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Record W3060981666 · doi:10.1002/hec.4145

Stirring the pot: Switching from blended fee‐for‐service to blended capitation models of physician remuneration

2020· article· en· W3060981666 on OpenAlexafffundabout
Nibene Habib Somé, Rose Anne Devlin, Nirav Mehta, Gregory S. Zaric, Sisira Sarma

Bibliographic record

VenueHealth Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of OttawaInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental HealthWestern University
FundersCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health Research
KeywordsCapitationRemunerationFee-for-servicePaymentService (business)Capitation feeBusinessMedicineFamily medicineHealth careEconomicsFinanceMarketing

Abstract

fetched live from OpenAlex

In Canada's most populous province, Ontario, family physicians may choose between the blended fee-for-service (Family Health Group [FHG]) and blended capitation (Family Health Organization [FHO] payment models). Both models incentivize physicians to provide after-hours (AH) and comprehensive care, but FHO physicians receive a capitation payment per enrolled patient adjusted for age and sex, plus a reduced fee-for-service while FHG physicians are paid by fee-for-service. We develop a theoretical model of physician labor supply with multitasking to predict their behavior under FHG and FHO, and estimable equations are derived to test the predictions empirically. Using health administrative data from 2006 to 2014 and a two-stage estimation strategy, we study the impact of switching from FHG to FHO on the production of a capitated basket of services, after-hours services and nonincentivized services. Our results reveal that switching from the FHG to FHO reduces the production of capitated services to enrolled patients and services to nonenrolled patients by 15% and 5% per annum and increases the production of after-hours and nonincentivized services by 8% and 15% per annum.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.135
GPT teacher head0.292
Teacher spread0.157 · 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 designTheoretical or conceptual
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

Citations29
Published2020
Admission routes3
Has abstractyes

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