ArticlePDF Available

A systematic literature review of simulation models for non-technical skill training in healthcare logistics

Authors:
  • KTH Royal Institute of Technology und Bremer Institut für Produktion und Logistik (BIBA)

Abstract and Figures

Background Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care. Methods A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality. Results Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low. Conclusions An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.
Content may be subject to copyright.
R E S E A R C H Open Access
A systematic literature review of simulation
models for non-technical skill training in
healthcare logistics
Chen Zhang
1*
, Thomas Grandits
2
, Karin Pukk Härenstam
3,4
, Jannicke Baalsrud Hauge
5
and Sebastiaan Meijer
2
Abstract
Background: Resource allocation in patient care relies heavily on individual judgements of healthcare professionals.
Such professionals perform coordinating functions by managing the timing and execution of a multitude of care
processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing
realistic representations have been developed. These simulations can be used to facilitate understanding of various
situations, coordination training and education in logistics, decision-making processes, and design aspects of the
healthcare system. However, no study in the literature has synthesized the types of simulations models available for
non-technical skills training and coordination of care.
Methods: A systematic literature review, following the PRISMA guidelines, was performed to identify simulation
models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This
article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM
digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge
foundation of our literature study. The screening process involved a query-based identification of papers and an
assessment of relevance and quality.
Results: Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of
simulation models can be used for constructing scenarios for addressing different types of problems, primarily for
training and education sessions. The papers identified were classified according to their utilized paradigm and
focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant
approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we
approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became
less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and
participatory simulations have increased in absolute terms, but the share of these modeling techniques among all
simulationsinthisfieldremainslow.
Conclusions: An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth
in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that
the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers
can be trained. However, more system-level and complex system-based approaches are limited and use methods
other than discrete-event simulation.
Keywords: Quality, Safety, Logistical simulations, Non-technical skills
* Correspondence: chenzh@kth.se
1
School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal
Institute of Technology, 2010, Röntgenvägen 1, 14152 Huddinge, Sweden
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Zhang et al. Advances in Simulation (2018) 3:15
https://doi.org/10.1186/s41077-018-0072-7
Background
Quality and safety in healthcare depend on the success-
ful interaction between multiple teams, individuals, and
support processes aimed at making the right resources,
such as medications, medical equipment, information,
and people, available at the right time [1,2]. Further-
more, in many healthcare settings, resource utilization
must be prioritized such that the person most in need of
a resource from a medical perspective will receive it.
The cost of failure is high, both in terms of personal tra-
gedies as well as the socio-economic burden of increased
costs due to prolonged treatments or hospital stay [3].
Many of the everyday decisions regarding how re-
sources will be used for patient care are made by indi-
viduals and networks of people performing coordinating
functions, in the sense that they manage the timing and
execution of many care processes of multiple patients.
Their decisions often depend on judgements combining
perspectives on the relevant medical conditions, the re-
sources at hand, and the urgency of the situation; their
decisions also depend on receiving information to help
make sense of the situation as well as managing high
stakes and competing goals [4].
Little is known about how these prioritizing and co-
ordination skills are learned, how people performing
them build their mental system models, what informa-
tion and strategies they use, and which work practices
are most successful. Most of the individuals performing
coordination tasks are trained on the job in an unsys-
tematic manner, and the knowledge remains, for the
most part, tacit.
Simulation in healthcare is well known as a method for
training individuals and teams in escalating situations sur-
rounding individual patients [5]. To create meaningful sim-
ulations for training the non-technical skills used in
coordination [6], there is a need to develop simulations of
logistical challenges in a systematic manner as well as to de-
scribe and develop learning outcomes for the non-technical
skills used in coordination. To support this development, it
is important to know what types of logistical problems can
be addressed by what types of simulations.
Logistics is one of many growing fields in healthcare
management. This trend is driven by various societal im-
pacts; population growth and an aging society have already
put pressure on the operation of healthcare systems [7,8].
