Medication Reconciliation by a Pharmacist in the Emergency Department: A Pilot Project
Bibliographic record
Abstract
INTRODUCTION The Institute for Healthcare Improvement has defined medication reconciliation as “a formal process of obtaining a complete and accurate list of each patient’s current home medications—including name, dosage, frequency and route— and comparing the physician’s admission, transfer, and/or discharge orders to that list. Discrepancies are brought to the attention of the prescriber and, if appropriate, changes are made to the orders.”1 The impetus behind this concept is to prevent adverse drug events. The Canadian Adverse Events Study reported an adverse event rate of 7.5% in Canadian hospitals, and 36% of the adverse events were deemed preventable.2 Drug- and fluidrelated events together constituted the second most common type of adverse event.2 Medication discrepancies can occur at any point in the medication use process, but the largest percentage of these discrepancies occur during the prescribing phase.3-7 The presence of a pharmacist during patient care rounds and when prescriptions are written has been associated with a reduction in medication discrepancies at the ordering stage.8-10 In a sample of 98 emergency department visits, the accuracy of medication lists completed by the triage nurse was 42.6%.11 Most physicians rely on patients’ drug lists when making a diagnosis and ordering tests and medications in the emergency department. However, without appropriate verification of the patient’s medication regimen, drug-related problems may not be detected, a diagnosis may be missed, or discrepancies in patient admission orders may occur.3,12-14 A few studies regarding medication reconciliation conducted in larger teaching centres have been published3,15; however, there are no similar published studies from small community hospitals. Colchester Regional Hospital is a 120-bed community hospital offering medical, surgical, psychiatric, pediatric, and obstetric services. The hospital has about 3000 admissions per year with a maximum capacity of 11 beds in the Emergency Department. Before the study, the hospital’s pharmacists were involved in direct patient care in the acute care units and provided seamless care at discharge to about 60% of patients treated in these units. Many of the interventions at discharge were performed to correct discrepancies in the admission orders prepared in the Emergency Department. Given that about 80% of the hospital’s admissions originate in the Emergency Department, the authors theorized that involving a pharmacist earlier in the process would lead to more timely interventions and would help to prevent medication discrepancies. The purposes of this study were to determine if involving a pharmacist in the documentation and reconciliation of medications in the Emergency Department would result in fewer medication discrepancies and to evaluate a multidisciplinary form for medication reconciliation in the Emergency Department.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".