Prescribing and testing by primary care providers to assess adherence to the Choosing Wisely Canada recommendations: a retrospective cohort study
Bibliographic record
Abstract
Background: Choosing Wisely Canada is an initiative to reduce overprescribing and overtesting. This study assessed adherence to 4 Choosing Wisely Canada recommendations for interventions commonly performed in primary care: (a) antibiotic prescriptions for infections that are probably viral in origin, (b) routine vitamin D tests in low-risk adults, (c) annual screening blood tests and (d) prescriptions of antipsychotic medication to treat symptoms of dementia. Methods: We conducted a retrospective cohort study of data from the electronic medical records of patients who had an encounter between 2014 and 2016 with a participating Manitoba Primary Care Research Network primary care provider in Manitoba, Canada. Patient encounter data were reviewed for prescribing and testing practices. Descriptive statistics and multivariable models assessed associations between patient and provider characteristics and rates of prescribing and testing. Results: Data for 164 195 patients from 230 providers were included in the study. Sixteen percent (n = 25 629) of patients had an encounter that involved potentially unnecessary diagnostic testing and treatment. A minority of providers contributed to above-average rates of prescribing and testing: 29% (n = 69) of providers prescribed antibiotics for a viral indication,11% (n = 24) prescribed an antipsychotic to a patient diagnosed with dementia, 9% (n = 24) ordered prostate-specific antigen tests and 14% (n = 34) ordered vitamin D tests at above-average rates, respectively. Patient and provider characteristics were associated with each of the prescribing and testing practices assessed. Interpretation: This study demonstrated that fewer than 30% of primary care providers contributed to interventions in direct contradiction to Choosing Wisely Canada recommendations. Improvement strategies specific to each prescription or testing recommendation should target specific providers to prevent patient harm and reduce unnecessary health care spending.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".