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Record W2928194429 · doi:10.1377/hlthaff.2018.05272

Do Incentive Payments Reward The Wrong Providers? A Study Of Primary Care Reform In Ontario, Canada

2019· article· en· W2928194429 on OpenAlexaffabout
Richard H. Glazier, Michael Green, Eliot Frymire, Alex Kopp, William Hogg, Kamila Premji, Tara Kiran

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsKingston Health Sciences CentreWestern UniversityÉlisabeth Bruyère HospitalInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsCapitationIncentiveEquity (law)PaymentPrimary careIncentive programBusinessAmbulatory careActuarial scienceMedicineFamily medicinePublic economicsFinanceHealth careEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Primary care payment reform in the US and elsewhere usually involves capitation, often combined with bonuses and incentives. In capitation systems, providing care within the practice group is needed to contain costs and ensure continuity of care, yet this is challenging in settings that allow patient choice in access to services. We used linked population-based administrative databases in Ontario, Canada, to examine a substantial payment called the "access bonus" designed to incentivize primary care access and to minimize primary care visits outside of capitation practices. We found that the access bonus flowed disproportionately to physicians outside large cities and to those whose patients made fewer primary care visits, received less after-hours care, made more emergency department visits, and had higher adjusted ambulatory costs. Our findings indicate a lack of alignment between these payments and their intended purpose. Financial incentives should be prospectively evaluated and frequently revisited to ensure relevance, alignment with system goals, efficiency, and equity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.343
Teacher spread0.310 · 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

Citations40
Published2019
Admission routes2
Has abstractyes

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