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Record W6988110539

Wizards of Oz? What the UK can learn from Australia's healthcare system

2021· other· en· W6988110539 on OpenAlexaboutno aff

Bibliographic record

VenueEconstor (Econstor) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careCounterfactual thinkingHealth insuranceSocial insuranceHealthcare systemPublic health insuranceSelf-insuranceInsurance policy
DOInot available

Abstract

fetched live from OpenAlex

Australia and the UK had very similar healthcare systems until the end of the 1940s, when they diverged. The UK created the National Health Service, while Australia opted for more gradual reforms within its existing system. We can see the Australian system as a plausible counterfactual for how healthcare in Britain might have evolved if the NHS had never been created. The Australian model of today is best described as a multi-layered hybrid system. It is, in the main, a public health insurance system, comparable to the systems in France, Canada, Taiwan and South Korea. A universal insurance programme (Medicare) pays for most healthcare costs, but Medicare does not run any healthcare facilities of its own. Instead, it maintains contractual relations with a range of healthcare providers. On top of universal public health insurance, most Australians have private health insurance (PHI). PHI potentially offers faster access to treatment, greater choice, higher levels of comfort, and additional services not covered by Medicare. Private health insurers in Australia are not allowed to discriminate on the basis of individual health risks. A person in poor health pays the same insurance premium as a person in good health. Thus, private health insurance in Australia is similar to social health insurance (SHI) in Europe or Israel. Australians with PHI tend to use the public system less. This is recognised in the Australian tax and transfer system: people with PHI receive a rebate, which effectively lowers their public insurance premium by around a quarter. This makes PHI more widely affordable. The Australian system is more decentralised than the British one. Australia's nine regions (the states and territories) are fully responsible for managing their own hospital sectors. The system is also more pluralistic. About one in three hospitals (adjusted for hospital size) are private. Total healthcare spending is lower in Australia, and it has been for nearly two decades. In 2019, it stood at 9.3 per cent of GDP, compared to 10.3 per cent in the UK. Public healthcare spending stood at 6.3 per cent of GDP in Australia, and 8 per cent of GDP in the UK. Australia achieves substantially better healthcare outcomes than the UK. Cancer survival rates are several percentage points higher, while heart attack and stroke mortality rates are several percentage points lower. In terms of Mortality Amenable to Healthcare (a measure of avoidable premature deaths), Australia is about a decade ahead of the UK. Even the Commonwealth Fund study (a study which is uniquely flattering to the NHS) acknowledges the superiority of the Australian system when it comes to outcomes. The NHS, however, appears to have a lead when it comes to avoidable hospitalisation rates for chronic conditions. On average, NHS hospitals also have shorter waiting times for various types of surgery than public hospitals in Australia. We cannot directly compare the two health systems in terms of their Covid-19 performance, because the NHS had to deal with a vastly greater Covid-19 caseload than the Australian system. We can, however, note that the UK still had higher Covid death rates and excess death rates than a number of countries which had to deal with an even greater caseload. The Australian system has its shortcomings, complexities and inconsistencies but it also gets some important things right. The ideas of tax rebates for PHI, and community rating in PHI, are certainly worth looking into, and so is the generally more decentralised nature of the Australian system. If nothing else, the Australian system can teach us to be more relaxed about the benefits of private sector involvement in healthcare delivery, private insurance and decentralisation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.147
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.007
Scholarly communication0.0110.017
Open science0.0010.006
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0290.006

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.

Opus teacher head0.033
GPT teacher head0.278
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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