Patients of Comprehensive Primary Care Physicians Receive Better Care and Have Better Outcomes: Findings from Ontario, Canada
Bibliographic record
Abstract
Context: Comprehensiveness is a key attribute of primary care. In recent years many metrics have been developed to identify comprehensive primary care practice, but with limited understanding of their relationship with health care and outcomes. In this study we use a published measure of primary care comprehensiveness to group physicians and then examine the patterns of care, health service use and health outcomes among their patients. Objective: To compare the patterns of care, health service use and health outcomes of patients of physicians who are comprehensive, in various types of focused practice, and other practice types. Study Design and Analysis: Population-based cross-sectional study. Setting or Dataset: The province of Ontario, Canada. Data sources included population-based primary are physician claims, provider databases, patient registration with a primary care physician and provincial health care utilization databases. Population studied: All primary care physicians in active practice in Ontario in 2019/20 and all individuals in Ontario who could be assigned to a primary care physician. Outcome Measures: Physicians were grouped by practice type and then their patients were identified using the provincial primary care registration database and primary care physician claims. Patient characteristics such as demographics and prevalence of chronic conditions were estimated. Patient outcomes included diabetes care, cancer screening rates and utilization of health care services for ambulatory care sensitive conditions. Results: Of 15,745 physicians, 15% had no assigned patients and were excluded. Of those included, 73% were comprehensive, 13% in focused practice, 9% very part-time and 5% other. Comprehensive physicians had the largest patient panels (mean 1,258 versus 249 for focused practice), higher rates of cancer screening (63.1% versus 46.5%) and diabetes care (69.0% with retinal exams versus 60.8%) and lower rates of hospitalizations for ambulatory care sensitive conditions (2.7 versus 15.0 per 1000) and emergency department (ED) visits (415.3 versus 856.2 per 1000). Conclusions: Primary care physician comprehensiveness is associated with improved preventive health care and chronic disease management and lower ED and hospital utilization. Policies and strategies to enhance comprehensive practice, attach patients to comprehensive physicians, and improve care for those being managed largely in non-comprehensive practices are needed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".