Benchmarking management practices in Australian public healthcare
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to investigate the quality of management practices of public hospitals in the Australian healthcare system, specifically those in the state-managed health systems of Queensland and New South Wales (NSW). Further, the authors assess the management practices of Queensland and NSW public hospitals jointly and globally benchmark against those in the health systems of seven other countries, namely, USA, UK, Sweden, France, Germany, Italy and Canada. DESIGN/METHODOLOGY/APPROACH: In this study, the authors adapt the unique and globally deployed Bloom et al. (2009) survey instrument that uses a "double blind, double scored" methodology and an interview-based scoring grid to measure and internationally benchmark the management practices in Queensland and NSW public hospitals based on 21 management dimensions across four broad areas of management - operations, performance monitoring, targets and people management. FINDINGS: The findings reveal the areas of strength and potential areas of improvement in the Queensland and NSW Health hospital management practices when compared with public hospitals in seven countries, namely, USA, UK, Sweden, France, Germany, Italy and Canada. Together, Queensland and NSW Health hospitals perform best in operations management followed by performance monitoring. While target management presents scope for improvement, people management is the sphere where these Australian hospitals lag the most. PRACTICAL IMPLICATIONS: This paper is of interest to both hospital administrators and health care policy-makers aiming to lift management quality at the hospital level as well as at the institutional level, as a vehicle to consistently deliver sustainable high-quality health services. ORIGINALITY/VALUE: This study provides the first internationally comparable robust measure of management capability in Australian public hospitals, where hospitals are run independently by the state-run healthcare systems. Additionally, this research study contributes to the empirical evidence base on the quality of management practices in the Australian public healthcare systems of Queensland and NSW.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".