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Record W2893161466 · doi:10.1377/hlthaff.2018.0491

Changes In End-Of-Life Care In The Medicare Shared Savings Program

2018· article· en· W2893161466 on OpenAlexaff
Lauren Gilstrap, Haiden A. Huskamp, David G. Stevenson, Michael E. Chernew, David C. Grabowski, J. Michael McWilliams

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Care Foundation
FundersNational Institute on Aging
KeywordsEnd-of-life careIncentivePaymentMedicineActuarial sciencePopulationBusinessService (business)Managed careHealth careNursingFinanceEconomicsMarketingEnvironmental healthPalliative careEconomic growth

Abstract

fetched live from OpenAlex

End-of-life care is often overly aggressive and inconsistent with patients' preferences. Although end-of-life care could therefore be a natural target for accountable care organizations (ACOs) in their efforts to reduce spending, identifying and curbing wasteful care for patients at high risk of death may be challenging. To date, the impact of ACOs on end-of-life care has not been quantified. Using fee-for-service Medicare claims through 2015 and a difference-in-differences approach, we found evidence of some changes in end-of-life care associated with providers' participation in the Medicare Shared Savings Program among both decedents and patients at high risk of death. Although generally suggestive of less aggressive care, most effects were small and inconsistent across cohorts of ACOs entering the program in different years. This suggests that ACOs have not yet substantially altered end-of-life care patterns and that additional incentives, time, or both may be needed. Alternatively, curbing wasteful end-of-life care might not be a viable source of substantial savings under population-based payment models.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.104
GPT teacher head0.438
Teacher spread0.333 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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