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This study improves hospital disaster preparedness evaluation by developing a calibrated multi-layer discrete-event simulation (DES) model. The model integrates two types of data: Work-As-Done (WAD), collected from hospital disaster drills between 2022 and 2024, and Work-As-Imagined (WAI), derived from staff surveys and protocols. Extra Waiting Time (EWT), defined as the gap between WAD and WAI length of stay (LOS), was introduced as a resilience indicator. Results showed that the task-duration (Layer 1) was the dominant factor of EWT reductions, with values decreasing from 37.87 min in 2022 to 29.00 min in 2024. While the incorporation of transfer time (Layer 2) produced smaller additional decreases (−1.42 min in 2023 and − 0.18 min in 2024) but improved the attribution of delays by separating treatment from patient movement. Together, these findings demonstrate that while task execution is the primary influence on hospital resilience, explicit transfer modeling enhances accuracy and transparency. The framework offers a practical tool for quantifying operational inefficiencies, supporting hospital workflow evaluation under disaster conditions, and can be adapted to other mass-casualty scenarios.

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Evaluating Hospital Disaster Resilience Using Simulation: Integrating Task Duration and Intra-Hospital Patient Transfer

  • 会议论文

  • 发表日期：

    2026年

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  • Desak Ayu Clara Dewanti,
  • Taro Kanno,
  • Toshiki Nishimura,
  • Kazumi Kajiyama,
  • Michihiro Tsubaki,
  • Sachika Sharikura,
  • Misumi Yamazaki,
  • Haruka Yoshida,
  • Joniarto Parung,
  • Amelia Santoso

摘要

This study improves hospital disaster preparedness evaluation by developing a calibrated multi-layer discrete-event simulation (DES) model. The model integrates two types of data: Work-As-Done (WAD), collected from hospital disaster drills between 2022 and 2024, and Work-As-Imagined (WAI), derived from staff surveys and protocols. Extra Waiting Time (EWT), defined as the gap between WAD and WAI length of stay (LOS), was introduced as a resilience indicator. Results showed that the task-duration (Layer 1) was the dominant factor of EWT reductions, with values decreasing from 37.87 min in 2022 to 29.00 min in 2024. While the incorporation of transfer time (Layer 2) produced smaller additional decreases (−1.42 min in 2023 and − 0.18 min in 2024) but improved the attribution of delays by separating treatment from patient movement. Together, these findings demonstrate that while task execution is the primary influence on hospital resilience, explicit transfer modeling enhances accuracy and transparency. The framework offers a practical tool for quantifying operational inefficiencies, supporting hospital workflow evaluation under disaster conditions, and can be adapted to other mass-casualty scenarios.