TY - GEN
T1 - Evaluating Hospital Disaster Resilience Using Simulation
T2 - 24th Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2025
AU - Dewanti, Desak Ayu Clara
AU - Kanno, Taro
AU - Nishimura, Toshiki
AU - Kajiyama, Kazumi
AU - Tsubaki, Michihiro
AU - Sharikura, Sachika
AU - Yamazaki, Misumi
AU - Yoshida, Haruka
AU - Parung, Joniarto
AU - Santoso, Amelia
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Disaster preparedness
KW - Discrete-event simulation
KW - Extra waiting time
KW - Hospital resilience
KW - Intra-hospital patient flow
UR - https://www.scopus.com/pages/publications/105023190744
U2 - 10.1007/978-981-95-4472-1_21
DO - 10.1007/978-981-95-4472-1_21
M3 - Conference contribution
AN - SCOPUS:105023190744
SN - 9789819544714
T3 - Communications in Computer and Information Science
SP - 244
EP - 251
BT - Methods and Applications for Modeling and Simulation of Complex Systems - 24th Asia Simulation Conference, AsiaSim 2025, Proceedings
A2 - Cai, Wentong
A2 - Low, Malcolm
A2 - Tan, Gary
A2 - D'Angelo, Gabriele
A2 - Ta, Duong
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 November 2025 through 19 November 2025
ER -