While healthcare logistics has been defined in various ways
by researchers, in this paper, we define it as operational
handlings for the delivery of care, including its supportive
services, from origination to recipient.Focusing on the re-
cipient of services, healthcare logistics could be
patient-centric or material-centric. Patient-centric logistics
relate to patient flows through the healthcare system. In
this context, quality, safety, and efficiency of services for pa-
tients are keywords. Material-centric logistics address the
positioning, storage, and circulation of goods and materials,
such as blood and pharmaceutical products, within the hos-
pital or the healthcare system.
Computer-based simulation plays an important role in
the operational support of healthcare logistics. Generally,
simulation can be useful in the design of complex
social-technical systems [9]. As an innovative technology
for adding analytical capacity, simulation can be used as
an intermediate test in the (re)design of organizational
rules and structures, workflow process management,
performance, and avoidance of human errors [10,11].
More specifically, according to Jun et al. [12], simulation
could provide benefits such as more effective redesign or
innovation, deeper insights into barriers and incentives
to adoption, and provision of an environment to
bench-testfinal products prior to formal release. A
change or improvement in real systems, however, might
be expensive or dangerous, and balancing resource allo-
cations is a central non-technical skill for healthcare
professionals. Simulation adds value by providing a solu-
tion for training individuals to solve customized prob-
lems in a virtual, persuasive environment.
The application of discrete-event simulation in health-
care began to grow considerably at the end of the 1990s
[12]; however, it remains unknown what type of simula-
tions could be used to train, develop, and test
non-technical aspects of coordination. Many types of
simulation paradigms exist today. Discrete-event simula-
tion, system dynamics, and agent-based simulation are
the most utilized tools for modeling and analyzing sys-
tems according to the users interests and the specific
task addressed. Discrete-event simulation is a tool for
assessing the efficiency of delivery structures, forecasting
changes in patient flow and examining resource effi-
ciency in staffing [12]. System dynamics focus on the ef-
fect of structure on behavior [13]. Instead of addressing
individual transactions, system dynamics is commonly
used for higher level problems, such as strategic decision
making, management controls, or policy changes [14].
Agent-based simulation is based on a bottom-upcon-
struction for the provision of emergent phenomena
based on individual interactions of resource units [15].
Literature reviews have been conducted with explicit
focus on the application of simulation in patient flow or
material flow. However, previous literature reviews have
been limited in at least one of the following aspects: (1)
Reviews usually address simulation of healthcare logistics
in a very narrow manner, analyzing a single key aspect
such as low stakeholder engagement [16], a single simula-
tion technique [17], or a single department; (2) most re-
views have examined papers published before 2012.
This study is a continuation of the work by
Dieckmann et al. [6], with a focus on the identification
of available simulation models to provide meaningful
Zhang et al. Advances in Simulation (2018) 3:15 Page 2 of 16
training of non-technical skills in healthcare logistics.
This is the perspective through which the literature was
reviewed and understood. Given the large number of
training simulations published, it is of interest to explore
the diversity in this genre. The objective of this study is
to provide a systematic literature review to answer the
following research question:
(1) What types of simulation models are currently
available for training non-technical skills in handling
logistical issues?
Methods
Search strategy
To answer the research question, the Web of Science Core
Collection, the ACM Digital Library, and JSTOR were
searched to retrieve articles focusing on simulation in
healthcare logistics between 1998 and 2017. We utilized
papers from these three databases because all of them
rigorously select core journals and the keynote proceed-
ings of conferences. The search terms were divided into
the following two categories: patient-centric queries and
material-centric queries. The papers were screened follow-
ing the Preferred Reporting Items for Systematic Reviews
and Meta-Analysis (PRISMA) guidelines.
The keywords were formulated by the individual re-
viewers to identify papers on relevant simulation tech-
niques and investigated systems, as summarized in
Tabl e 1.Keywordssuchashealthcare,”“patient flow,
pharma*,”“blood,and drugspecify the issues ad-
dressed. Keywords such as simulation,”“system dynam-
ics,”“simulator,and gamespecify the research methods
implemented.
Paper inclusion criteria
The criteria for inclusion in this review were that studies
addressed the research question and strive to improve
the performance of healthcare logistics. As the focus was
logistical issues in healthcare management, publications
regarding epidemiology, nutrition process improvement,
and statistical analysis of health programs were not in-
cluded. Abstracts, book reviews of limited length, and
papers not granting access to full texts were also dis-
carded. In addition to these general requirements,
criteria for classification were implemented:
(1) Application-oriented paper. The paper employs at
least one simulation technique and presents a
detailed scenario, or experiment, of a real-world
healthcare system.
