Evidence map›Paper›PMID 41408400›Full record

ArticleNPJ digital medicine2025

Meta-analysis of randomized controlled trials of electronic health interventions to reduce medication errors.

Michelle Natasha Colin, Salma Tri Octaviany, Michelle Darmawan, Joseph Fide Anggi, Clara Fernanda Kusuma, Angga Prawira Kautsar

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Michelle Natasha ColinFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Salma Tri OctavianyFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Michelle DarmawanFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Joseph Fide AnggiFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Clara Fernanda KusumaFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Angga Prawira KautsarFaculty of Pharmacy, Universitas Padjadjaran, Sumedang, West Java, Indonesia. angga.prawira@unpad.ac.id.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication errors, both potential and actual errors, can pose a significant safety concern in healthcare. Potential errors refer to mistakes detected and prevented (either manually or electronically), while actual errors include both harmless and harmful events, such as adverse drug events (ADEs). Electronic interventions, particularly computerized decision-support systems (CDS), aim to reduce medication errors and thus enhance patient safety. Our study conducts systematic reviews and a meta-analysis on the effectiveness of electronic interventions in decreasing medication errors compared to usual care. Randomized controlled trials (RCTs) articles from EBSCO, Embase, PubMed, and Web of Science databases were systematically screened and selected. The analysis included 12 studies, finalized by nine after addressing heterogeneity. The results demonstrated that electronic interventions were associated with a 15% reduction in risk of medication errors (RR = 0.85; 95% CI: 0.77-0.94). Subgroup analysis showed that CDS interventions were particularly effective in our study. The findings provide evidence for the potential benefit of integrating electronic systems to enhance medication safety. Further research is necessary to validate these outcomes across a range of settings.

Identifiers

PMID41408400
PMCPMC12715224

What OpenQuestion holds

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LicenceCC BY
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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.