Comparison of primary care physician payment models in the management of hypertension.
Bibliographic record
Abstract
OBJECTIVE: To determine primary care physician screening, treatment, and control rates for hypertension and to examine whether type of physician payment model affected these rates. DESIGN: A cross-sectional chart abstraction study. SETTING: Community health centres (salary), primary care networks (capitation), or traditional fee-for-service practices in Ontario. PARTICIPANTS: A total of 135 primary care physicians, 45 from each of the 3 different models of care. Data were abstracted from 28 adult patient charts randomly selected from each physician. MAIN OUTCOME MEASURES: Screening rates were based on the presence of at least 1 blood pressure reading in the past 3 years, treatment rates on the number of patients with hypertension treated with antihypertensive medication, and control rates on the number of patients with hypertension whose most recent blood pressure readings were below 140/90 mm Hg, below 130/80 mm Hg for patients with diabetes, or below 120/75 mm Hg for patients with renal disease. RESULTS: Overall, 92.5% of all patients were screened for hypertension, 86.4% of patients with hypertension were treated with antihypertensive medications, and 44.9% of patients with hypertension had their blood pressure controlled. Mean screening rates were 90.6%, 93.5%, and 93.3% (P = .22), and after adjusting for sociodemographic factors and comorbid conditions, mean treatment rates were 90.9%, 81.0%, and 87.4% (P < .05) and mean control rates were 54.5%, 38.6%, and 41.6% (P < .05) for capitation, salary, and fee-for-service physicians, respectively. CONCLUSION: Our results showed that although screening rates were similar between all 3 models, there were differences in treatment and control rates, with capitation physicians having the best treatment and control rates. Further investigation into whether this type of payment model results in improved chronic disease management for other chronic diseases and preventative care maneuvers will give support to health care policy makers who are moving toward capitation-type payment models for primary care delivery.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".