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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".