Evidence map›Paper›PMID 40463795›Full record

ArticleFrontiers in veterinary science2025

First opinion practice electronic health records are a useful source of descriptions of medication errors.

Eirini Petrou, Heather Davies, Maria Aoun, Alan D Radford, David Singleton, Peter-John M Noble, David R Killick

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Eirini PetrouDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
Heather DaviesDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
Maria AounDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
Alan D RadfordDepartment of Infection and Microbiomes, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
David SingletonDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
Peter-John M NobleDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.
David R KillickDepartment of Small Animal Clinical Science, Institute of Infection, Veterinary and Ecological Science, Leahurst, University of Liverpool, Liverpool, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Medication error (MedE) is a leading global cause of harm in human healthcare with significance both in patient morbidity and mortality, and consequent legal and financial issues. Despite this, MedEs are a poorly explored area in veterinary medicine. Research has so far focussed on survey work and errors spontaneously reported to third parties, such as professional indemnity providers. Aim: Determine if MedEs can be successfully identified in first opinion electronic health records (EHRs). Animals: EHRs pertaining to animals treated in UK first opinion practice. Materials and methods: Regular expressions (REGEX) were designed (with assistance from a domain expert) to identify explicit reference to MedEs in the SAVSNET EHR dataset. Identified MedEs were then classified by the linear sequence of medication therapy, the degree of harm caused, the role of the person who made the error, and the medication type involved. Results: In total, 6,665 EHRs were identified by the REGEX, of which a random 2,847 were manually reviewed, with 1,023 (35.9%) matching the MedEs case definition. Of these MedEs, 29.5% ( Conclusion: EHRs are a useful source of data on MedEs. MedEs are a common cause of patient harm in veterinary practice. The data provided here highlights drug classes at higher risk of problems for which mitigating action and/or education interventions are indicated.

Indexed as

adverse eventgeneral practiceinformaticsmedical errorspatient safetypharmacovigilanceREGEXveterinary medicine

Identifiers

PMID40463795
PMCPMC12130827

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.