Assessing Hospital Adaptive Resource Allocation Strategies in Responding to Mass Casualty Incidents

Background: Hospitals are expected to operate at a high performance level even under exceptional conditions of peak demand and resource disruptions. This understanding is not mature yet and there are wide areas of possible improvement. In particular, the fast mobilization and reconfiguration of resources frequently result into the severe disruption of elective activities, worsening the quality of care. This becomes particularly evident during the on-going coronavirus disease 2019 (COVID-19) pandemic. More resilient resource allocation strategies, that is, which adapt to the dynamics of the prevailing circumstance, are needed to maximize the effectiveness of health-care delivery. In this study, a simulation approach was adopted to assess and compare different hospital’s adaptive resource allocation strategies in responding to a sudden onset disaster mass casualty incident (MCI). Methods: A specific set of performance metrics was developed to take into consideration multiple objectives and priorities and holistically assess the effectiveness of health-care delivery when coping with an MCI event. Discrete event simulation (DES) and system dynamics (SD) were used to model the key hospital processes and the MCI plan. Results: In the daytime scenario, during the recovery phase of the disaster, a gradual disengagement of resources from the emergency department (ED) to restart ordinary activities in operating rooms and wards returned the best performance. In the night scenario, the absorption capacity of the ED was evaluated by identifying the current bottleneck and assessment of the benefit of different resource mobilization strategies. Conclusions: The present study offers a robust approach, effective strategies and new insights to design more resilient plans to cope with MCIs. It becomes particularly relevant when considering the risk of indirect damage of emergencies, where all the available resources are shifted from the care of the ordinary to the “disaster” patients, like during the on-going COVID-19 pandemic. Future research is needed to widen the scope of the analysis and take into consideration additional resilience capacities such as operational coordination mechanisms among multiple hospitals in the same geographic area.

Suboptimal rate adaptive resource allocation in multiuser OFDM communication systems

This thesis aims to study the performance of adaptive resource allocation in the downlink of multiuse OFDM systems with fixed or varialbe rate requirements (with fairness consideration) as well as low complexity algorithms for real-time implementations in practical systems. We first verify the simplifying assumption of flat transmit power over the entire bandwidth. Two different optimal and suboptimal power allocation schemes are applied in a single-user system and the decrease in the total throughput due to the presence of the power mask on subcarriers is measured. Based on the comparison of the achieved data rates, a flat transmit power is then assumed in the proposed suboptimal multiuser resource allocation algorithms. Two suboptimal resource allocation algorithms are then proposed using this simplifying assumption. The objective of the first algorithm is to maximize the total throughput while maintaining rate proportionality among the users. The proposed suboptimal algorithm prioritizes the user with the highest sensitivity to the subcarrier allocation and the variance over the subchannel gains is sued to define the sensitivity of each user. The second algorithm concerns rate adaptive resource allocation in multiuser OFDM systems with fixed rate constraints for each user. We propose a suboptimal joint subchannel and power allocation algorithm which attempts to maximize the total throughput wihile supporting the users with their minimum rate requirments. The main feature of this algorithm is its low complexity while achieving close to optimum capacity.