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Record W235142587

Medical Tourism: A Look at How Medical Outsourcing Can Reshape Health Care

2014· article· en· W235142587 on OpenAlexaboutno aff
Rebecca Bennie

Bibliographic record

VenueTexas international law journal · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsMedical tourismTourismOutsourcingHealth careBusinessPublic relationsEconomic growthMarketingMedicinePolitical scienceLawEconomics
DOInot available

Abstract

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SUMMARYINTRODUCTION 583I. AN OVERVIEW OF MEDICAL TOURISM 584II. THE BENEFITS OF MEDICAL TOURISM 587III. THE RISKS OF MEDICAL TOURISM 591IV. THE UNKNOWN FUTURE OF MEDICAL TOURISM 594V. AUTHOR'S RECOMMENDATIONS 597A. Recommendations for the U.S. Federal and State Governments 597B. Recommendations for Employers and Insurance Providers 598C. Recommendations for Patients 598D. Recommendations for Destination Countries 599CONCLUSION 600INTRODUCTIONIn a world where millions of people face healthcare costs beyond their means or years-long waits for procedures, an increasing number of patients are traveling internationally to receive treatment. This phenomenon has been coined medical tourism.1 Medical tourism offers many benefits to the internationally traveling patient, but the inherent risks may not be fully realized. This Note will give an overview of tourism then enumerate its benefits and risks. Next, the Note will discuss some factors that may shape the future of tourism. Finally, the author will recommend actions to be taken by both home and destination countries to regulate tourism and to capitalize on the opportunities it presents.I. AN OVERVIEW OF MEDICAL TOURISMTraveling abroad to receive care is a concept that has been around since the Roman Empire.2 In recent years, however, the demographics of tourism have been changing. Until recently, tourists were mostly residents of developed countries traveling to receive inexpensive cosmetic surgery.' As the cost of healthcare began to rise in industrialized countries, particularly in the United States, the face of tourism began to change to include individuals seeking affordable and timely alternatives to surgery or treatment in their home countries.4 Today, tourism is estimated to be a USD 100 billion industry, and hundreds of thousands of patients are traveling abroad for treatment as the globalization of healthcare continues.5Howard Bye lists six reasons people seek care abroad: (1) to receive specific treatments not found in their own countries; (2) to obtain more immediate surgery or other care; (3) to receive lower-cost dental and services; (4) to get treatment not covered by their health insurance; (5) to purchase cheaper prescription drugs; and (6) to shop for procedures not approved by regulatory bodies in their home countries, such as the Food and Drug Administration.6 Medical tourists commonly seek nonemergency surgical care,7 elective cosmetic surgeries,8 fertility treatments,9 and alternative, or holistic, medicine.10While the motivations of tourists are varied, most tourists today fall within one of a few general categories. Some are from countries that ration healthcare, such as Canada and the United Kingdom, and are looking to avoid long waiting lists for treatment in their home countries. Others are uninsured or underinsured Americans, including U.S. retirees who do not yet qualify for Medicare.12 A third group is composed of middle-class Americans seeking cosmetic surgery that is not covered by their insurance or that is cheaper than their insurance deductibles.11 The final group is the affluent upper-class of developing countries, which seeks care in the United States or other developed countries in order to receive a higher quality of care than the patient-tourist would receive at home.Other ways of categorizing tourists include differentiating them based on how they pay for their care abroad and based on destination. Tourists pay one of three ways: (1) out of pocket, (2) through private insurance that has innetwork foreign providers, and (3) through government insurance that has partnered with foreign providers. …

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0340.004

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 head0.408
Teacher spread0.375 · 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
GenreEmpirical

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

Citations7
Published2014
Admission routes1
Has abstractyes

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