MODYPLAN: Early-Stage Hospital Simulation based on Treatment Chains

Abstract

Discrete-event simulation of hospitals typically specifies flow by means of a process graph through which patients are routed. While this is generally fine for models in which processes are clearly defined, e.g. smaller units such as emergency departments, it falls short of capturing the fact that a patient can in principle go from one unit to any other unit if medical procedure requires it. This problem becomes even more evident when modeling a whole hospital, at which level specifying each individual treatment through directed edges and nodes becomes unviably complex. In the past three years, we have been developing a combined hospital simulation/space design tool in which processes are defined individually by patient, as sequence of visited units imported from the Hospital Information System (treatment chains). The major advantage of this approach lies in the fact that we can now design a future space layout in which assumed capacities (staffing, equipment, required space) can be tested against the recorded patient volume, using an Agent-Based Simulation to re-enact each treatment chain. In contrast to other hospital simulations, this approach targets early stages of architectural conception, during which the actual building structure is elaborated. Using the method, we can compare and contrast different layouts during that stage, making simulation a driver for design rather than a tool for late optimization within the final floor plan.

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

“…Modeling and analyzing the queuing times for the election, the natural choice of modeling approach is a discrete event based queuing system, where the voters are the entities that run through a system of servers and queues that represent the different stations during the voting process. While a more detailed model using agents and their individual (locally optimal) paths would have been possible (see for example [ 11 , 12 ] or also [ 13 , 14 ]), an agent-based approach was deemed too detailed because of the sequential nature of the voting system, as there is no prioritization within the waiting voters and always the first in the queue is the next to vote (i.e. there is no overtaking, and therefore the individual paths of the voters are not that important).…”

Hopca

“…Khan and Callahan (1993) [142] created a queuing model to examine the operating characteristics of the system (waiting in line, length of wait) with statistical test in 200-bed hospital laboratory. [143] compared different hospital layouts based on patient routing characteristics with simulations such as process flows and agent-based simulations. [144] simulated the queuing pattern focusing on patient waiting time, patient satisfaction, and labour intensity in a pharmacy department.…”

“…Utilizing DES in architectural design was suggested by [237], especially for earlystage conceptual design. Their approach integrates DES simulation into a hospital space planning tool.…”

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

“…Modeling and analyzing the queuing times for the election, the natural choice of modeling approach is a discrete event based queuing system, where the voters are the entities that run through a system of servers and queues that represent the different stations during the voting process. While a more detailed model using agents and their individual (locally optimal) paths would have been possible (see for example [11,12] or also [13,14]), an agent-based approach was deemed too detailed because of the sequential nature of the voting system, as there is no prioritization within the waiting voters and always the first in the queue is the next to vote (i.e. there is no overtaking, and therefore the individual paths of the voters are not that important).…”