Physician Variation and the Impact of Payment Model in Cardiac Imaging
Bibliographic record
Abstract
BACKGROUND: The influence of fee-for-service reimbursement on cardiac imaging has not been compared with other payment models. Furthermore, variation in ordering practices is not well understood. METHODS AND RESULTS: This retrospective, population-based cohort study using linked administrative data from Alberta, Canada included adults with chronic heart disease (atrial fibrillation, coronary artery disease, and heart failure) seen by cardiac specialists for a new outpatient consultation April 2012 to December 2018. Generalized linear mixed-effects models estimated the association of payment model (including the ability to bill to interpret imaging tests) and the use of cardiac imaging and quantified variation in cardiac imaging. Among 31 685 adults seen by 308 physicians at 136 sites, patients received an observed mean of 0.67 (95% CI, 0.67-0.68) imaging tests per consultation. After adjustment, patients seeing fee-for-service physicians had 2.07 (95% CI, 1.68-2.54) and fee-for-service physicians with ability to interpret had 2.87 (95% CI, 2.16-3.81) times the rate of receiving a test than those seeing salaried physicians. Measured patient, physician, and site effects accounted for 31% of imaging variation and, following adjustment, reduced unexplained site-level variation 40% and physician-level variation 29%. CONCLUSIONS: We identified substantial variation in the use of outpatient cardiac imaging related to physician and site factors. Physician payment models have a significant association with imaging use. Our results raise concern that payment models may influence cardiac imaging practice. Similar methods could be applied to identify the source and magnitude of variation in other health care processes and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".