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British Columbia's pay-for-performance experiment: Part of the solution to reduce emergency department crowding?

2013· article· en· W2140554383 on OpenAlexaffabout
Amy Cheng, Jason M. Sutherland

Bibliographic record

VenueHealth Policy · 2013
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of British ColumbiaRoyal Columbian Hospital
Fundersnot available
KeywordsEmergency departmentOvercrowdingIncentivePay for performanceIncentive programHealth departmentMedicineCrowdingMedical emergencyBusinessPublic healthPsychologyEconomicsNursingEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Emergency department (ED) overcrowding continues to be a well-publicized problem in a number of countries. In British Columbia, a province in Canada, an ED pay-for-performance (ED P4P) program was initiated in 2007 to create financial incentives for hospitals to reduce patients' ED length of stay (ED LOS). This study's objectives are to determine if the ED P4P program is associated with decreases in ED LOS, and to address the ED P4P program's limitations. METHODS: We analyze monthly hospital-level ED LOS time data since the inception of the financial incentives. Since the ED P4P program was phased in at different hospitals from different health authorities over time, hospitals' data from only two regional health authorities are included in the study. RESULTS: We find association between the implementation of ED P4P and ED LOS time data. However, due to the lack of control data, the findings cannot demonstrate causality. Furthermore, our findings are from hospitals in the greater Vancouver area only. INTERPRETATION: BC's ED P4P was introduced to create incentives for hospitals to reduce ED LOS by providing incremental incentive funding. Available data indicate that the ED P4P program is associated with mixed successes in reducing ED LOS among participating hospitals.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.032
GPT teacher head0.359
Teacher spread0.326 · 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 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

Citations15
Published2013
Admission routes2
Has abstractyes

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