Usability Inspection to Improve an Electronic Provincial Medication Repository
Bibliographic record
Abstract
BACKGROUND: Medication errors are a significant source of actual and potential harm for patients. Community medication records have the potential to reduce medication errors, but they can also introduce unintended consequences when there is low fit to task (low cognitive fit). PharmaNet is a provincially managed electronic repository that contains the records for community-based pharmacy-dispensed medications in British Columbia. This research explores the usability of PharmaNet, as a representative community-based medication repository. METHODS: We completed usability inspections of PharmaNet through vendor applications. Vendor participants were asked to complete activity-driven scenarios, which highlighted aspects of medication management workflow. Screen recording was later reviewed. Heuristics were applied to explore usability issues and improvement opportunities. RESULTS AND DISCUSSION: Usability inspection was conducted with four PharmaNet applications. Ninety-six usability issues were identified; half of these had potential implications for patient safety. These were primarily related to login and logout procedures; display of patient name; display of medications; update and display of alert information; and the changing or discontinuation of medications. RECOMMENDATIONS: PharmaNet was designed primarily to support medication dispensing and billing activities by community pharmacies, but is also used to support care providers with monitoring and prescribing activities. As such, some of the features do not have a strong fit for other clinical activities. To improve fit, we recommend: having a Current Medications List and Displaying Medication Utilization Charts.
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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 | 0.045 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".