Evidence map›Paper›PMID 42242699›Full record

ArticleJMIR formative research2026

Digital Twin-Based Virtual Hospital Platform for IT Outage Disaster Response Training: Implementation and Evaluation Study.

SungA Bae, Ye Ji Kim, Min Woo Lee, Soo Jeong Kim, Jin Young Park

Abstract readCase Reports
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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.

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

5 authors.

SungA Bae *Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Seoul, Republic of Korea.ORCID 0000-0003-1484-4645
Ye Ji Kim *Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Seoul, Republic of Korea.ORCID 0009-0005-6923-3931
Min Woo LeeDivision of Planning and Management, Office of Medical Information Technology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea.ORCID 0009-0005-6923-3931
Soo Jeong KimCenter for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Seoul, Republic of Korea.ORCID 0000-0001-8859-3573
Jin Young ParkCenter for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Seoul, Republic of Korea.ORCID 0000-0002-5351-9549

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital IT outages severely disrupt clinical workflows and use of electronic medical records, threatening patient safety and operational continuity. Traditional disaster response training faces limitations, including high resource requirements, restricted repeatability, and inability to be conducted without interrupting 24/7 hospital operations. Digital twin technology enables realistic, repeatable simulation training in virtual environments, avoiding operational disruption.

objectiveThis study developed and implemented a digital twin-based virtual hospital platform for level 1 IT outage disaster response training and evaluated its feasibility through quantitative performance metrics and participant survey feedback.

methodsA digital twin-based virtual hospital platform modeling 317 clinical spaces and 6 building entrances of Yongin Severance Hospital, South Korea, was developed to simulate a hospital information system failure (code white level 1 IT outage) with 7 patient cases of varying complexity levels, covering complete outpatient workflows from registration to payment. A total of 60 multidisciplinary participants (physicians, nurses, laboratory technicians, pharmacists, and administrative staff) were recruited through purposive sampling from clinical departments and support services directly involved in outpatient IT outage response. Emergency prescription and patient information lookup systems were integrated into the training. Performance evaluation included scenario completion rates, prescription accuracy, completion times, and operational readiness scores. Training outcomes were compared with 2023 conventional training records from the same institution using descriptive metrics. Open-ended survey responses were analyzed using structured content summarization with text mining and word cloud techniques. A 7-item operational readiness checklist was assessed by a panel of 5 training facilitators to evaluate system functional completeness.

resultsIn July 2024, a total of 60 multidisciplinary participants completed the training exercise. All 7 patient scenarios achieved 100% completion rates, with perfect accuracy in medical billing concordance and prescription entry timeliness. Scenario completion times ranged from 25 to 47 minutes, with variations reflecting testing wait times and workflow complexity. The overall operational readiness score was 80% (with 70% for digital twin platform operational readiness and 90% for emergency prescription program operational readiness). Training reduced resource consumption by 70 minutes compared to the 2023 conventional training approach, decreasing full-time equivalent requirements from 0.072 to 0.038. Open-ended survey feedback yielded 5 content categories (target extensions, mock training ideas, drug and prescription system improvement, operations and evaluation systems, and process improvement), with "realism," "collaboration," and "prescription" as the most frequent keywords.

conclusionsThis proof-of-concept study demonstrates that a simulation-oriented digital twin platform can support multi-department IT disaster response training with complete workflow execution. Identified technical gaps in user permissions and prescription classification provide a concrete development road map for institutional deployment.

Indexed as

Hospital Information SystemsDigital HealthHumansRepublic of KoreaSurveys and Questionnairesdigital twindisaster preparednesshealth information systemsIT outagesimulation trainingsurvey feedbackvirtual hospital simulation

Identifiers

PMID42242699
PMCPMC13545525

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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.