Performance of UK National Health Service compared with other high income countries: observational study
Bibliographic record
Abstract
Objective To determine how the UK National Health Service (NHS) is performing relative to health systems of other high income countries, given that it is facing sustained financial pressure, increasing levels of demand, and cuts to social care. Design Observational study using secondary data from key international organisations such as Eurostat and the Organization for Economic Cooperation and Development. Setting Healthcare systems of the UK and nine high income comparator countries: Australia, Canada, Denmark, France, Germany, the Netherlands, Sweden, Switzerland, and the US. Main outcome measures 79 indicators across seven domains: population and healthcare coverage, healthcare and social spending, structural capacity, utilisation, access to care, quality of care, and population health. Results The UK spent the least per capita on healthcare in 2017 compared with all other countries studied (UK $3825 (£2972; €3392); mean $5700), and spending was growing at slightly lower levels (0.02% of gross domestic product in the previous four years, compared with a mean of 0.07%). The UK had the lowest rates of unmet need and among the lowest numbers of doctors and nurses per capita, despite having average levels of utilisation (number of hospital admissions). The UK had slightly below average life expectancy (81.3 years compared with a mean of 81.7) and cancer survival, including breast, cervical, colon, and rectal cancer. Although several health service outcomes were poor, such as postoperative sepsis after abdominal surgery (UK 2454 per 100 000 discharges; mean 2058 per 100 000 discharges), 30 day mortality for acute myocardial infarction (UK 7.1%; mean 5.5%), and ischaemic stroke (UK 9.6%; mean 6.6%), the UK achieved lower than average rates of postoperative deep venous thrombosis after joint surgery and fewer healthcare associated infections. Conclusions The NHS showed pockets of good performance, including in health service outcomes, but spending, patient safety, and population health were all below average to average at best. Taken together, these results suggest that if the NHS wants to achieve comparable health outcomes at a time of growing demographic pressure, it may need to spend more to increase the supply of labour and long term care and reduce the declining trend in social spending to match levels of comparator countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".