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Record W2775671402 · doi:10.1377/hlthaff.2017.0814

Medicare ACO Program Savings Not Tied To Preventable Hospitalizations Or Concentrated Among High-Risk Patients

2017· article· en· W2775671402 on OpenAlexaff
J. Michael McWilliams, Michael E. Chernew, Bruce E. Landon

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHealth Care Foundation
FundersNational Institute on Aging
KeywordsAmbulatory careAmbulatoryMedicineHealth careOutpatient visitsPreventive careEmergency medicineEnvironmental healthEconomicsInternal medicine

Abstract

fetched live from OpenAlex

It has been widely assumed that better management and coordination of care for chronic conditions and high-risk patients would be the leading mechanisms for achieving savings in accountable care organizations (ACOs), specifically by reducing acute care needs through enhanced outpatient and preventive care. We examined the extent to which changes in spending and hospitalizations for ACO patients in the Medicare Shared Savings Program (MSSP) have been consistent with this expectation. By 2014, participation in the MSSP was associated with significant reductions in total Medicare fee-for-service spending for ACO patients but with proportionately smaller reductions in hospitalizations and some increases in hospitalizations for ambulatory care-sensitive conditions. In addition, spending reductions were not clearly concentrated among high-risk patients: Reductions for those patients accounted for only 38 percent of the total reduction among ACOs entering the MSSP in 2012, and reductions among 2013 MSSP entrants were almost entirely concentrated among lower-risk patients. These findings suggest that, on average, care coordination and management efforts focused on ambulatory care-sensitive conditions and high-risk patients have not been the major drivers of early savings in the MSSP.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.305
Teacher spread0.270 · 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

Citations89
Published2017
Admission routes1
Has abstractyes

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