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Record W2807370102 · doi:10.1377/hlthaff.2017.1358

Comparison Of Hospitals Participating In Medicare’s Voluntary And Mandatory Orthopedic Bundle Programs

2018· article· en· W2807370102 on OpenAlexaff
Amol S. Navathe, Joshua M. Liao, Daniel Polsky, Yash Shah, Qian Huang, Jingsan Zhu, Zoe M. Lyon, Robin Wang, Josh Rolnick, Joseph Martinez, Ezekiel Emanuel

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsInstitute of Health Economics
FundersNational Institute on Aging
KeywordsPaymentJoint replacementRehabilitationBaseline (sea)MedicineBusinessFamily medicinePhysical therapyArthroplastyFinance

Abstract

fetched live from OpenAlex

We analyzed data from Medicare and the American Hospital Association Annual Survey to compare characteristics and baseline performance among hospitals in Medicare's voluntary (Bundled Payments for Care Improvement initiative, or BPCI) and mandatory (Comprehensive Care for Joint Replacement Model, or CJR) joint replacement bundled payment programs. BPCI hospitals had higher mean patient volume and were larger and more teaching intensive than were CJR hospitals, but the two groups had similar risk exposure and baseline episode quality and cost. BPCI hospitals also had higher cost attributable to institutional postacute care, largely driven by inpatient rehabilitation facility cost. These findings suggest that while both voluntary and mandatory approaches can play a role in engaging hospitals in bundled payment, mandatory programs can produce more robust, generalizable evidence. Either mandatory or additional targeted voluntary programs may be required to engage more hospitals in bundled payment programs.

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 score0.312
Threshold uncertainty score0.997

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 head0.083
GPT teacher head0.359
Teacher spread0.276 · 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

Citations37
Published2018
Admission routes1
Has abstractyes

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