Quantifying the Impact of Prescription-Related Problems on Pharmacy Workload
Bibliographic record
Abstract
ABSTRACT Objectives: To determine pharmacy workload related to resolving prescription-related problems, including ambiguous, illegible, and incomplete orders, and to determine the types of problems and the frequency of their occurrence. Methods: An independent observer prospectively documented the time required to resolve prescription-related problems in the pharmacy of a major teaching hospital. The type and frequency of the problems were recorded according to predefined criteria. Results: Pharmacists spent a mean total of 20.1 min to resolve each prescription-related problem from the time it was first identified to the time it was resolved and the prescription processed. The mean time per problem for clinical pharmacists was 3 times the mean time per problem for dispensing pharmacists; however, clinical pharmacists were involved in resolving fewer problematic orders (31% of orders) than dispensing pharmacists (92% of orders). About 13 prescriptionrelated problems were encountered in the pharmacy during each 8-h day shift, so about 2 h of dispensing pharmacist time and about 2 h of clinical pharmacist time were required during each shift. The rate of prescription-related problems was 55.2 per 1000 orders, including 6.5 illegible orders and 5.9 incomplete orders per 1000 orders. Conclusions: Pharmacists spent a substantial amount of time resolving prescription-related problems, many of which could be avoided if medication orders were complete and legible. The results of this study suggest that improvements could be made to the medication-ordering process. RESUME Objectifs : Definir la charge de travail de la pharmacie consacree a resoudre les problemes lies aux ordonnances, y compris les prescriptions ambigues, illisibles et incompletes, et determiner le type et la frequence de ces problemes. Methodes : Documentation prospective par un observateur independant du temps necessaire pour resoudre les problemes lies aux ordonnances dans une pharmacie d’un important hopital d’enseignement. Le type et la frequence des problemes ont ete notes selon des criteres predefinis. Resultats : Les pharmaciens ont passe une moyenne totale de 20,1 minutes a resoudre chaque probleme lie a une ordonnance, a compter du moment de son identification jusqu’au moment de sa resolution et de l’execution de l’ordonnance. Les pharmaciens cliniciens passaient en moyenne trois fois plus de temps par probleme que les pharmaciens d’officine. En revanche, ils ont resolu moins d’ordonnances problematiques (31 %) que les pharmaciens d’officine (92 %). On a compte environ 13 problemes lies a des ordonnances au cours de chaque quart de huit heures a la pharmacie, ce qui equivaut a environ deux heures consacrees par le pharmacien d’officine et deux heures par le pharmacien clinicien par quart de travail. Le taux de problemes lies aux ordonnances etait de 55,2 pour 1 000 ordonnances, dont 6,5 etaient des ordonnances illisibles et 5,9 des ordonnances incompletes. Conclusions : Les pharmaciens ont passe un nombre important d’heures a resoudre les problemes lies aux ordonnances, dont de nombreux auraient pu etre evites si les prescriptions avaient ete lisibles et completes. Les resultats de cette etude laissent croire que des ameliorations peuvent etre apportes au processus de demande de medicaments.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".