(2) Subjective and methodological paper. The paper
focuses on subjective and methodological
perspectives on simulation techniques but might
not report a use case (Fig. 1).
The scope of the research and focus area were decided
after the screening process. Simulation paradigms were
classified based on statements from the authors; if no simu-
lation technique was stated, the conceptualization frame-
work was checked to determine its relevant category.
Results
Following the retrieval of papers, discarding of dupli-
cates, and review by the authors, the total number of es-
sential publications was 294. The search identified 248
patient-centric and 46 material-centric papers. The
patient-centric spectrum included 214 problem-solving
papers, among which 114 utilized discrete-event simula-
tion. For material-centric papers, discrete-event simula-
tion was the dominant simulation paradigm as well. The
number of publications for the past 5 years remained
high, reinforcing our supposition that there is much
knowledge to be gained from recent publications. The
repository is available in the declaration.
For qualitative analysis, representative papers, listed in
Table 2, were identified. The papers featured statements
of the relevant research questions or a description of the
investigated system. We considered the number of pub-
lications utilizing different simulation techniques, scopes
of research, and tools.
Following the screening process, we identified the ques-
tion levels and the categories of addressed issues. The fol-
lowing question levels were derived: single department/
unit, cross-department/unit, and cross-institutional. The
Table 1 Queries used for the different databases
Database Patient-centric queries Material-centric queries
Web of Science
Core Collection
TI = ((healthcareOR health SAME care)
AND (system SAME dynamicsOR patient
SAME flowOR gam*)
TI = (healthcareOR health SAME care OR care)
AND TS = (pharma*OR bloodOR drug)
AND TI = (simulationOR system SAME dynamic*
OR simulator*OR gam*)
ACM recordAbstract:(+(health care”“healthcare)+
(system dynamics”“patient flow”“gam*))
recordAbstract:(+(hospital”“drug”“pharma*”“blood)+
(simulation”“simulator*”“gam*))
JSTOR ti:(healthcareOR health care) AND
(system dynamicsOR patient flow
OR gameOR simulation)
ti:((drugOR hospitalOR bloodOR pharmaceutical)
AND (system dynamicsOR patient flowOR game
OR simulationOR simulator))
SAME, OR, and AND are logic operators of keywords
Zhang et al. Advances in Simulation (2018) 3:15 Page 3 of 16
category single department/unit included studies that
model operation within a single department in an
organization. The category cross-department/unit in-
cluded studies that simulate multiple departments/units
within the same organization. The category
cross-institutional included the simulation modeling of in-
teractions and flows between healthcare service providers
in a large-scale network with widespread distribution re-
gions. We identified the following categories of addressed
issues: care pathway and appointment, staffing decision
making, work procedures, specialized transport, facility
design, healthcare systems, supply chain, inventory man-
agement, network distribution and dispatching, network
configuration, procurement logistics, methodological con-
tributions, and miscellaneous. The facility design was con-
sidered because architectural planning is a strategic
decision that has a durable and profound effect on health-
care operation. The miscellaneous category included all
research publications that we were not able to clearly clas-
sify into at least one of the abovementioned categories.
Logistical simulationsreview
Discrete-event simulation
Discrete-event simulation has been applied to model
and analyze all aspects of logistics management in
healthcare. In particular, patient flow management and
planning of staffing requirement are effective applica-
tions of this simulation technology. Our profiling of the
literature is mostly in line with the findings of previous
literature reviews; that is, discrete-event simulation is a
useful tool with respect to improving patient flow, man-
aging bed capacity, and scheduling and utilizing of
resources [16,17]. DeRienzo et al. addressed the effect
of nursing capacity by comparing different nursing sizes
and demonstrated the applicability of supporting health-
care managers in handling operative tasks [18]. Deva-
priya et al. also developed a decision-supporting tool
based on discrete-event simulation for the strategic plan-
ning of hospital bed capacity [19]. Bhattacharjee et al.
analyzed appointment scheduling policies for patients to
be treated by a medical scanning machine [20]. Vasilakis
et al. developed a discrete-event simulation to study how
long it took for patients to obtain their appointments
from their referral [21]. Jørgensen et al. investigated in-
ternal blood logistics in hospitals and evaluated the ef-
fects of various management controls on the waiting
times for accessing blood samples [22]. This simulation
paradigm is most suitable for the realistic representation
of processes in health services for analyzing what-if
scenarios and assessing the performance of a logistical
system.
