What has driven acute public hospital expenditure growth in South Australia? An analysis of the relative importance of major expenditure drivers between 2006–07 and 2017–18
Bibliographic record
Abstract
Objective The aim of this study was to investigate whether increasing costs of delivering care have driven real growth in acute public hospital expenditure in South Australia (SA) and what has contributed to these real cost increases. Methods Using published time-series data, we decomposed inflation-adjusted growth in per capita total acute public hospital recurrent expenditure into its major utilisation and cost components to evaluate their relative contribution over the 12 years to 2017-18. Results Real per capita total acute public hospital recurrent expenditure grew by AU$667 (45.2%) over the 12-year period; of this, 86.0% was from real growth in input costs per weighted activity unit, with real growth in the average salaries of hospital staff accounting for AU$247 or 37.0%. Hospital utilisation rates contributed a minor 14.0%. Conclusion Over the 12 years to 2017-18, real growth in average clinical salaries was a more important driver of real growth in per capita total acute public hospital expenditure than rates of hospital utilisation. This would be facilitated by improvements in the scope, accuracy, quality and consistency of published national hospital data. What is known about the topic? Public hospital expenditure is one of the largest and fastest growing areas of government expenditure in Australia. Policy narratives often centre around demand pressures from an increasingly older, overweight, and chronically ill population. Comparatively little attention has been paid to the influence of increases in real input costs within the Australian context. What does this paper add? Real salary growth has been a major driver of acute public hospital recurrent expenditure growth in SA, whereas hospital utilisation rates have played a minor role. What are the implications for practitioners? A clearer understanding of the main drivers of acute public hospital expenditure growth and the resulting benefits to population health is needed to guide the efficient and sustainable use of scarce healthcare resources.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".