EFFECTS OF PATIENT CENTERED MEDICAL HOMES ON MEDICARE BENEFICIARIES WITH MULTIPLE CHRONIC CONDITIONS
Bibliographic record
Abstract
The Centers for Medicare & Medicaid Services (CMS) Multi-payer Advanced Primary Care Practice (MAPCP) Demonstration tested the effect of patient-centered medical homes (PCMH) on Medicare and Medicaid beneficiaries in eight states. Using multivariable linear and logistic regression models, the evaluation assessed how people with multiple chronic conditions performed on 7 utilization measures (e.g., all-cause hospitalizations), 7 process quality measures (e.g., receipt of total lipid panel); and 4 health outcome measures (e.g., avoidable catastrophic events) relative to a comparison group of beneficiaries in non-PCMH practices. Medicare fee-for-service beneficiaries in demonstration states with 3+ chronic conditions present in 2+ consecutive years of Medicare claims who were in the high-risk category of the CMS Hierarchical Condition Category index were defined as beneficiaries with multiple chronic conditions. Roughly a quarter of beneficiaries met inclusion criteria. Over the first two years, the MAPCP Demonstration was not associated with many statistically significant outcomes relative to the comparison group. Several states’ significant outcomes were mostly in an unexpected direction (i.e., favored the comparison group). No consistent patterns emerged in terms of which outcomes most improved across the demonstration states or which states had the best outcomes. Of the 18 selected measures, no state had significant results in more than 3 outcomes. Overall, the Demonstration does not appear to have had a significant positive effect on Medicare beneficiaries with multiple chronic conditions in the first two years of the demonstration. Final analyses spanning all three years of the demonstration will be available at the time of the conference.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".