Development and Initial Evaluation of a Software-Based Clinical Workload Measurement System for Pharmacists
Bibliographic record
Abstract
ABSTRACT Background: Implementation of a new pharmacy computer system allowed the creation of a workload measurement system that focused on pharmacists’ direct patient care activities. Objective: To describe a workload and outcomes measurement system that was developed within Meditech software and to document its use in a community hospital. Methods: A numeric system was developed for recording pharmacists’ workload when documenting interventions in patients’ medical records. Interventions were categorized according to the nature of the drug-related problem, the anticipated patient outcome, and acceptance of the intervention by the prescriber. Pharmacists’ clinical interventions were quantified over a 6-month period. Results: Fourteen pharmacists tabulated a total of 2645 interventions over the period January to June 2006. The mean number of interventions per pharmacist per clinical shift (± standard deviation) was 4.6 ± 3.4. A broad range of drug-related problems was identified. For every intervention, a mean of 1.4 clinical, 0.8 humanistic, and 0.1 economic outcomes were recorded. Only 3.2% of the pharmacists’ recommendations had been rejected by prescribers at the time of documentation. Conclusions: Numerous drug-related problems were identified by pharmacists, with various anticipated outcomes. Most of the interventions proposed by pharmacists were accepted by the prescribers. The workload measurement system allowed pharmacists to document their clinical activities and the anticipated outcomes of their interventions. RESUME Historique : La mise en place d’un nouveau systeme informatique de pharmacie a permis de creer un systeme de mesure de la charge de travail des pharmaciens, principalement axe sur leurs interventions de soins directs aux patients. Objectif : Decrire le systeme de mesure de la charge de travail et des resultats developpe a partir du progiciel Meditech et documenter son utilisation dans un hopital communautaire. Methodes : Un systeme numerique a ete elabore pour enregistrer la charge de travail des pharmaciens parallelement a la consignation des interventions dans les dossiers medicaux des patients. Les interventions ont ete categorisees selon la nature du probleme relie a la pharmacotherapie, le resultat therapeutique escompte, et l’acceptation de l’intervention par le prescripteur. Les interventions cliniques des pharmaciens ont ete quantifiees sur une periode de six mois. Resultats : En tout, 2645 interventions realisees par 14 pharmaciens ont ete recensees pour la periode allant de janvier a juin 2006. Le nombre moyen d’interventions par pharmacien et par quart de travail (± l’ecart-type) etait de 4,6 ± 3,4. Un large eventail de problemes relies a la pharmacotherapie ont ete deceles. Pour chaque intervention, on a recense 1,4 resultat sur le plan clinique, 0,8 sur le plan humain et 0,1 sur le plan economique. Seulement 3,2 % des recommandations des pharmaciens avaient ete rejetees par les prescripteurs au moment de la consignation. Conclusions : De nombreux problemes relies a la pharmacotherapie avec des resultats therapeutiques escomptes varies ont ete deceles par les pharmaciens. La plupart des interventions proposees par les pharmaciens ont ete acceptees par les prescripteurs. Ce systeme de mesure de la charge de travail a permis aux pharmaciens de consigner leurs activites cliniques et les resultats escomptes de leurs interventions.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".