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Record W4292739225 · doi:10.1111/poms.13830

Care coordination for healthcare referrals under a shared‐savings program

2022· article· en· W4292739225 on OpenAlexafffund
Fernanda Bravo, Retsef Levi, Georgia Perakis, Gonzalo Romero

Bibliographic record

VenueProduction and Operations Management · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
FundersAir Force Office of Scientific ResearchDivision of Civil, Mechanical and Manufacturing InnovationNatural Sciences and Engineering Research Council of CanadaCenter for Hierarchical Manufacturing, National Science FoundationNational Science Foundation
KeywordsCapitationIncentiveBeneficiaryBusinessReferralHealth carePopulationActuarial scienceCost sharingFee-for-serviceFinanceEconomicsMedicineMicroeconomicsNursingEnvironmental health

Abstract

fetched live from OpenAlex

Accountable care organizations (ACOs) are responsible for the quality and cost of care of specified patient populations, including the cost of referrals. Motivated by this environment, we study care coordination for healthcare referrals. We consider an ACO that refers an uncertain number of patients from its attributed population to a preferred external provider for specialized health services. ACOs are typically paid under the Medicare Shared Savings Program (MSSP). Under the MSSP, the payer sets a spending benchmark for the beneficiary population during a fixed time period and shares any gains (losses) relative to it with the ACO. During the billing period, all services delivered to the attributed population by the ACO and external providers continue to be reimbursed under fee‐for‐service. Gains (losses) are determined at the end of the period by comparing the actual spending, which includes all care expenses (regular visits, referrals, and failed treatments) incurred by the payer in the period to the predefined benchmark. In this environment, the ACO and external providers—the latter not compensated under the MSSP—lack incentives to invest enough in care coordination initiatives. We study financial incentive mechanisms between the ACO and its preferred external provider to achieve integrated care coordination in referral markets under the MSSP. We show that traditional fee‐for‐service and capitation agreements do not provide sufficient incentives for care coordination in referral markets. However, a risk‐ and cost‐sharing mechanism can induce integrated care coordination efforts while satisfying the ACO and provider's participation constraints. We characterize a family of such mechanisms and numerically study the variability of the ACO and the external provider's profit. We demonstrate that this type of agreement can be used not only to induce integrated care coordination but can also result in a Pareto improvement in profit variability. We also illustrate the impact of the different MSSP risk tracks parameters on the performance of this care coordination mechanism, including their effect on the quality of care and the payer's mean spending.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.784

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 studies0.0010.000
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 head0.086
GPT teacher head0.325
Teacher spread0.239 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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