PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Umemoto Mizuki Co-author(s): Kadono Shunsuke : kadono-shunsuke038@g.ecc.u-tokyo.ac.jp Mitsuhashi Daichi : mitsuhashi@g.ecc.u-tokyo.ac.jp Kanno Taro : tkanno@g.ecc.u-tokyo.ac.jp Kajiyama Kazumi : kazumi@qj8.so-net.ne.jp Sharikura Sachika : sharikura-@cmed.showa-u.ac.jp Ikari Ryoko : wachiryo@gmail.com Yoneyama Masashi : yoneyama-m@cmed.showa-u.ac.jp

Modeling and simulation of in-hospital medical processes in the event of disaster for the evaluation of BCP and training
Background and purpose: Disaster-base hospitals are expected to play a central role in regional disaster medicine and required to provide medical treatment to many injured patients in the event of large-scale disasters. To be well prepared for disasters and improve the response capability, disaster response training based on the well-designed BCPs is demanded for hospital staff. However, traditional live exercise simulation is costly and has many restrictions from the ordinal hospital operations and activities. Also, there is no established method to evaluate the effectiveness and resilience of BCPs. With this background, this study aims to develop a highly realistic computer simulation model of the whole processes of disaster medicine in disaster-based hospitals that can be used for training and BCP evaluation. As the first step of this purpose, we are developing a model of the in-hospital medical processes in the event of disasters. Methods: By analyzing relevant documents including disaster training scenarios, observing an actual disaster response training, and interviewing with the medical staff who are in charge of hospital disaster management, we identified some important medical processes and response tasks, critical resources and decisions making required for each task, movement of staff and patients in the hospital, and communication among different areas in the hospital and its contents. Also, we developed a patient model that describes the detailed information about a patient such as vital signs, condition of injuries, other physical conditions, required medical treatments, and time and quantity of resources required for each treatment. Results: We developed a process-based simulation with the models developed and conducted simulations of disaster medical processes in the target hospital using an actual disaster training scenario considering patients arrivals, resource supply, and staff gathering. Each process is executed when there are patients waiting for the process and there is a necessary quantity of resources for the task required, including staff, medical materials and equipment, and a vacancy in the room or the area to perform the process. This simulation outputs the flow of patients in the hospital and resource consumptions in each area, and we could observe that the entire disaster medical processes including triage, in-hospital transportation of the patients, examination and treatment, operations, hospitalization, and so on. Also, we observed that the simulation could replicate the situation such as the stuck processes due to the lack of resources, for example, the number of patients who are waiting for examinations such as X-ray photography (XP) and computerized tomography (CT) were increasing after the disaster occurrence because the capacities of the rooms for those examinations are too small to accept many patients. In addition, we conducted simulations with modified scenarios, different patient arrivals and different resource allocation policy, to observe the scenario dependency for model verification. The results showed that medical processes were likely to be stuck in the scenario in which many severely injured patients arrive with short intervals. The results also suggest that the entire medical processes proceed efficiently without less stuck when resources are allocated to the patients with triage level of red . Discussion: By comparing the simulation results with different scenarios, it is expected to find bottlenecks as well as potential problems efficiently in the current disaster response rules and resource allocation policy. It is also expected to use this simulation for simulation-based optimization to better design BCPs as well as to find challenging scenarios for training. Furthermore, we intend to extend this simulation into human-in-the-loop simulation and use it for training and exercise. Conclusion: We developed a process-based simulation that can replicate the situation in hospitals in the event of a disaster. In the next step, we will extend the model to include activities in the command post of treatment areas, disaster response headquarters, and normal duties in the hospital, as well as communications and interactions among those different areas and sections in the hospital and develop a precise and comprehensive human-in-the-loop simulation.

Modeling and simulation of in-hospital medical processes in the event of disaster for the evaluation of BCP and training

Background and purpose: Disaster-base hospitals are expected to play a central role in regional disaster medicine and required to provide medical treatment to many injured patients in the event of large-scale disasters. To be well prepared for disasters and improve the response capability, disaster response training based on the well-designed BCPs is demanded for hospital staff. However, traditional live exercise simulation is costly and has many restrictions from the ordinal hospital operations and activities. Also, there is no established method to evaluate the effectiveness and resilience of BCPs. With this background, this study aims to develop a highly realistic computer simulation model of the whole processes of disaster medicine in disaster-based hospitals that can be used for training and BCP evaluation. As the first step of this purpose, we are developing a model of the in-hospital medical processes in the event of disasters. Methods: By analyzing relevant documents including disaster training scenarios, observing an actual disaster response training, and interviewing with the medical staff who are in charge of hospital disaster management, we identified some important medical processes and response tasks, critical resources and decisions making required for each task, movement of staff and patients in the hospital, and communication among different areas in the hospital and its contents. Also, we developed a patient model that describes the detailed information about a patient such as vital signs, condition of injuries, other physical conditions, required medical treatments, and time and quantity of resources required for each treatment. Results: We developed a process-based simulation with the models developed and conducted simulations of disaster medical processes in the target hospital using an actual disaster training scenario considering patients arrivals, resource supply, and staff gathering. Each process is executed when there are patients waiting for the process and there is a necessary quantity of resources for the task required, including staff, medical materials and equipment, and a vacancy in the room or the area to perform the process. This simulation outputs the flow of patients in the hospital and resource consumptions in each area, and we could observe that the entire disaster medical processes including triage, in-hospital transportation of the patients, examination and treatment, operations, hospitalization, and so on. Also, we observed that the simulation could replicate the situation such as the stuck processes due to the lack of resources, for example, the number of patients who are waiting for examinations such as X-ray photography (XP) and computerized tomography (CT) were increasing after the disaster occurrence because the capacities of the rooms for those examinations are too small to accept many patients. In addition, we conducted simulations with modified scenarios, different patient arrivals and different resource allocation policy, to observe the scenario dependency for model verification. The results showed that medical processes were likely to be stuck in the scenario in which many severely injured patients arrive with short intervals. The results also suggest that the entire medical processes proceed efficiently without less stuck when resources are allocated to the patients with triage level of red . Discussion: By comparing the simulation results with different scenarios, it is expected to find bottlenecks as well as potential problems efficiently in the current disaster response rules and resource allocation policy. It is also expected to use this simulation for simulation-based optimization to better design BCPs as well as to find challenging scenarios for training. Furthermore, we intend to extend this simulation into human-in-the-loop simulation and use it for training and exercise. Conclusion: We developed a process-based simulation that can replicate the situation in hospitals in the event of a disaster. In the next step, we will extend the model to include activities in the command post of treatment areas, disaster response headquarters, and normal duties in the hospital, as well as communications and interactions among those different areas and sections in the hospital and develop a precise and comprehensive human-in-the-loop simulation.

Paper UM119 Preview

Author and Presentation Info

Lead Author Name: Mizuki Umemoto (umemoto-mizuki9492017@g.ecc.u-tokyo.ac.jp)

Bio: Mizuki Umemoto is the 2nd year of master's degree student in the department of engeneering at the University of Tokyo. His current interests are training and testing preparedness for major accidents and disasters in disaster base hospitals. His graduation thesis addresses a methodology to formulate a business continuity plan from the perspective of resource management(e.g. human, products, money, information, trust)

Country: Japan
Company: The University Of Tokyo
Job Title: Master 2nd

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper UM119 Preview

Author and Presentation Info

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