Evidence map›Paper›PMID 39827262›Full record

ReviewNPJ digital medicine2025

A pathway from fragmentation to interoperability through standards-based enterprise architecture to enhance patient safety.

Zoie Shui-Yee Wong, Yang Gong, Shin Ushiro

Abstract readReview
In one paragraph

Review 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

3 authors.

Zoie Shui-Yee WongGraduate School of Public Health, St. Luke's International University, Tokyo, Japan. zoiesywong@gmail.com.
Yang GongMcWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
Shin UshiroSchool of Medicine, Kyushu University, Fukuoka, Japan.

Funding

St. Luke's Health Science Research Fund Supplementary Research Support Program 2024
6 · The paper itself

Abstract

Creating an ontology is the essential step in natural language processing (NLP). To improve patient safety in this era of generative AI, it is crucial to develop a standards-driven, ontology-based architecture for patient safety that can seamlessly integrate with health systems, thereby facilitating effective detection and monitoring potentially preventable harms in healthcare. This visionary, whole-system approach to patient safety addresses a significant gap in establishing resilient safety systems within the healthcare sector.

Identifiers

PMID39827262
PMCPMC11743194

What OpenQuestion holds

Textmetadata
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.