Evidence map›Paper›PMID 40589011›Full record

ArticleJournal of the Chinese Medical Association : JCMA2025

Timely patient data-driven resource planning to optimize in-hospital emergent evacuation.

Shu-Chen Kuo, Shih-Hsin Hung, Kuan-Jui Tseng, An-Yeh Lin, Shin-Shang Chou

Abstract read
In one paragraph

Article in Journal of the Chinese Medical Association : JCMA, 2025. 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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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shu-Chen KuoDepartment of Nursing, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Shih-Hsin HungDepartment of Nursing, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Kuan-Jui TsengDepartment of Nursing, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
An-Yeh LinInformation Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Shin-Shang ChouDepartment of Nursing, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital fires pose significant threats, yet Hospital Incident Command Centers (HICS) often lack standardized methods for assessing the evacuation devices and workforce needed for patients of varying illness severity. Frontline nurses typically rely on personal experience and paper-based assessments, leading to communication challenges and workforce shortages, particularly during night shifts. The Emergency Evacuation Information System (EEIS) was developed to connect hospital information systems and patient data, optimizing evacuation efficiency.

methodsThis mixed-methods study consisted of four phases: (1) Twenty-nine senior nurses participated in three expert group discussions, and the meeting minutes were thematically analyzed to develop a draft EEIS evacuation framework; (2) the accuracy of the EEIS draft algorithms was tested by retrieving evacuation data and validated through accuracy assessment methods; (3) the EEIS was implemented hospital-wide, with consistency between EEIS and nurse assessment data validated using kappa agreement; (4) think-aloud methods with four preset questions collected in-charge nurses' feedback during fire drills.

resultsIn phase 1, an evacuation framework was established covering ward resources, patient evacuation devices, and hospital support resources. Phase 2 involved building the EEIS by integrating patient data from multiple systems, achieving 100% accuracy. In phase 3, EEIS-managed units in general wards and intensive care units showed excellent agreement with nurse assessments ( κ = 0.974, p < 0.000; 0.86, p < 0.000). In phase 4, feedback from 21 in-charge nurses after fire drills emphasized the need for accessible power outlets and wireless network connectivity outside the building to optimize response procedures.

conclusionThe EEIS rapidly provides adequate resources to nurses and facilitates communication with the HICS, ensuring the efficient and safe evacuation of patients. This system can be implemented in other digitized hospitals, with future developments potentially integrating the EEIS with local fire departments to enhance rescue operations.

Indexed as

Disaster PlanningHospital Information SystemsFiresHumansEmergenciesHospitalsNursingSafety

Identifiers

PMID40589011
PMCPMC12637149

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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.