System dynamics
System dynamics is used for organizational simulations.
The paradigm is a mechanism-driven one for making
decisions strategically for health services and resources
from a global perspective. For instance, Rashwan et al.
developed a system dynamics simulation to study bed
blocking in Irish hospitals [23]. The focus was twofold:
testing the policies for solving delayed discharges and
envisaging the counterproductive and unintended conse-
quences of these new policies [24]. Brailsford et al. simu-
lated patient flow perspectives to identify system-wide
bottlenecks [25]. Through the simulation, Lane et al.
Fig. 1 PRISMA flow diagram of assessment procedure and results: number of records included and excluded and reasons
Zhang et al. Advances in Simulation (2018) 3:15 Page 4 of 16
Table 2 Catalog of papers
Focused issue Reference Paradigm Scale Software Representative main finding
Care pathway and
appointment
[1921,25,26,28,29,43,47,
5087,88127]
DES (63); ABS (3);
SD (3); mixed (4),
Misc. (16)
Single department (65),
Cross-departments
(17), cross-institutional
(7)
Arena (30); Simul8(5); FlexSim (5); AnyLogic (3);
NetLogo (2); Witness (2); ProModel (1); C++(1);
ProcessModel (1); Microsoft Excel (1); iThink (1);
AutoMod (1); SLX (2); EDSim (1); Matlab (1);
DGHPSim (1); ARIS (1); MedModel (3); OMNeT+
+ (1); Misc. (27)
These studies focus on modeling patient
pathways from admission to discharge as acting
the basis of direct intervention on patient flows.
Staffing decision
making
[24,35,37,42,46,49,55,
128202,248]
DES (50); SD (3);
ABS (3); gaming (1);
mixed (26), Misc.(5)
Single department (56),
cross-departments (22),
cross-institutional (10)
Arena (22); FlexSim (7); AnyLogic (5); Simul8(3);
MedModel (3); FDI (3); Matlab (2); ProModel
(2); Tecnomatix Plant Simulation (2); ARCINFO
(1); AutoMod (1); C++ (1); Petri Nets (1); Extend
(1); Microsoft Excel (1); Netlogo (1); OMNeT+
+(1); Venism (1); SLX (1); SIMPROCESS (1);
STELLA (1); ABFS (1); Java IDE (1); Misc. (27);
These studies use simulation for decision
support of care capacities.
Work procedures [203214] DES (8); Misc. (4) Single department (7),
cross-departments (5)
Arena (3); Simul8(2); ProModel (1); MedModel
(1); OMNeT++ (1); ExtendSim (1); Misc. (3)
Simulation is used to identify impact factors in
service procedures.
Specialized
Transport
[215222] DES (2); ABS (1);
Misc. (5)
Cross-institutional (8) Arena (2); ArcGIS (1); Google Cloud (1);
Microsoft Excel (1); Misc. (3)
These studies address handling of patients in the
regional healthcare network.
Facility design [223230] DES (4); ABS (2);
mixed (1); Misc. (1)
Single department (2),
cross-departments (1)
Unity (1); ProModel (1); NetLogo (1); Extend
(1); Misc. (4)
These studies use simulations to analyze hospital
infrastructure and its impact on the operation.
Healthcare systems [231235] DES (1); ABS (2);
Misc. (2)
Cross-institutional (5) Arena (2); Python (1); AnyLogic (1); NetLogo
(1)
These studies use simulations to support the
modeling and analysis of improvements in the
system perspective.
Supply chain [236243] DES (5), gaming (1);
Misc. (2)
Cross-departments (5),
cross-institutional (3)
ExtendSim (1); GAMS (1); Matlab (1); Bonita
Open Solution (1), Board game (1); Misc. (3)
The simulation model is generally used for
recreating different actors in the supply chain
network.
