Use of hospitalists by Medicare beneficiaries: a national picture.
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics of hospitalists serving Medicare beneficiaries. DATA SOURCES: Medicare claims from 2009 and 2011 merged with the Provider Enrollment, Chain, and Ownership System file for physician characteristics. STUDY DESIGN: Our construction of the Medicare Data on Physician Practice and Specialty (MD-PPAS) enabled identification of hospitalists based on the attending physician for Medicare admissions (medical and surgical) in 2009 and 2011. PRINCIPAL FINDINGS: In 2011, hospitalists constituted 13.3% of physicians who designated their specialty as primary care and 4.4% of all physicians serving Medicare beneficiaries. Compared to other physicians, hospitalists were more likely to be female, under forty, and in large practices. More than a quarter of Medicare admissions had a hospitalist as the attending physician, though the rate was substantially higher for medical than surgical admissions (31.8% versus 11.3%). Between 2009 and 2011, the percentage of medical admissions with a hospitalist as the attending physician increased by roughly a quarter (from 25.7% to 31.8%). CONCLUSIONS: This analysis provides a more current and complete estimate of the use of hospitalists by the Medicare population than is available from prior studies. The ability to identify hospitalists from claims data will facilitate research on the impact of hospitalist use on quality and cost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".