Evidence map›Paper›PMID 41142508›Full record

ArticleExploratory research in clinical and social pharmacy2025

The impact of patient information on prescribing errors: Insights from pharmaceutical interventions.

Daisuke Koike, Masahiro Ito, Keiko Tomomatsu, Ryuta Shindo, Terumi Miyashita, Junichi Yamakami, Akihiko Horiguchi, Shigeki Yamada

Abstract read
In one paragraph

Article in Exploratory research in clinical and social pharmacy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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.

  1. 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

8 authors.

Daisuke KoikeDepartment of Gastroenterological Surgery, Fujita Health University School of Medicine, Bantane Hospital, 3-6-10, Otobashi, Nakagawa ward, Nagoya Zip cord: 454-8509, Japan.
Masahiro ItoDepartment of Gastroenterological Surgery, Fujita Health University School of Medicine, Bantane Hospital, 3-6-10, Otobashi, Nakagawa ward, Nagoya Zip cord: 454-8509, Japan.
Keiko TomomatsuDepartment of Quality and Safety in Healthcare, Fujita Health University Hospital, Toyoake, 1-98, Dengakugakubo, Kutsukakecho, Toyoake, Aichi Zip cord: 470-1192, Japan.
Ryuta ShindoDepartment of Quality and Safety in Healthcare, Fujita Health University Hospital, Toyoake, 1-98, Dengakugakubo, Kutsukakecho, Toyoake, Aichi Zip cord: 470-1192, Japan.
Terumi MiyashitaDepartment of Quality and Safety in Healthcare, Fujita Health University Hospital, Toyoake, 1-98, Dengakugakubo, Kutsukakecho, Toyoake, Aichi Zip cord: 470-1192, Japan.
Junichi YamakamiDepartment of Quality and Safety in Healthcare, Fujita Health University Hospital, Toyoake, 1-98, Dengakugakubo, Kutsukakecho, Toyoake, Aichi Zip cord: 470-1192, Japan.
Akihiko HoriguchiDepartment of Gastroenterological Surgery, Fujita Health University School of Medicine, Bantane Hospital, 3-6-10, Otobashi, Nakagawa ward, Nagoya Zip cord: 454-8509, Japan.
Shigeki YamadaDepartment of Pharmacotherapeutics and Informatics, Fujita Health University School of Medicine, Toyoake, 1-98, Dengakugakubo, Kutsukakecho, Toyoake, Aichi Zip cord: 470-1192, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Medication errors are more likely to occur in patients with complex conditions, where appropriate prescribing requires accurate and comprehensive patient information. Inadequate use of such information, such as overlooking laboratory results or patient weight, can lead to dosing errors or contraindicated prescriptions, even with electronic checking systems. This study aimed to analyze prescribing errors detected through pharmaceutical interventions, focusing on the patient information in the hospital information system. Methods: A retrospective analysis was conducted on 9774 pharmaceutical interventions performed between April 2019 and March 2020. Of these, 3372 interventions related to patient information stored in hospital information system were included. Prescribing errors were categorized into five patient-specific information categories: allergy information, laboratory data, concomitant drugs, patient weight, and patient status and history. Demographic and prescription data were analyzed, and a heatmap was developed to visualize high-risk areas. Results: Among the included interventions, 1352 (40.1 %) prescriptions involved corrections of prescribing errors, with laboratory data being the most frequently utilized patient information source ( Conclusion: Laboratory data were the most frequently used information source, to prevent prescribing errors. The risk heatmap demonstrated weekends, pediatric patients, and renal dosing as high-risk areas. These findings suggest that future information systems should enhance the utility of laboratory data and incorporate tailored alerting strategies focused on high-risk patient conditions and clinical settings, such as real-time lab data alerts or weight-based dosing calculators, and potentially explore the use of AI for proactive error prevention.

Indexed as

Clinical decision support systemComputerized physician order entryMedication errorPatient safetyPharmaceutical intervention

Identifiers

PMID41142508
PMCPMC12547715

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Registered trials

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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.