A Dynamic Scheduler for Health Care Resources in the Emergency Department of TBRHSC on exploiting Machine Learning during Diverse Situations of COVID-19 Pandemic.

雷霆湾地区健康科学中心（TBRHSC）是安大略西北部的一个重要医疗机构。TBRHSC目前正在使用一个名为AMiON的商业软件来安排工作人员的日程。然而，由于COVID-19大流行导致过度拥挤的情况，急诊部一直面临许多不寻常的困难。尽管如此，由于流感季节而增加的额外压力仍然存在。由于TBRHSC的多名工作人员因被怀疑为冠状病毒无症状携带者而被隔离，随着患者数量激增和医生数量有限，调度变得更加关键。此外，为限制冠状病毒在医院范围内的可能暴露，TBRHSC急诊部已主动安排工作人员分为不同区域（高污染风险区和低污染风险区）。因此，为了解决所有这些新出现的问题，我们建议开发一个智能和自适应的调度程序，以确保最大限度地减少这两个区域的工作人员之间的接触，并最大限度地提高工作人员的满意度。此外，调度系统将按需分配非急诊科医生与急诊科常规工作人员合作，以提供进一步的支持。共有三名学生将在这个研究项目下接受培训。研究结果将通过出版物传播。

Thunder Bay Regional Health Sciences Centre (TBRHSC) is an essential healthcare facility in Northwestern Ontario. TBRHSC is currently utilizing a commercial software called AMiON for staff scheduling purposes. However, due to the overcrowding situation caused by the COVID-19 pandemic, the Emergency Department has been facing many unusual difficulties. Nevertheless, the extra added layer of pressure because of flu season continues to persist. Since over a number of staff at TBRHSC have been quarantined under the suspicion of being asymptomatic carriers of coronavirus, scheduling becomes even more critical with a surging number of patients and a limited number of physicians. Furthermore, to limit the possible exposure of coronavirus in the hospital premises, the TBRHSC emergency department has taken the initiative to schedule staff divided into different zones (High contamination risk zone and low contamination risk zone). Therefore, to resolve all these emerging issues, we are proposing to develop a smart and adaptive scheduler that will ensure to minimize the contact between the staff of these two zones and maximize staff satisfaction. Besides, the scheduling system will allocate non-emergency department physicians on-demand in cooperation with the regular emergency department staff for further support. A total of three students will be trained under this research project. The results of the research will be disseminated through publications.