Saskatchewan Physicians’ Opinions of Their Personalized Prescribing Profiles Related to Opioids, Benzodiazepines, Stimulants, and Gabapentin
Bibliographic record
Abstract
Overdoses of prescription medications continue to be a significant concern for health systems around the world. Medical regulators in several jurisdictions have started generating personalized prescribing profiles for individual physicians as an audit and feedback tool to reduce the sub-optimal prescribing of high-risk drugs such as opioids, benzodiazepines and stimulants. However, little is known about how to most effectively communicate the data in these prescriber profiles to the intended recipients. The aim of this study was to collect the opinions of physicians in Saskatchewan, Canada, regarding their personalized prescriber profiles. One-on-one semi-structured interviews were completed in January 2019 with 17 physicians who were given access to personalized profiles containing their prescribing information on opioids, benzodiazepines, stimulants and gabapentin. Interviews were recorded and data was analyzed using thematic analysis. Respondents thought the profiles were a useful tool that had significant potential to improve their prescribing practices. However, many physicians also thought the profiles were confusing and difficult to interpret. Several recommendations were made to improve the prescriber profiles, which may be applicable to other jurisdictions currently using, or planning to develop, similar quality improvement tools. These recommendations include: limiting the use of abbreviations and acronyms; being explicit regarding the intent of the profiles; ensuring comparator data is relevant to the individual recipient; using a combination of numbers and visuals to display data; and providing detailed context regarding what the data means.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".