Factors associated with medical device-related sentinel events in Korea: A secondary analysis of the Korea Patient Safety Reporting & Learning System database

Purpose: This study aims to identify the factors associated with medical device-related sentinel events in Korean medical institutions and to provide information that can be used to support prevention strategies and institutional improvement.Methods: A secondary analysis was conducted using data reported to the Korea Patient Safety Reporting & Learning System from 2020 to 2024. Of the 84,263 reported safety incidents, 532 cases related to medical devices were analyzed. Descriptive statistics, the chi-square test, and binomial logistic regression analysis were performed to identify factors influencing sentinel events.Results: Of the 532 incidents, 46 (8.6%) were sentinel events. The results of the binomial logistic regression analysis showed that the odds of sentinel events were 5.43 times higher in patients in their 70s than in those in their 30s. General hospitals had 76% lower odds of serious medical device accidents compared to hospitals.Conclusion: Patient age and hospital type are factors influencing medical device-related sentinel events. To prevent such events, medical institutions should strengthen professional management and monitoring of elderly patient care, and government support for small hospitals should also be considered.

Authors

Publication Details

Journal
The Journal of Korean Academic Society of Nursing Education
Published
2026-05-31
DOI
https://doi.org/10.5977/jkasne.2026.32.2.261
Primary Topic
Patient Safety and Medication Errors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors associated with medical device-related sentinel events in Korea: A secondary analysis of the Korea Patient Safety Reporting & Learning System database

김한아, Youngjin Kim
The Journal of Korean Academic Society of Nursing Education
Patient Safety and Medication Errors
article

Factors associated with medical device-related sentinel events in Korea: A secondary analysis of the Korea Patient Safety Reporting & Learning System database

김한아, Youngjin Kim
article en

Abstract

Purpose: This study aims to identify the factors associated with medical device-related sentinel events in Korean medical institutions and to provide information that can be used to support prevention strategies and institutional improvement.Methods: A secondary analysis was conducted using data reported to the Korea Patient Safety Reporting & Learning System from 2020 to 2024. Of the 84,263 reported safety incidents, 532 cases related to medical devices were analyzed. Descriptive statistics, the chi-square test, and binomial logistic regression analysis were performed to identify factors influencing sentinel events.Results: Of the 532 incidents, 46 (8.6%) were sentinel events. The results of the binomial logistic regression analysis showed that the odds of sentinel events were 5.43 times higher in patients in their 70s than in those in their 30s. General hospitals had 76% lower odds of serious medical device accidents compared to hospitals.Conclusion: Patient age and hospital type are factors influencing medical device-related sentinel events. To prevent such events, medical institutions should strengthen professional management and monitoring of elderly patient care, and government support for small hospitals should also be considered.

The Journal of Korean Academic Society of Nursing EducationVol. 32(2)
Good health and well-being
Openalex Percentile: Top 4%
Patient Safety and Medication Errors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.