Primary Care Physician Panel Size and Quality of Care: A Population-Based Study in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the relationship between the number of patients under a primary care physician's care (panel size) and primary care quality indicators. METHODS: We conducted a cross-sectional, population-based study of fee-for-service and capitated interprofessional and non-interprofessional primary health care practices in Ontario, Canada between April 2008 and March 2010, encompassing 4,195 physicians with panel sizes ≥1,200 serving 8.3 million patients. Data was extracted from multiple linked, health-related administrative databases and covered 16 quality indicators spanning 5 dimensions of care: access, continuity, comprehensiveness, and evidence-based indicators of cancer screening and chronic disease management. RESULTS: The likelihood of being up-to-date on cervical, colorectal, and breast cancer screening showed relative decreases of 7.9% (P <.001), 5.9% (P = .01), and 4.6% (P <.001), respectively, with increasing panel size (from 1,200 to 3,900). Eight chronic care indicators (4 medication-based and 4 screening-based) showed no significant association with panel size. The likelihood of individuals with a new diagnosis of congestive heart failure having an echocardiogram, however, increased by a relative 8.1% (P <.001) with higher panel size. Increasing panel size was also associated with a 10.8% relative increase in hospitalization rates for ambulatory-care-sensitive conditions (P = .04) and a 10.8% decrease in non-urgent emergency department visits (P = .004). Continuity was highest with medium panel sizes (P <.001), and comprehensiveness had a small decrease (P = .03) with increasing panel size. CONCLUSIONS: Increasing panel size was associated with small decreases in cancer screening, continuity, and comprehensiveness, but showed no consistent relationships with chronic disease management or access indicators. We found no panel size threshold above which quality of care suffered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".