Impact of a clinical decision support system on identifying drug‐related problems and making recommendations to providers during community pharmacist‐led medication reviews in Ontario, Canada: A pilot study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a clinical decision support system (CDSS) to identify drug-related problems (DRPs) during community pharmacist medication reviews. DESIGN: Pilot 3-phase (group), open-label study. SETTING AND PARTICIPANTS: Two community pharmacies in Sarnia, Ontario, with pharmacists providing medication reviews to patients. STUDY PROCEDURES: Five pharmacists participated in three phases (groups). During Phase 1, pharmacists conducted medication reviews in 25 adult patients using the usual approaches. In Phase 2, pharmacists were trained to use a CDSS to identify DRPs, and then conducted medication reviews using the tool in a different group of 25 adult patients. In Phase 3, pharmacists conducted medication reviews without the aid of the CDSS in 25 additional adult patients. MAIN OUTCOME MEASURES: The primary outcome was recommendation to the primary care physician to alter pharmacotherapy based on medication review, assessed using mean number and frequency (yes/no) of recommendations by patient. Secondary outcomes included number of potential DRPs, actual DRPs, medication review duration time, pharmacist's perceptions of the CDSS and patient satisfaction with medication review. RESULTS: The mean number of recommendations to primary care physicians to alter pharmacotherapy per patient in Phases 1, 2 and 3 did not differ: 1.0 (SD = I.2) versus 1.5 (1.0) versus 1.5 (1.0), respectively; p = 0.223. The percentage of patients with a pharmacy recommendation sent to physicians across the phases, however, differed: 52% versus 80% versus 88%, respectively; p = 0.010, with more in Phases 2 and 3 compared to 1. There were more potential DRPs in group 2 compared to other groups. There were no differences in actual DRPs and medication review time. Pharmacists had positive attitudes about the CDSS. Patients were generally satisfied with their medication review. CONCLUSIONS: This small pilot study provides some preliminary evidence for performance and feasibility of a CDSS to identify DRPs that pharmacists will act on. Future research is recommended to validate these findings in a larger sample.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".