Sources of practice variations in cardiology - The influence of clinical context, cost, physicians’ perceptions and practice considerations
Bibliographic record
Abstract
Background: Practice variation is common and may represent variation in values and preferences in the setting of limited evidence regarding optimal care or indicate deficiencies in care. Methods: We administered a case-based survey to cardiologists in the United States and Canada. Participants selected their preferred management option and then rated the influence of 7 factors (safety, effectiveness, patient-centered care, efficiency, local hospital practice, medicolegal concerns and prior experience) on their decision using a scale of 1 (unimportant) to 7 (critically important). Follow-up questions explored knowledge and attitudes on healthcare costs. The relationship between management choice and perceived influence of each factor was examined using repeated measures ANOVA. Free text comments were analyzed using basic content analysis.Results: One hundred and six cardiologists completed the survey. Respondents rated safety (5.8), effectiveness (5.7) and patient-centered care (5.7) as important determinants irrespective of their management choice. Cardiologists frequently (range 19%-87%) chose options not recommended by clinical practice guidelines (CPG), with individual cardiologists sometimes choosing guideline-suggested options and sometimes not. Differences in ratings of factors between those who chose guideline-suggested options and those who did not varied based on the case. Respondents considered cost to be important in decision-making; however, they did not feel well informed and, consequently, seldom discussed this with patients.Conclusion: Cardiologists rate evidence-based practice as an important factor influencing their decision-making whether or not they make CPG-concordant choices. Sources of practice variation include case-context, local hospital practice and medicolegal concerns. Implementation strategies to improve high value patient-centered care should consider physicians’ perceptions of effectiveness of the management options. Successful strategies to improve patient-centered care will require engagement from physicians, particularly to understand how best to support their ability to counsel and involve patients when choosing treatment options and considering cost in these decisions. A deeper understanding of practice variation and its implications will require use of qualitative methods.
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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 | 0.018 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".