Performance in the Medicare Shared Savings Program After Accounting for Nonrandom Exit
Bibliographic record
Abstract
Background: Accountable care organizations (ACOs) in the Medicare Shared Savings Program (MSSP) are associated with modest savings. However, prior research may overstate this effect if high-cost clinicians exit ACOs. Objective: To evaluate the effect of the MSSP on spending and quality while accounting for clinicians' nonrandom exit. Design: Similar to prior MSSP analyses, this study compared MSSP ACO participants versus control beneficiaries using adjusted longitudinal models that accounted for secular trends, market factors, and beneficiary characteristics. To further account for selection effects, the share of nearby clinicians in the MSSP was used as an instrumental variable. Hip fracture served as a falsification outcome. The authors also tested for compositional changes among MSSP participants. Setting: Fee-for-service Medicare, 2008 through 2014. Patients: A 20% sample (97 204 192 beneficiary-quarters). Measurements: Total spending, 4 quality indicators, and hospitalization for hip fracture. Results: In adjusted longitudinal models, the MSSP was associated with spending reductions (change, -$118 [95% CI, -$151 to -$85] per beneficiary-quarter) and improvements in all 4 quality indicators. In instrumental variable models, the MSSP was not associated with spending (change, $5 [CI, -$51 to $62] per beneficiary-quarter) or quality. In falsification tests, the MSSP was associated with hip fracture in the adjusted model (-0.24 hospitalizations for hip fracture [CI, -0.32 to -0.16 hospitalizations] per 1000 beneficiary-quarters) but not in the instrumental variable model (0.05 hospitalizations [CI, -0.10 to 0.20 hospitalizations] per 1000 beneficiary-quarters). Compositional changes were driven by high-cost clinicians exiting ACOs: High-cost clinicians (99th percentile) had a 30.4% chance of exiting the MSSP, compared with a 13.8% chance among median-cost clinicians (50th percentile). Limitation: The study used an observational design and administrative data. Conclusion: After adjustment for clinicians' nonrandom exit, the MSSP was not associated with improvements in spending or quality. Selection effects-including exit of high-cost clinicians-may drive estimates of savings in the MSSP. Primary Funding Source: Horowitz Foundation for Social Policy, Agency for Healthcare Research and Quality, and National Institute on Aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".