The Effects of Multispecialty Group Practice on Health Care Spending and Use
Bibliographic record
Abstract
U.S. physicians are increasingly joining multispecialty group practices.In this paper, we analyze how a primary care physician's practice type -single (SSP) versus multispecialty practice (MSP) -affects health care spending and use.Focusing on Medicare beneficiaries who change their primary care physician due to a geographic move, we compare changes in practice patterns before and after the move between patients who switch practice types and those who do not.We use instrumental variables to address potential selection by patients into practice types after the move.We find that changing from a single to a multi-specialty primary care group practice decreases annual Medicare-financed per capita expenditures by about $1,600 -a 28% reduction.The effect is driven primarily by changes in hospital expenditures and is concentrated among patients with two or more chronic conditions, suggesting that MSP improves care delivery by reducing hospitalizations among relatively sick patients.The results imply that, while research has shown the potential for physician consolidation to increase prices in some settings, large multispecialty groups also have the potential to lower costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".