Documentation of drug related problems and their management in community pharmacy: Data evolution over six years
Bibliographic record
Abstract
BACKGROUND: Documentation of pharmacists' activities, such as drug related problems (DRPs) management, is necessary to estimate fair remuneration but is rarely done in community pharmacies. OBJECTIVE: To document and evaluate the evolution of DRPs prevalence and management over six years. METHODS: Observational study carried out since 2016 in a community pharmacy. Documentation was made yearly for 21 days (depending on seasons, holidays and medical internship rotations) using the ClinPhADoc tool. Pharmacists documented: medication, DRP type, intervention, implied partner and time for DRP management. A subanalysis was made depending on the medical rotation. RESULTS: A total of 171 437 prescriptions were received and 6 844 (4.0%) documented with 1 550 DRPs. Most frequent DRPs were procedural (n = 506, 32.6%), dosage/posology (n = 263, 17.0%) and drug-drug interaction (n = 153, 9.9%). Mean time dedicated to DRP management was 6.9 min, the longest time was for clinical DRPs (11.0 min, SD = 6.6). Most DRPs (n = 726, 44.6%) were managed by the pharmacist alone taking less working time than when involving other stakeholders (p < 0.01). Statistically significant differences were found in DRPs between the beginning and end of medical rotation (p < 0.05). CONCLUSIONS: Documentation of DRP management allowed consistent results over the years. Patterns of DRPs can be used to develop inter-professional interventions to prevent DRPs.
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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 | 0.028 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".