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Record W2151220202 · doi:10.4212/cjhp.v62i3.794

Medication Reconciliation by a Pharmacist in the Emergency Department: A Pilot Project

2009· article· en· W2151220202 on OpenAlexaffvenueabout
Andrea J Kent, Louise Harrington, Jill Skinner

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2009
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsNova Scotia Health Authority
Fundersnot available
KeywordsPharmacistMedicineEmergency departmentTriageAdverse effectMedical prescriptionEmergency medicineMedication ReconciliationMedical emergencyAdverse drug eventPatient safetyHealth careClinical pharmacyFamily medicinePharmacyNursingInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.138
GPT teacher head0.413
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2009
Admission routes3
Has abstractyes

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