Inventory
management
[244258] DES (8); mixed (2);
Misc. (5)
Single department (3),
cross-departments (1),
cross-institutional (11)
Simul8 (2); Arena (2); C++(1); CSIM18 (1); Java
(1); JSL (1); SCA (1); Misc. (6)
These studies explore different inventory or
replacement polities for material handling.
Network
distribution and
dispatching
[32,259264] DES (4); gaming (2);
ABS (1); Misc. (3)
Cross-departments (2),
cross-institutional (8)
Microsoft Excel (2); Arena (1); MedModel (1);
ProModel (1); JADE (1); VBA (1); Misc. (3)
These studies use simulations for operational
transport.
Network
configuration
[265267] DES (1); Misc. (2) Cross-institutional (3) Arena (1); Misc. (2) These studies focus on the design of the
network.
Procurement
logistics
[27,268,269] SD (1); Misc. (2) Cross-department (1),
cross-institutional (2)
Qnet2000 (1); iThink (1); Misc. (1) Simulation is used for understanding the
interactive rule between service vendor and
recipient.
Misc. [31,36,38,270281] DES (4); SD (3); ABS
(1); gaming (1);
mixed (3); Misc. (4)
Single department (5),
cross-departments (4),
cross-institutional (7)
Arena (4); AnyLogic (2); iThink (2); Simul8 (1); NetLogo (1); Microsoft Excel (1); Powersim (1); Misc.
(4)
Methodology [1215,39,282286,287305]Reviews, surveys, and methodological reflections
and comparisons of logistics simulations in other
sectors.
Zhang et al. Advances in Simulation (2018) 3:15 Page 5 of 16
showed that the daily variation of used hospital bed cap-
acity could not be balanced in the long run by simply in-
creasing capacity; instead, optimal design of flows
should be the core of the operation technology [26].
One paper investigated logistical outsourcing [27] and
deployed system dynamics simulation with a sensitivity
analysis for the evaluation and analysis of sustainability
and economic performance. Content holders can use
system dynamics simulation to envisage the complexity
and identify opportunities and risks of the policies and
management controls proposed.
Agent-based simulation
Agent-based simulation could be considered a means of
soft computing in healthcare logistics. Agent-based
simulation provides a gateway for understanding the be-
havior of distributed and connected service providers.
The associated modeling and analysis are able to handle
engineering system problems in complex networks. As
an example, this paradigm was introduced to solve the
coordination and collaboration difficulty of caregivers in
a mental healthcare system [28]. The positive effect of
coordination technology was confirmed by such model-
ing considerations. The local decision rules of caregivers
were relevant for operative decision making with respect
to successful provision of home help, a conclusion
drawn from Marcon et al.s work [29]. Bidding decisions
made by distributers and suppliers in the pharmaceutical
industry were studied in Jetly et al.s work [30]. The per-
formance of a multi-site network was simulated with
pre-selected indicators, including the number of released
products, degree of consolidation, and the return on as-
sets. Multi-agent systems are not only effective for mod-
eling flows between providers; they could also be applied
in hospital environments. In Marin et al.s work, pa-
tients, nurses, doctors, and the department as the man-
ager are specified as agents with simple behavior rules
[31]. With the support of multi-agent languages, the
properties and relationships of actors could be simulated
and validated for a specific social-technical environment.
Game and participatory simulation
Games and participatory simulation are life-like media
that facilitate experimental learning. The use of such
media enables the development of non-technical team-
work skills. For instance, Mustafee and Katsaliaki devel-
oped a pedagogical business game that simulated the
blood supply chain [32,33]. The players were encour-
aged to propose different solutions, taking costs,
time-efficiency, and stock levels of products into ac-
count. Focusing on quality of service in healthcare, a
web-based organizational simulation was built and de-
ployed for training referral and diagnostic skills [34].
The results showed that the usefulness of information
on symptoms, diseases, and severity levels is associated
with the perception of information sources. A board
game provided valuable insight into the adoption of fu-
ture technology in hospital logistical work [15]. Regard-
ing the practical settings of two hospitals, the adoption
of wearable technologies was reflected through role play.
