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the first iteration has been completed, based on the
conclusions obtained during the analysis and
validation phase, the model is updated and a new
cycle is carried out. The process will be repeated
until the objectives are achieved.
An Agent-Based Model for Emergency
Departments is being designed, in which all rules
within the model concern the agents, no higher level
behaviour is modelled; it emerges as a result of local
level actions and interactions. This model describes
the complex dynamics found in a hospital ED,
representing each individual and system as an
individual agent. After ending the first cycle two
distinct kinds of agents have been identified, active
and passive. Active agents represent the individuals
involved in the ED, in this case all human actors,
such as patients, nurses or doctors. Passive agents
represent services and other reactive systems, such
as the information technology (IT) infrastructure or
central services used for performing tests.
In order to simulate the model state machines are
used to represent the actions of each agent and the
communication between agents. This takes into
consideration all the variables that are required to
represent the many different states that an individual
may be in throughout the course of their time in a
hospital emergency department, be that individual a
patient, a member of hospital staff, or any other role.
The change in these variables, invoked by an input
from an external source, is modelled as a transition
between states. The communication between
individuals is modelled as the inputs that agents
receive and the outputs they produce, both implicitly
and explicitly. In order to control the agent
interaction, the physical environment in which these
agents interact also have to be modelled, being
sufficient do it as a series of interconnected areas,
such as admissions, the waiting room, or
consultation suits.
In the next cycles new agents and state variables
will be added gradually, until the simulator behaves
as similar as possible to the real system, although
being less complex. After this, data assimilation and
optimisation techniques will be used for new
improvements of the tool. Parallel simulations with
different parameters will be performed, in order to
make adjustments to the model based on the
comparison of the results of these simulations with
data from real systems. High Performance
Computing will be necessary due to the high amount
of data and computation inherent to these both
phases.
The remainder of this article is organised as
follows; section 2 describes the related work in
healthcare operational management and simulation.
The proposed emergency department model is
detailed in section 3, while the corresponding
simulation is given in section 4. In section 5 the
future work is pointed out. Finally, section 6 closes
with conclusions.
2 RELATED WORK
The modelling and simulation of hospital emergency
departments sits at the intersection of a number of
distinct fields. In addition Agent-based techniques
have been used in the modelling of healthcare
operational management, but there are few pure
agent-based models to be found in the literature that
have been rigorously validated against their real
world counterparts.
Economics, biology, and social sciences are the
three fields in which agent-based models are most
utilised (Jones and Evans, 2008). Modelling
techniques using agents can bring the most benefit
when applied to human systems where agents
exhibit complex and stochastic behaviour, the
interaction between agents are heterogeneous and
complex, and agent positions are not fixed
(Bonabeau, 2002). In the particular case of social
sciences ABMs are used in situations where human
behaviour cannot be predicted using classical
methods such as qualitative or statistical analysis
(Norling and Sonenberg, 2000). Human behaviour is
also modelled with ABMs in the fields of
psychology (Smith and Conrey, 2007) and
epidemiology (Epstein, 2009) amongst others.
Agent technology is a useful tool when applied
to healthcare applications. Previous works modelling
healthcare systems have focused on patient
scheduling under variable pathways and stochastic
process durations, the selection of an optimal mix
for patient admission in order to optimise resource
usage and patient throughput (Hutzschenreuter et al.,
2008). Work has been performed using differing
degrees of agent-based modelling for evaluating
patient waiting times under the effects of different
ED physician staffing schedules (Jones and Evans,
2008) or patient diversion strategies (Laskowski and
Mukhi, 2008).
This proposal addresses many of the issues
surrounding the modelling and simulation of a
hospital emergency department using agent-based
technologies. The basic rules governing the actions
of the individual agents are defined, in an attempt to
understand micro level behaviour. The macro level
behaviour, which means the system as a whole,
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