Evaluation of a Nationwide e-Prescribing System
Bibliographic record
Abstract
Electronic prescribing, defined as the electronic generation and transmission of a medication order for community-dwelling patients, is presented as an essential technology to improve medication use. The objective of this study was to evaluate a nationwide e-prescribing system in Quebec, Canada. A mixed-method study was conducted from July 2017 until June 2018. A descriptive analysis of e-prescription usage was performed using aggregated usage data, combined with an exploratory descriptive analysis of the e-prescribing system from the perspective of users of two electronic health records (EHR) and pharmacy management systems (PMS) (n=9 prescribers; 8 pharmacy technicians and 11 pharmacists). Overall, the adoption of the system was low, with only 2% of prescriptions being electronically transmitted and retrieved during the study period. Alignment problems were identified on the prescriber's and receiver's side, generating safety issues, and hindering the potential for benefits realization.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".