MétaCan
Menu
Back to cohort
Record W4390033292 · doi:10.7202/1107004ar

The Impact of Health Care Cost Increases on Fraud and Economic Waste

2023· article· en· W4390033292 on OpenAlexaffvenue
M. Martin Boyer, Pierre-Thomas Léger

Bibliographic record

VenueAssurances et gestion des risques · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHealth careBusinessHealth care costEnvironmental healthNatural resource economicsEconomicsMedicineEconomic growth

Abstract

fetched live from OpenAlex

In a model of imperfect information with costly auditing, we examine the effect of increases in health-care costs and general inflation on the optimal health-insurance policy and on waste. We show that in such a setting, individuals will buy more than full insurance. Moreover, as the cost of medical care increases, consumers (i.e., patients) are less likely to file unjustified claims while insurance providers audit with a lower probability. As a result, waste associated with costly auditing is reduced. We also show that a general increase in the opportunity cost of illness (reflected through lost earnings due to illness) also decreases waste, but not as much as health-care cost increases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.527
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.472
Teacher spread0.398 · 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 teacher head, 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAssurances et gestion des risquesSame topicGlobal Health Care IssuesFrench-language works237,207