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Appl Clin Inform 2025; 16(05): 1419-1429
DOI: 10.1055/a-2701-5819
Research Article

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

Authors

  • Qichuan Fang

    1   School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, People's Republic of China

  • Jun Liang

    1   School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, People's Republic of China
    2   Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China
    3   National Key Laboratory of Transvascular Implantable Devices, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China
    4   Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China

  • Peng Xiang

    2   Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People's Republic of China
    5   Intelligent Medical Research Center, Zhejiang University Institute of Computer Innovation Technology, Hangzhou, Zhejiang Province, People's Republic of China

  • Min Zhao

    6   Big Data Center, The First Affiliated Hospital of Xiamen University, XiaMen, Fujian Province, People's Republic of China
    7   Department of Gynecology, The First Affiliated Hospital of Xiamen University, XiaMen, Fujian Province, People's Republic of China

  • Yunfan He

    8   School of International Relations and Public Affairs, Fudan University, Shanghai, China
    9   National Institute of Intelligent Evaluation and Governance, Fudan University, Shanghai, People's Republic of China

  • Zijiao Zhang

    10   School of Public Health, Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China

  • Haofeng Wan

    11   School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, People's Republic of China

  • Yue Hu

    12   School of Nursing, Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China

  • Tong Wang

    13   School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong Province, People's Republic of China
    14   School of Basic Medical Sciences, Shandong University, Jinan, Shandong Province, People's Republic of China
    15   Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong Province, People's Republic of China

  • Jianbo Lei

    16   Clinical Research Center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, People's Republic of China
    17   The First Affiliated Hospital, Hainan Medical University, Haikou, Hainan Province, People's Republic of China
    18   Center for Medical Informatics, Institute of Advanced Clinical Medicine, Peking University, Beijing, People's Republic of China

Funding This work was supported by the National Natural Science Foundation of China (grant number: 81871455); the Zhejiang Provincial Natural Science Foundation of China (grant number: LY22H180001); the Municipal Natural Science Foundation of Beijing (grant number: 7222306); the National TCM innovation team and talent support projects (grant number: ZYYCXTD-C-202210); the Key Research and Development Program of Zhejiang Province (grant number: 2024C03215); the Hainan Province Science and Technology special fund (grant number: ZDYF2022SHFZ292); and the Hainan Province Clinical Medical Center (grant number: QWYH2022341).
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Abstract

Background

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.

Objective

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.

Methods

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.

Results

A total of 204 EHR downtime events were identified, with 96.1% (n = 196) unplanned. The most frequent categories were medication-related events (n = 52, 25.5%), imaging-related events (n = 35, 17.2%), and accounting and billing-related events (n = 17, 8.3%). For severity, 76.0% (n = 155) of events were reported as patient care disruptions, while 76.5% (n = 156) occurred within certain departments. In terms of time, the daily downtime incidence was 0.142 (95% CI: 0.122–0.164) on weekdays versus 0.064 (95% CI: 0.044–0.090) on weekends, with an incidence rate ratio (IRR) of 2.22 (95% CI: 1.52–3.25). The downtime incidence during the morning period was 0.0130 per hour (95% CI: 0.0107–0.0156), which was higher than other time periods, with IRRs ranging from 1.42 (95% CI: 1.06–1.90) to 22.2 (95% CI: 12.66–38.92).

Conclusion

In this study, analysis of EHR downtime events in a Chinese hospital identified three key issues: high-risk downtime in medication processes, peak occurrence periods on weekdays and during morning hours, and significant clinical care disruptions. Recommended measures include implementing tiered contingency protocols, enhancing technical resilience, and establishing standardized reporting mechanisms.

Keywords

downtime - electronic health records - health information technology - patient safety - accident prevention

Protection of Human and Animal Subjects

We state that a prior approval was obtained from the biomedical ethics committee of the hospital where the second author works (IRB-2021–841) and that the experimental data were anonymized in this study.


Supplementary Material



Publication History

Received: 28 April 2025

Accepted: 14 September 2025

Article published online:
24 October 2025

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