Evidence map›Paper›PMID 39488434›Full record

ArticleBMJ health & care informatics2024

Understanding prescribing errors for system optimisation: the technology-related error mechanism classification.

Magdalena Z Raban, Alison Merchant, Erin Fitzpatrick, Melissa T Baysari, Ling Li, Peter Gates, Johanna I Westbrook

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

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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

7 authors.

Magdalena Z RabanCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia Magda.Raban@mq.edu.au.ORCID http://orcid.org/0000-0002-6995-2849
Alison MerchantCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Erin FitzpatrickCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Melissa T BaysariSchool of Medical Sciences, Biomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Ling LiCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Peter GatesNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, New South Wales, Australia.
Johanna I WestbrookCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0003-1083-8192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTechnology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors (TREs) occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of TREs using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data and to assess the reliability with which reviewers could independently apply the classification. MATERIALS AND

methodsUsing data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify TREs involving a review of the error in the CPOE and simulating the error in the CPOE testing environment.

resultsThe technology-related error mechanism (TREM) classification comprises six mechanism categories, one contributing factor and 19 subcategories. The categories are as follows: (1) incorrect system configuration or system malfunction, (2) opening or using the wrong patient record, (3) selection errors, (4) construction errors, (5) editing errors, (6) errors that occur when using workflows that differ from a paper-based system (7) contributing factor: use of hybrid systems.

conclusionTREs remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring TREs to inform and prioritise system improvements and has now been updated for the paediatric setting.

Indexed as

Medical Order Entry SystemsMedication ErrorsHospitals, PediatricHumansReproducibility of ResultsDecision Support Systems, ClinicalInformation TechnologyMedical InformaticsSafety Management

Identifiers

PMID39488434
PMCPMC11535662

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