Scoping medical tourism and international hospital accreditation growth
Bibliographic record
Abstract
PURPOSE: Uwe Reinhardt stated that medical tourism can do to the US healthcare system what the Japanese automotive industry did to American carmakers after Japanese products developed a value for money and reliability reputation. Unlike cars, however, healthcare can seldom be test-driven. Quality is difficult to assess after an intervention (posteriori), therefore, it is frequently evaluated via accreditation before an intervention (a priori). This article aims to scope the growth in international accreditation and its relationship to medical tourism markets. DESIGN/METHODOLOGY/APPROACH: Using self-reported data from Accreditation Canada, Joint Commission International (JCI) and Australian Council on Healthcare Standards (ACHS), this article examines how quickly international accreditation is increasing, where it is occurring and what providers have been accredited. FINDINGS: Since January 2000, over 350 international hospitals have been accredited; the JCI's total nearly tripling between 2007-2011. Joint Commission International staff have conducted most international accreditation (over 90 per cent). Analysing which countries and regions where the most international accreditation has occurred indicates where the most active medical tourism markets are. However, providers will not solely be providing care for medical tourists. PRACTICAL IMPLICATIONS: Accreditation will not mean that mistakes will never happen, but that accredited providers are more willing to learn from them, to varying degrees. If a provider has been accredited by a large international accreditor then patients should gain some reassurance that the care they receive is likely to be a good standard. ORIGINALITY/VALUE: The author questions whether commercializing international accreditation will improve quality, arguing that research is necessary to assess the accreditation of these growing markets.
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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 | 0.040 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.037 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".