Evidence map›Paper›PMID 22920446›Full record

Trial reportTrials2012

Using novel Canadian resources to improve medication reconciliation at discharge: study protocol for a randomized controlled trial.

Robyn Tamblyn, Allen R Huang, Ari N Meguerditchian, Nancy E Winslade, Christian Rochefort, Alan Forster, Tewodros Eguale, David Buckeridge, André Jacques, Kiyuri Naicker and 1 more

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01179867 (Using Novel Canadian Resources to Improve Medication Reconciliation at Discharge), which is not on this map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01179867 nacompletednot on this mapstarted 2014, after this paper: background citation

Using Novel Canadian Resources to Improve Medication Reconciliation at Discharge

TypeinterventionalSponsorMcGill UniversityRan2014 to 2019Enrolled4,014ConditionsMedication Reconciliation, Adverse Drug EventsArmsElectronic Medication Reconciliation
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Electronic tools to support medication reconciliation: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017
    Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Robyn TamblynDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal, QC H3A 1A2, Canada. robyn.tamblyn@mcgill.ca
Allen R Huang
Ari N Meguerditchian
Nancy E Winslade
Christian Rochefort
Alan Forster
Tewodros Eguale
David Buckeridge
André Jacques
Kiyuri Naicker
Kristen E Reidel

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdverse drug events are responsible for up to 7% of all admissions to acute care hospitals. At least 58% of these are preventable, resulting from incomplete drug information, prescribing or dispensing errors, and overuse or underuse of medications. Effective implementation of medication reconciliation is considered essential to reduce preventable adverse drug events occurring at transitions between community and hospital care. An electronically enabled discharge reconciliation process represents an innovative approach to this problem. METHODS/

designParticipants will be recruited in Quebec and are eligible for inclusion if they are using prescription medication at admission, covered by the Quebec drug insurance plan, admitted from the community, 18 years or older, admitted to a general or intensive care medical or surgical unit, and discharged alive. A sample size of 3,714 will be required to detect a 5% reduction in adverse drug events. The intervention will comprise electronic retrieval of the community drug list, combined with an electronic discharge reconciliation module and an electronic discharge communication module. The primary outcomes will be adverse drug events occurring 30 days post-discharge, identified by a combination of patient self-report and chart abstraction. All emergency room visits and hospital readmission during this period will be measured as secondary outcomes. A cluster randomization approach will be used to allocate 16 medical and 10 surgical units to electronic discharge reconciliation and communication versus usual care. An intention-to-treat approach will be used to analyse data. Logistic regression will be undertaken within a generalized estimating equation framework to account for clustering within units. DISCUSSION: The goal of this prospective trial is to determine if electronically enabled discharge reconciliation will reduce the risk of adverse drug events, emergency room visits and readmissions 30 days post-discharge compared with usual care. We expect that this intervention will improve adherence to medication reconciliation at discharge, the accuracy of the community-based drug history and effective communication of hospital-based treatment changes to community care providers. The results may support policy-directed investments in computerizing and training of hospital staff, generate key requirements for future hospital accreditation standards, and highlight functional requirements for software vendors.

trial registrationNCT01179867.

Indexed as

Clinical Pharmacy Information SystemsCommunity Pharmacy ServicesContinuity of Patient CareMedication ReconciliationPatient DischargePharmacy Service, HospitalResearch DesignCluster AnalysisCommunicationDrug-Related Side Effects and Adverse ReactionsElectronic Health RecordsEmergency Service, HospitalHumansLogistic ModelsMedical Record LinkagePatient Readmission

Identifiers

PMID22920446
PMCPMC3502593

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.