The effect of pharmacist-led medication review in high-risk patients in the emergency department: an evaluation protocol
Bibliographic record
Abstract
BACKGROUND: Adverse drug events are unintended and harmful events related to medication use. They are a leading cause of visits to the emergency department, unplanned admissions to hospital and death. Adverse drug events can be misdiagnosed in the emergency department, resulting in treatment delays. Our objective was to describe a process to evaluate the effect of pharmacist-led medication review in high-risk patients in the emergency department on the number of days these patients subsequently spent in hospital within 30 days of their index visit. METHODS: We describe the evaluation of a prospective multicentre quality improvement program. During the evaluation period, triage nurses will flag incoming patients to the emergency department at high risk for adverse drug events by applying a clinical decision rule consisting of 4 variables (comorbid conditions, antibiotic use within 7 days, medication changes within 28 days and age). Consecutive eligible patients will be enrolled in the study and systematically allocated to either a pharmacist-led medication review group or a control group. In the intervention group, pharmacists will collect best-possible medication histories, review the patient's medications for appropriateness and adverse drug events, and communicate the results of their medication review to patients, caregivers and physicians. In the control group, nurses will start medication reconciliation by collecting best-possible medication histories, and physicians will refer patients to onsite pharmacists for specific medication management questions as needed. Health outcomes will be assessed using anonymized data linkage to administrative health databases. The primary outcome will be the percent days spent in hospital over a 30-day period. INTERPRETATION: This protocol describes the methods for evaluating the effect of pharmacist-led medication review in high-risk patients in the emergency department on use of health services, and highlights the methodological challenges that will be encountered. We plan to disseminate the results of this evaluation through articles published in peer-reviewed journals, presentations at scientific meetings and briefing notes to institutional, provincial and national stakeholders.
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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 | 0.051 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".