Physician order entry: a mixed blessing to pharmacy?
Bibliographic record
Abstract
Objective. The Alberta Cancer Board (ACB) Pharmacy conducted a timing study to determine how electronic prescription ordering impacts the workload in pharmacy in comparison to the current paper system. The objective was to compare the mean time required to review orders generated by an electronic physician order entry system to the existing paper method, and to determine whether such an implementation would decrease pharmacist intervention rates. Methods. Self-reporting and measurement by stopwatch timing were used to record the timing data on the prescription order review process on all outpatient parenteral chemotherapy orders for adults handled within the ACB’s two main tertiary centres for the month of June. The primary endpoint measured was mean pharmacist order review time for manual and electronic orders. The secondary endpoint measured was pharmacist intervention rate for manual and electronic orders. Results. Among all 836 chemotherapy orders reviewed, the mean pharmacist order review time was increased by 5.15 min with the implementation of an electronic order entry system. A total of 62 pharmacist interventions were recorded in the study. The pharmacist intervention rate was 7.14% for the electronic orders and 7.47% for the manual paper orders. Conclusions. The study showed that the implementation of an electronic physician order entry system has significant impacts on pharmacy workload without providing significant reductions in pharmacist intervention rates.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".