Evidence map›Paper›PMID 31289084›Full record

ArticleBMJ open2019

Using prescribing very short answer questions to identify sources of medication errors: a prospective study in two UK medical schools.

Amir H Sam, Chee Yeen Fung, Rebecca K Wilson, Emilia Peleva, David C Kluth, Martin Lupton, David R Owen, Colin R Melville, Karim Meeran

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. 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

9 authors at 3 institutions in 1 country.

Amir H SamMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.ORCID 0000-0002-9599-9069
Chee Yeen FungMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
Rebecca K WilsonMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
Emilia PelevaMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
David C KluthMedical Education, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.
Martin LuptonMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
David R OwenMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
Colin R MelvilleDivision of Medical Education, School of Medical Sciences, University of Manchester, Manchester, UK.
Karim MeeranMedical Education Research Unit, Imperial College School of Medicine, Imperial College London, London, UK.
Imperial College London · GBUniversity of Edinburgh · GBUniversity of Manchester · GB

Funding

Medical Research Council MR/N008219/1
6 · The paper itself

Abstract

objectiveTo assess the utility and ability of the novel prescribing very short answer (VSA) question format to identify the sources of undergraduate prescribing errors when compared with the conventional single best answer (SBA) question format and assess the acceptability of machine marking prescribing VSAs.

designA prospective study involving analysis of data generated from a pilot two-part prescribing assessment.

settingTwo UK medical schools.

participants364 final year medical students took part. Participation was voluntary. There were no other inclusion or exclusion criteria. OUTCOMES: (1) Time taken to mark and verify VSA questions (acceptability), (2) differences between VSA and SBA scores, (3) performance in VSA and (4) SBA format across different subject areas and types of prescribing error made in the VSA format.

results18 200 prescribing VSA questions were marked and verified in 91 min. The median percentage score for the VSA test was significantly lower than the SBA test (28% vs 64%, p<0.0001). Significantly more prescribing errors were detected in the VSA format than the SBA format across all domains, notably in prescribing insulin (96.4% vs 50.3%, p<0.0001), fluids (95.6% vs 55%, p<0.0001) and analgesia (85.7% vs 51%, p<0.0001). Of the incorrect VSA responses, 33.1% were due to the medication prescribed, 6.0% due to the dose, 1.4% due to the route and 4.8% due to the frequency.

conclusionsPrescribing VSA questions represent an efficient tool for providing detailed insight into the sources of significant prescribing errors, which are not identified by SBA questions. This makes the prescribing VSA a valuable formative assessment tool to enhance students' skills in safe prescribing and to potentially reduce prescribing errors.

Indexed as

Clinical CompetenceEducation, Medical, UndergraduateMedication ErrorsAnalgesicsEducational MeasurementFluid TherapyHumansInsulinPilot ProjectsProspective StudiesUnited KingdomAnalgesicsInsulinclinical pharmacology

Identifiers

PMID31289084
PMCPMC6629393
OpenAlexW2961263326

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.