Evidence map›Paper›PMID 41135583›Full record

ArticleApplied clinical informatics2025

Electronic Health Record Downtime Events of a Hospital: A Retrospective Analysis from Adverse Event Reports.

Qichuan Fang, Jun Liang, Peng Xiang, Min Zhao, Yunfan He, Zijiao Zhang, Haofeng Wan, Yue Hu, Tong Wang, Jianbo Lei

Abstract read
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In one paragraph

Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Qichuan FangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, People's Republic of China.
Jun LiangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, People's Republic of China.
Peng XiangDepartment of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China.
Min ZhaoBig Data Center, The First Affiliated Hospital of Xiamen University, XiaMen, Fujian Province, People's Republic of China.
Yunfan HeSchool of International Relations and Public Affairs, Fudan University, Shanghai, China.
Zijiao ZhangSchool of Public Health, Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China.
Haofeng WanSchool of Mathematics and Statistics, Beijing Technology and Business University, Beijing, People's Republic of China.
Yue HuSchool of Nursing, Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China.
Tong WangSchool of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong Province, People's Republic of China.
Jianbo LeiClinical Research Center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China.

Funding

Hainan Province Clinical Medical Center QWYH2022341Hainan Province Science and Technology special fund ZDYF2022SHFZ292Key Research and Development Program of Zhejiang Province 2024C03215Municipal Natural Science Foundation of Beijing 7222306National Natural Science Foundation of China 81871455National TCM innovation team and talent support projects ZYYCXTD-C-202210Zhejiang Provincial Natural Science Foundation of China LY22H180001
6 · The paper itself

Abstract

The widespread adoption of health information technology (HIT) has deepened hospitals' reliance on electronic health records (EHR). However, EHR downtime events, which refer to partial or complete system failures, can disrupt hospital operations and threaten patient safety. Systematic research on HIT downtime events in China remains limited.This study aims to identify and classify reported EHR downtime events in a Chinese hospital, assess their frequency and severity, and propose improvement recommendations and response strategies.We identified and coded downtime events based on a Chinese hospital's adverse event reports between January 2018 and August 2022, extracting features such as time, type, and affected scope. Both descriptive and inferential statistics were used for analysis.A total of 204 EHR downtime events were identified, with 96.1% (

Indexed as

Electronic Health RecordsHospitalsMedical ErrorsHumansPatient SafetyRetrospective StudiesTime Factors

Identifiers

PMID41135583
PMCPMC12552068

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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.