Such role playing could also be used to analyze the
working environment in wards [35].
Hybrid modeling
Hybrid modeling is the efficient combination of various
simulation modeling techniques described. Most hybrid
modeling involves coupling discrete-event simulation
and system dynamics. Two case studies, concerning in-
fection control and regional social care system engineer-
ing, were simulated using hybrid models [36]. Zulkepli
modeled an integrated ICU by combining system dy-
namics and discrete-event simulation [37]. The greatest
advantage of hybrid modeling is the ability to integrate
different simulation approaches and empirical data from
different sources [38].
Analysis of trends
Discrete-event simulation has been the most prominent
paradigm for modeling patient-centric logistics over the
past decade. Between 2008 and 2017, as presented in
Fig. 2, more than half of the included papers used
discrete-event simulation. However, the distribution of
simulation techniques among different periods showed
growing methodological diversity in recent years. The
presence of system dynamics is observed in all periods,
although the number of publications remained small.
Agent-based simulation, games, and hybrid modeling
were utilized only in the last decade. The specific simu-
lation paradigm used was not stated in some studies, es-
pecially between 1998 and 2007, during which the
majority of the methods used were classified as miscel-
laneous. Game-based methods were used in a few stud-
ies. Thus, interactive simulations are still quite new and
rarely used.
Fig. 2 Simulation paradigms for patient-centric logistics
Zhang et al. Advances in Simulation (2018) 3:15 Page 6 of 16
The single category was the predominant level ad-
dressed in all periods, as shown in Fig. 3. Between 2008
and 2012, the majority of studies addressed logistical is-
sues at the single-unit level. The systems perspective
was introduced between 2013 and 2017. Work address-
ing logistics issues at the cross-departmental and
cross-institutional levels formed half of all research ef-
forts. However, cross-institutional issues remained
largely underexplored in the literature compared with
other problems studied.
Discrete-event simulation was the most prominent
simulation paradigm in material-centric approaches as
well, as shown in Fig. 4. The research theme started to de-
velop in 2006, after which the number of publications and
the diversity of the utilized paradigms increased. Despite
the growth, six of 16 papers utilized discrete-event simula-
tion, and only three papers utilized system dynamics,
agent-based simulation, and/or hybrid modeling.
Compared with patient-centric logistics, material-centric
logistics was covered by a limited number of articles. Shah
et al. had already stressed the underexplored potential of
this area in 2004 [39]. We identified few publications on
this subject during this period. The period 20132017
showed the largest output, but the volume was still not able
to catch up with that of papers related to patient-centric
logistics.
As shown in Fig. 5, a growing number of papers have
analyzed material handling between multiple units. How-
ever, simulation design for cross- and single-department
logistics was lacking over the last 5 years, despite studies
reporting on the need for improving hospital internal sup-
ply chains to reduce costs [40].
Discussions
The literature review demonstrated that different simula-
tion techniques could be utilized for different educational
purposes, as summarized in Table 3. Discrete-event simula-
tion is suitable for operational problems, whereas strategic
issues are better explored by system dynamics.
Agent-based simulation stands out as a versatile tool
because that agent method is object-oriented and flexible
for describing the anatomy of complex systems formed by
multiple actors. Healthcare logistics is a complex
socio-technical system characterized by interconnected
components and non-rational operation management.
Agent-based simulation can explicitly model the interaction
between system components, facilitating the understanding
of overall performance under uncertainty and dynamics.
Games and participatory simulation are particularly
useful for training at the tactical level because games
help identify productive or counterproductive human
actions. The strength of the agent-based method is the
modeling and analysis of human behavior [33,41].
Healthcare logistics are largely characterized by
non-rational operative decision making by medical
personnel regarding needs of their patients. By model-
ing decisions at the agent level, it is possible to obtain
insight into the reasoning process of decisions being
made [42]. By involving these operational experts in
participatory simulations, we can assess their percep-
tion of processes and healthcare system operations [43].
This effort delivers insight at another level of abstrac-
tion than technical, often discrete-event-based, simula-
tions can provide.
