Medical Tourism: A Cost or Benefit to the NHS?
Bibliographic record
Abstract
'Medical Tourism' - the phenomenon of people travelling abroad to access medical treatment - has received increasing attention in academic and popular media. This paper reports findings from a study examining effect of inbound and outbound medical tourism on the UK NHS, by estimating volume of medical tourism and associated costs and benefits. A mixed methods study it includes analysis of the UK International Passenger Survey (IPS); interviews with 77 returning UK medical tourists, 63 policymakers, NHS managers and medical tourism industry actors policymakers, and a review of published literature. These informed costing of three types of treatments for which patients commonly travel abroad: fertility treatment, cosmetic and bariatric surgery. Costing of inbound tourism relied on data obtained through 28 Freedom-of-Information requests to NHS Foundation Trusts. Findings demonstrate that contrary to some popular media reports, far from being a net importer of patients, the UK is now a clear net exporter of medical travellers. In 2010, an estimated 63,000 UK residents travelled for treatment, while around 52,000 patients sought treatment in the UK. Inbound medical tourists treated as private patients within NHS facilities may be especially profitable when compared to UK private patients, yielding close to a quarter of revenue from only 7% of volume in the data examined. Costs arise where patients travel abroad and return with complications. Analysis also indicates possible savings especially in future health care and social costs averted. These are likely to be specific to procedures and conditions treated. UK medical tourism is a growing phenomenon that presents risks and opportunities to the NHS. To fully understand its implications and guide policy on issues such as NHS global activities and patient safety will require investment in further research and monitoring. Results point to likely impact of medical tourism in other universal public health systems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".