Frequency of low-value care in Alberta, Canada: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine how frequently 10 low-value services highlighted by Choosing Wisely are done and what factors influence their provision. METHODS: This is a retrospective cohort study using routinely collected health data from five linked data sets from 2012 to 2015 in the Canadian province of Alberta to determine the frequency with which 10 low-value services were provided. RESULTS: Between 2012 and 2015, 162 143 people (4% of all 3 814 536 adult Albertans and 5% of the 3 423 135 who saw a physician at least once in that time frame) received at least one of the 10 low-value services, including 29.8% of Albertans older than 75 years (57 811 of 194 068). The proportion of adults receiving low-value services ranged from carotid artery imaging in 0.1% of asymptomatic adults without cerebrovascular disease, to prostate-specific antigen (PSA) testing in 55.5% of men 75 years or older without a history of prostate cancer. Although age, Charlson scores and frequency of primary care visits were associated with low-value service provision, the directions of the association differed across services; however, higher socioeconomic status, increased frequency of specialist contact and higher ratio of specialists to primary care physicians in the patient's region were associated with an increased risk of receiving all of the low-value services we examined. The low-value services which resulted in the greatest costs to the healthcare system were cervical cancer screening in women older than 65 without history of cervical dysplasia or genital cancer, PSA testing in men older than 75 without history of prostate cancer and preoperative stress testing/cardiac imaging before non-cardiac surgery. CONCLUSIONS: Even within a universal coverage healthcare system, the proportion of patients receiving low-value services varied widely (from <0.1% to 56%). Increased use was associated with higher socioeconomic status, increased frequency of specialist contact and higher ratio of specialists to primary care physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".