Safety Implications of Standardized Continuous Quality Improvement Programs in Community Pharmacy
Bibliographic record
Abstract
Standardized continuous quality improvement (CQI) programs combine Web-based technologies and standardized improvement processes, tools, and expectations to enable quality-related events (QREs) occurring in individual pharmacies to be shared with pharmacies in other jurisdictions. Because standardized CQI programs are still new to community pharmacy, little is known about how they impact medication safety. This research identifies key aspects of medication safety that change as a result of implementing a standardized CQI program. Fifty-three community pharmacies in Nova Scotia, Canada, adopted the SafetyNET-Rx standardized CQI program in April 2010. The Institute for Safe Medication Practices (ISMP) Canada's Medication Safety Self-Assessment (MSSA) survey was administered to these pharmacies before and 1 year into their use of the SafetyNET-Rx program. The nonparametric Wilcoxon signed-rank test was used to explore where changes in patient safety occurred as a result of SafetyNETRx use. Significant improvements occurred with quality processes and risk management, staff competence, and education, and communication of drug orders and other information. Patient education, environmental factors, and the use of devices did not show statistically significant changes. As CQI programs are designed to share learning from QREs, it is reassuring to see that the largest improvements are related to quality processes, risk management, staff competence, and education.
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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 | 0.037 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".