Assessment of the impact of pharmacy learners on admission medication reconciliation in Toronto, Canada
Bibliographic record
Abstract
Abstract Introduction Currently there is a lack of published data examining the clinical impact of pharmacy learners on patient care outcomes in acute care. A collaborative of hospital pharmacists in Canada established consensus on eight clinical pharmacy key performance indicators (cpKPIs) representing essential patient processes of care. Of the eight cpKPIs, admission medication reconciliation has been established as a cornerstone patient care process. The implementation of cpKPI measurement creates an opportunity to quantify pharmacy learner contribution to patient care. Aim To determine if the presence of pharmacy learners partnering with pharmacists is associated with an increased number of patients receiving admission medication reconciliation (AMR). Methods In this prospective observational study, pharmacists and learners (on 5‐week rotations) tracked patients receiving AMR in the electronic health record from 25 January to 17 July 2016. The number of patients receiving AMR were compared during timeframes when a learner was present (intervention) to when a learner was not present (control). Results In the main analysis of 30 learner‐pharmacist pairs with 4684 patients, 1136 patients received AMR in the intervention group versus 887 patients in the control group (adjusted for 5 weeks). The number of patients receiving AMR in the presence of a pharmacy learner partnered with a pharmacist (median = 43, IQR = 23–59) was significantly increased compared to the presence of a pharmacist alone (median = 36, IQR = 17–53, p < 0.001). Learners partnered with pharmacists to perform AMR for 41% of the patients. Conclusion Overall, pharmacy learners partnering with pharmacists increased the number of patients receiving AMR.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".