Regarding training purposes, agent-based simulation
and games are suitable for training negotiation and
Fig. 3 Research scope for patient-centric logistics
Fig. 4 Simulation paradigms for material-centric logistics
Fig. 5 Research scope for material-centric logistics
Zhang et al. Advances in Simulation (2018) 3:15 Page 7 of 16
coordination in logistics, whereas discrete-event simula-
tion and system dynamics can be utilized for reducing
the uncertainty of decision-making processes by adding
details to the model.
Participatory simulation is valuable for validating vari-
ous simulators that model complex systems. The advan-
tage of participatory simulation corresponds to the
delivery structure of the investigated system, as healthcare
logistics is carried out by collaborative efforts in which dif-
ferent professionals, knowledge, and skills work together.
Discrete-event simulation has the lowest requirement of
technological preparation and is found to support all areas
[26,4446]. System dynamics and agent-based simulation
might require formal methods and mathematics pertain-
ing to system design, such as differential equations [47],
decision theories [48], and game-based approaches [33].
Conclusions
Complex socio-technical systems, such as air traffic con-
trols [49], routinely apply flow and logistical simulations.
The studies examined in this review indicate a growing
practice of implementing simulation in healthcare set-
tings to create situations in which the non-technical
skills of managers, coordinators, and decision makers
can be trained and developed. Building on existing con-
cepts from other industries [50], future applications
might be hands-on training of teams using gaming and
participatory simulation alongside empirical data to cre-
ate situations for training tricky decision making, for
strategic planning, and for exploring the effect of deci-
sions on other parts of the system [51].
Our review yielded many examples of applications in
healthcare, indicating that the issues of training of stra-
tegic or operational coordination and decision making in
healthcare can all be addressed by simulation. The orthog-
onal simulation techniques are discrete-event simulation,
system dynamics, agent-based simulation, game, and par-
ticipatory simulation. For patient-centric logistics,
discrete-event simulation in single-department/unit
scenarios is the most dominant form of simulation, the
maturity of which takes the lead over other categories by a
large margin. As a systems perspective was applied,
discrete-event simulation became less popular and was
compensated for by system dynamics or hybrid modeling.
The literature review showed that tools for logistical simu-
lations vary in this field, with tools such as AnyLogic,
Arena, NetLogo, and board games implemented most fre-
quently. This is an extensive study analyzing the growth in
the use of simulation in healthcare settings.
Lack of standardization
The number of miscellaneous simulations was signifi-
cant, although discrete-event simulation, system dynam-
ics, and agent-based simulation were well-established
and well-standardized simulation techniques in many
software packages. Most of the miscellaneous simula-
tions were custom-made solutions. A focal point of these
papers was implementing the modular design of proto-
cols, revealing a lack of standards. Compared with pro-
cesses in many other industries, healthcare processes are
less standardized, and thus, composition of services var-
ies. We believe that much effort could be saved by
employing standardization in both healthcare processes
and simulation formulism.
Lack of identification for material-centric logistics
In the domain of material-centric logistics, the focus is
on inventory management and network distribution. A
general lack of articles indicated limited research effort.
One reason is that material-centric logistics is not an in-
dependent research stream yetin many cases, the ana-
lysis of material-centric logistics is attached to a larger
research project pertaining to physical distribution and
logistical management.
Lack of complex system modeling and simulation
System dynamics, agent-based simulation, and hybrid
modeling were underdeveloped for handling the
Table 3 Guidelines for selecting a suitable logistical simulation model for training purposes
Discrete-event simulation System dynamics Agent-based simulation Game and participatory simulation
Level of training Operational Strategic All Tactical, operational
Training purpose Process management
and innovation
Planning Reasoning, negotiation, distributed
management
Experience, awareness, perception
Lower boundary of
technical preparation
Qualitative workflow Casual loop Objected-oriented programming Low-tech material
Higher boundary of
technical preparation
Differential equations Agent system High-tech graphic and interaction
Applicable area All Staffing decision making,
procurement logistics
Staffing decision making, transport,
hospital design, network distribution
and dispatching
Staffing decision making, supply
chain management, network
distribution and dispatching
Tools Arena, Simio, Simu8,
AnyLogic
Venism, AnyLogic NetLogo, AnyLogic Boards, unity
Zhang et al. Advances in Simulation (2018) 3:15 Page 8 of 16
complexity of social-technical systems. Digital transform-
ation would change many aspects of the
human-technology interaction in the provision of health
services. A knowledge gap exists between the promise of
future delivery of care that abolishes institutional bound-
aries and the current methods for testing and demonstrat-
ing functionalities. To bridge this gap, we require a better
understanding of interconnected relationships between
care providers and extensions to model individual-level
requirements.
