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Record W2112849135 · doi:10.1377/hlthaff.2010.0204

Higher Fees Paid To US Physicians Drive Higher Spending For Physician Services Compared To Other Countries

2011· article· en· W2112849135 on OpenAlexaboutno aff
Miriam Laugesen, Sherry Glied

Bibliographic record

VenueHealth Affairs · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersCommonwealth FundRobert Wood Johnson FoundationU.S. Department of Health and Human Services
KeywordsBusinessHealth spendingFamily medicineFinanceMedicineHealth careEconomicsHealth insuranceEconomic growth

Abstract

fetched live from OpenAlex

Higher health care prices in the United States are a key reason that the nation's health spending is so much higher than that of other countries. Our study compared physicians' fees paid by public and private payers for primary care office visits and hip replacements in Australia, Canada, France, Germany, the United Kingdom, and the United States. We also compared physicians' incomes net of practice expenses, differences in financing the cost of medical education, and the relative contribution of payments per physician and of physician supply in the countries' national spending on physician services. Public and private payers paid somewhat higher fees to US primary care physicians for office visits (27 percent more for public, 70 percent more for private) and much higher fees to orthopedic physicians for hip replacements (70 percent more for public, 120 percent more for private) than public and private payers paid these physicians' counterparts in other countries. US primary care and orthopedic physicians also earned higher incomes ($186,582 and $442,450, respectively) than their foreign counterparts. We conclude that the higher fees, rather than factors such as higher practice costs, volume of services, or tuition expenses, were the main drivers of higher US spending, particularly in orthopedics.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.083
GPT teacher head0.299
Teacher spread0.216 · 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 designObservational
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

Citations144
Published2011
Admission routes1
Has abstractyes

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