Access Intervention in an Integrated, Prepaid Group Practice: Effects on Primary Care Physician Productivity
Bibliographic record
Abstract
OBJECTIVE: To estimate the joint effect of a multifaceted access intervention on primary care physician (PCP) productivity in a large, integrated prepaid group practice. DATA SOURCES: Administrative records of physician characteristics, compensation and full-time equivalent (FTE) data, linked to enrollee utilization and cost information. STUDY DESIGN: Dependent measures per quarter per FTE were office visits, work relative value units (WRVUs), WRVUs per visit, panel size, and total cost per member per quarter (PMPQ), for PCPs employed >0.25 FTE. General estimating equation regression models were included provider and enrollee characteristics. PRINCIPAL FINDINGS: Panel size and RVUs per visit rose, while visits per FTE and PMPQ cost declined significantly between baseline and full implementation. Panel size rose and visits per FTE declined from baseline through rollout and full implementation. RVUs per visit and RVUs per FTE first declined, and then increased, for a significant net increase of RVUs per visit and an insignificant rise in RVUs per FTE between baseline and full implementation. PMPQ cost rose between baseline and rollout and then declined, for a significant overall decline between baseline and full implementation. CONCLUSIONS: This organization-wide access intervention was associated with improvements in several dimensions in PCP productivity and gains in clinical efficiency.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".