Evidence map›Paper›PMID 37575685›Full record

ArticleRisk management and healthcare policy2023

Analysis of Factors Related to Domestic Patient Safety Incidents Using Decision Tree Technique.

Jieun Shin, Ji-Hoon Lee, Nam-Yi Kim

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2023. 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
0.8field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

3 authors at 1 institution in 1 country.

Jieun ShinDepartment of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.ORCID 0000-0002-6192-2610
Ji-Hoon LeeDepartment of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.ORCID 0009-0000-3542-9403
Nam-Yi KimDepartment of Nursing, Konyang University, Daejeon, Republic of Korea.ORCID 0000-0003-1973-7726
Konyang University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To address the increasing number of patient safety incidents, their scope and extent should be assessed and the situations in which they occur determined. This study employed a decision tree analysis based on patient safety incident cases to identify groups at high risk for adverse patient safety incidents and provide data to develop prevention strategies for minimizing their occurrence or recurrence. Methods: In total, 8934 patient safety incidents were analyzed using the "2021 Patient Safety Report Data", which were systematically collected by the Korea Institute for Healthcare Accreditation. A decision tree analysis (Chi-square Automatic Interaction Detection) was employed to identify the characteristics associated with the degree of risk for patient safety incidents. Results: The groups most vulnerable to adverse events were those who experienced healthcare-associated infections (HAI) in long-term care facilities, followed by those experiencing HAI in tertiary hospitals, general hospitals, or clinics, and those experiencing fall-related events in neuropsychiatry departments of tertiary hospitals, general hospitals, or clinics. Conclusion: The most important factor in the degree of harm in patient safety accidents was the type of accident, followed by the type of medical institution, and then the treatment department. Particularly, HAI and falls are the most important factors determining the degree of harm in patient safety accidents.

Indexed as

decision treeincidentinfectionpatient safetyprevention

Identifiers

PMID37575685
PMCPMC10422998
OpenAlexW4385662212

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.