The association between payment model and specialist physicians’ selection of patients with diabetes: a descriptive study
Bibliographic record
Abstract
Background: As the number of people with chronic diseases increases, understanding the impact of payment model on the types of patients seen by specialists has implications for improving the quality and value of care. We sought to determine if there is an association between specialist physician payment model and the types of patients seen. Methods: In this descriptive study, we used administrative data to compare demographic characteristics, illness severity and visit indication of patients with diabetes seen by fee-for-service and salary-based internal medicine and diabetes specialists in Calgary and Edmonton between April 2011 and September 2014. The study cohort included all newly referred adults with diabetes (no appointment with a specialist in prior 4 yr). Diabetes was identified using a validated algorithm that excludes gestational diabetes. Results: Patients managed by salary-based physicians (n = 2736) were sicker than those managed by fee-for-service physicians (n = 21 218). Patients managed by salary-based specialists were more likely to have 5 or more comorbidities (23.0% [n = 628] v. 18.1% [n = 3843]) and to have been admitted to hospital or seen in an emergency department for an ambulatory care sensitive condition in the year before their index visit, probably reflecting poorer disease control or barriers to optimal outpatient care. A higher proportion of visits to salary-based physicians were for appropriate indications (65.2% [n = 744] v. 55.6% [n = 5553]; risk ratio 1.17, 95% confidence interval 1.09–1.27). Interpretation: Salary-based specialists were more likely to see patients with a clear indication for a specialist visit, while fee-for-service specialists were more likely to see healthier patients. Future research is needed to determine if the differences in types of patients are attributable to payment model or other provider- or system-level factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".