Evidence map›Paper›PMID 32556895›Full record

Observational studyInternational journal of clinical pharmacy2020

Evaluation of the medication reconciliation process and classification of discrepancies at hospital admission and discharge in Italy.

Mattia Dei Tos, Cristina Canova, Teresa Dalla Zuanna

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Observational study in International journal of clinical pharmacy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Mattia Dei TosEmergency Department, AULSS 2, Via C. Forlanini 71, 31029, Vittorio Veneto, Treviso, Italy.
Cristina CanovaDepartment of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan 18, Padova, Italy.
Teresa Dalla ZuannaDepartment of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan 18, Padova, Italy. teresa.dallazuanna@studenti.unipd.it.ORCID http://orcid.org/0000-0003-1244-404X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Medication errors at different transitions of care are common and potentially harmful. Medication reconciliation process should be evaluated to reduce the unintentional discrepancies. Objective This study aims to identify and classify unintentional medication discrepancies at hospital admission and discharge and associated risk factors. Setting Two general internal medicine and a pulmonology wards of an Italian non-academic hospital. Method A retrospective observational study was conducted among adult patients admitted to the wards. In order to evaluate the current medication reconciliation process of these wards, the frequency and type of unintentional chronic medication discrepancies between the physician assessment of home medication and hospital admission and discharge prescriptions were studied. Patients' characteristic associated with the presence of at least one unintentional discrepancy were evaluated. Main outcome measure Frequencies of unintentional medication discrepancies upon admission and discharge and associated patients' characteristics. Results Among the 144 patients enrolled in the study, 53 and 64 unintentional medication discrepancies were identified at hospital admission and at discharge, respectively. Both at admission and discharge a quarter of patients had at least one unintentional discrepancy. 'Medication omission' was the most frequent type of discrepancy identified and respiratory system and nervous system were the classes of medication with the highest rate of unintentional discrepancies. Unintentional discrepancies were more likely to occur in patients receiving more medicine pre-admission, longer hospitalization stays and coming from or discharged to a nursing home. Conclusion Transitions of care are critical moments for patient safety in terms of unintentional medication discrepancies and a more structured medication reconciliation process is needed. The medication reconciliation process should be considered in terms of a multidisciplinary approach involving all health professionals as well as patients and caregivers directly.

Indexed as

AdultAgedAged, 80 and overFemaleHospitalizationHumansItalyLength of StayMaleMedication ErrorsMedication ReconciliationMiddle AgedPatient AdmissionPatient DischargeRetrospective StudiesRisk FactorsItalyMedication discrepanciesMedication errorsMedication reconciliationTransitions of care

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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.