The Impact of Phone Interruptions on the Quality of Simulated Medication Order Validation Using Eye Tracking
Bibliographic record
Abstract
INTRODUCTION: Order validation is an important component of pharmacy services, where pharmacists review orders with a focus on error prevention. Interruptions are frequent and may contribute to a reduction in error detection, thus potential medication errors. However, studying such errors in practice is difficult. Simulation has potential to study these events. METHODS: This was a pilot, simulation study. The primary objective was to determine the rate of medication error detection and the effect of interruptions on error detection during simulated validation. Secondary objectives included determining time to complete each prescription page. The scenario consisted of validating three handwritten medication order pages containing 12 orders and 17 errors, interrupted by three phone calls timed during one order for each page. Participants were categorized in groups: seniors and juniors (including residents). Simulation sessions were videotaped and eye tracking was used to assist in analysis. RESULTS: Eight senior and five junior pharmacists were included in the analysis. There was a significant association between interruption and error detection (odds ratio = 0.149, 95% confidence interval = 0.042-0.525, P = 0.005). This association did not vary significantly between groups (P = 0.832). Juniors took more time to validate the first page (10 minutes 56 seconds vs. 6 minutes 42 seconds) but detected more errors (95% vs. 69%). However, all major errors were detected by all participants. CONCLUSIONS: We observed an association between phone interruptions and a decrease in error detection during simulated validation. Simulation provides an opportunity to study order validation by pharmacists and may be a valuable teaching tool for pharmacists and pharmacy residents learning order validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".