Evidence map›Paper›PMID 32796084›Full record

ArticleBMJ health & care informatics2020

Changes in medication administration error rates associated with the introduction of electronic medication systems in hospitals: a multisite controlled before and after study.

Johanna I Westbrook, Neroli S Sunderland, Amanda Woods, Magda Z Raban, Peter Gates, Ling Li

Abstract readMulticenter Study
In one paragraph

Article in BMJ health & care informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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.

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

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

  1. Pooled it
  2. Pooled it
  3. Observational
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  7. Review
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  12. Observational
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  14. TeamSTEPPS improves patient safety.BMJ open quality · 2024
    Article
  15. Article
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  17. Observational
  18. Article
  19. Article
  20. Effect of automated unit dose dispensing with barcode scanning on medication administration errors: an uncontrolled before-and-after study.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2021
    Article
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

6 authors.

Johanna I WestbrookCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia johanna.westbrook@mq.edu.au.ORCID http://orcid.org/0000-0003-1083-8192
Neroli S SunderlandCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia.
Amanda WoodsCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia.
Magda Z RabanCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia.
Peter GatesCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia.
Ling LiCentre for Health Systems and Safety Research, Macquarie University Australian Institute of Health Innovation, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic medication systems (EMS) have been highly effective in reducing prescribing errors, but little research has investigated their effects on medication administration errors (MAEs).

objectiveTo assess changes in MAE rates and types associated with EMS implementation.

methodsThis was a controlled before and after study (three intervention and three control wards) at two adult teaching hospitals. Intervention wards used an EMS with no bar-coding. Independent, trained observers shadowed nurses and recorded medications administered and compliance with 10 safety procedures. Observational data were compared against medication charts to identify errors (eg, wrong dose). Potential error severity was classified on a 5-point scale, with those scoring ≥3 identified as serious. Changes in MAE rates preintervention and postintervention by study group, accounting for differences at baseline, were calculated.

results7451 administrations were observed (4176 pre-EMS and 3275 post-EMS). At baseline, 30.2% of administrations contained ≥1 MAE, with wrong intravenous rate, timing, volume and dose the most frequent. Post-EMS, MAEs decreased on intervention wards relative to control wards by 4.2 errors per 100 administrations (95% CI 0.2 to 8.3; p=0.04). Wrong timing errors alone decreased by 3.4 per 100 administrations (95% CI 0.01 to 6.7; p<0.05). EMS use was associated with an absolute decline in potentially serious MAEs by 2.4% (95% CI 0.8 to 3.9; p=0.003), a 56% reduction in the proportion of potentially serious MAEs. At baseline, 74.1% of administrations were non-compliant with ≥1 of 10 procedures and this rate did not significantly improve post-EMS.

conclusionsImplementation of EMS was associated with a modest, but significant, reduction in overall MAE rate, but halved the proportion of MAEs rated as potentially serious.

Indexed as

Drug Administration ScheduleMedication ErrorsMedication Systems, HospitalPharmaceutical PreparationsEfficiency, OrganizationalHospitals, TeachingHumansPharmaceutical Preparationsmedical informaticsnursingpatient carerecord systems

Identifiers

PMID32796084
PMCPMC7430327

What OpenQuestion holds

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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.