quantile frontier estimators can provide new insights from the data compared to the traditional nonparametric estimators, the traditional approach, especially the CRS-DEA, still has its merits and value in itself (see more discussion in Sect. 4).
- 4.
For the cases of multiple-output, one can either follow the multivariate conditional quantile approach proposed by Daouia and Simar (2007) or utilize aggregation techniques to aggregate outputs. In this study, we adopt Daraio and Simar ’s (2007) approach (the approach based on Principal Component Analysis) to aggregate hospital outputs into a single output measure. An alternative approach would be to use a price-based aggregation approach (Zelenyuk 2020).
- 5.
Other standard regularity conditions are “No Free Lunch” and “Producing Nothing is Possible” (see more details in Sickles and Zelenyuk 2019).
- 6.
Being similar to recent studies in the literature (e.g. Clement et al. 2008; Hu et al. 2012; Besstremyannaya 2013; Chowdhury and Zelenyuk 2016), we measure efficiency in output direction because the level of inputs used in public hospitals is usually fixed and influenced by external factors (the budget of hospitals are usually planned in advance with relatively fixed (typically 12+ months) labour contracts and huge investment in fixed inputs). Moreover, an output-oriented model is consistent with the aim of Queensland Health, which is to maximize healthcare services delivered to local community from given resources (see Queensland Health 2016).
- 7.
- 8.
- 9.
- 10.
There are 16 HHSs in Queensland, but only 15 HHSs directly manage and operate public hospitals in defined local geographical areas, the remaining HHS is a specialist statewide HHS dedicated to caring for children and young people from across Queensland.
- 11.
Public hospitals in Queensland include acute hospitals, mixed sub- and non-acute hospitals, early parenting centres, women’s and children’s hospitals, and psychiatric hospitals. We only consider public acute hospitals, which account for more than 90% of inpatient cases treated. Our sample does not include hospitals that were just opened in 2017 and hospitals that are not operated by a HHS.
- 12.
- 13.
- 14.
Ideally, outputs of hospitals should be measured by the improvement in medical condition of patients. However, it is technically difficult to obtain this measure in practice, thus most of the hospital efficiency studies use quantities of services as an alternative measure of hospital outputs (Hollingsworth 2008).
- 15.
Public acute hospitals in Australia are divided into five groups listed in descending order of activity volume and service diversification, as follows: principal referral hospitals, public acute group A hospitals, public acute group B hospitals, public acute group C hospitals, public acute group D hospitals. According to Australian Institute of Health and Welfare (2015), hospitals in the first three groups are generally larger than hospitals in the last two groups.
- 16.
The classification is based on the remoteness area information provided in the Australian hospital peer groups in which the remoteness of a hospital is measured by the physical road distance to its nearest urban centre.
- 17.
Note that the IDs here are not the real ID but randomly generated for each HHS.
- 18.
We thank the anonymous referee for this insight.
- 19.
- 20.
Note that the IDs here are not the real ID but randomly generated for each hospital.
- 21.
K-mean clustering is an unsupervised machine learning algorithm helping cluster data into a predetermined number of clusters so as to minimize the within-cluster sum of squares.
- 22.
A deeper analysis on hospital efficiency based on geographical location (e.g. with some spatial maps) could be a fruitful research direction. Some hospitals in major cities may benefit from the presence of other hospitals to adjust their capacities or to select their patients, while this may be not possible for hospitals in remote areas. Moreover, some hospitals in urban areas may be in intensive competition, while others in rural areas may be local monopolies. We thank the anonymous referee for this insight.
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Acknowledgements
We thank the Editor and two anonymous referees for many fruitful comments that helped improving this paper substantially. We acknowledge the support from our institution. We also acknowledge the financial support from the Australian Research Council (from the ARC Future Fellowship grant FT170100401). We thank Dan O’Halloran for his fruitful comments. We also thank David Du, Hong Ngoc Nguyen, Zhichao Wang and Evelyn Smart for their feedback from proofreading. We acknowledge and thank Queensland Health for providing part of the data that we used in this study. These individuals and organizations are not responsible for the views expressed in this paper.
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Nguyen, B.H., Zelenyuk, V. (2021). Robust Efficiency Analysis of Public Hospitals in Queensland, Australia. In: Daouia, A., Ruiz-Gazen, A. (eds) Advances in Contemporary Statistics and Econometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-73249-3_12
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