Limitations
The review has limitations. The search terms were for-
mulated by the authors. As a result, the data search
might not have been comprehensive. To eliminate the
risk of omitting important contributions, the search
terms combined keywords related to content and scien-
tific methods, respectively. Second, although both jour-
nal and conference contributions were considered, the
exclusion of abstracts and posters might lead to publica-
tion bias according to the Assessment of Multiple Sys-
tematic Reviews (AMSTAR) checklist for assessing the
quality of systematic reviews. Because the aim of the re-
view was to identify logistical simulations for training
and education purposes, the exclusion is understandable
for the short contributions that are not able to docu-
ment the simulation models in a detailed manner.
Therefore, publication bias is not prevalent in our litera-
ture review. The review only analyzed papers published
since 1998. This approach was taken because the growth
in the use of healthcare simulations started as Jun et al.
surveyed the practical application of discrete-event
simulation in healthcare [12], which was noted by
Persson and Persson [52]. Therefore, a synthesis of the
literature after 1998 should not distort the analysis.
General conclusion
The overview demonstrates that the simulation models
available are mainly event-based, which is understand-
able. The strict regulations and rules associated with the
medical field make process simulation particularly suited
to handling issues in this area. These perceptions, to-
gether with the lack of literature on using agent-based
simulation and participatory simulation, suggest a re-
search direction involving the development of ontol-
ogies, architectures, and terminologies for their better
acceptance in training and education of non-technical
skills, with more problem-solving studies performed to
demonstrate the corresponding benefits.
It is worth noting that the growth of digitalized health-
care occurs in parallel with the demographic change into
an aging society. Currently, digital transformation,
provision of homecare, and de-institutionalization are
transferring practical applications into the decentralized
paradigm. This effort requires coordination between
caregivers and stakeholders. Agent-based simulation and
participatory simulation can support comprehensive en-
gineering to achieve quality and safety improvements.
Therefore, agent-based simulation and participatory
simulation are promising approaches for better handling
healthcare logistics given current societal trends.
Abbreviations
ABS: Agent-based simulation; DES: Discrete-event simulation;
Misc: Miscellaneous; Mixed: Hybrid simulation; SD: Systems dynamics
Funding
This work is supported by the Stockholm County Council, in the design of the
study and collection, analysis, interpretation of data, and writing the manuscript.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from
the corresponding author on reasonable request.
Authorscontributions
CZ, TG, and SM designed the study and located the articles from the databases.
CZ and TG performed the screening process. CZ, TG, SM, KH, and JH analyzed
and interpreted the systematic review results. CZ and TG were responsible for
writing the paper. KH, JH, and SM were the major contributors for revising the
manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
PublishersNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Author details
1
School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal
Institute of Technology, 2010, Röntgenvägen 1, 14152 Huddinge, Sweden.
2
School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal
Institute of Technology, Hälsovägen 11, 14152 Huddinge, Sweden.
3
Pediatric
Emergency Department, Karolinska University Hospital, Tomtebodavägen 18a,
17177 Stockholm, Sweden.
4
Department of Learning, Informatics, Management
and Ethics, Karolinska Institute, Tomtebodavägen 18a, 17177 Stockholm,
Sweden.
5
School of Industrial Engineering and Management, Royal Institute of
Technology, Mariekällgatan 3, 15144 Södertälje, Sweden.
Received: 6 March 2018 Accepted: 25 June 2018
References
1. Klein G, Feltovich PJ, Bradshaw JM, Woods DD. Common ground and
coordination in joint activity. In: Rouse WB, Boff KR, editors. Organizational
simulation. New Jersey: Wiley; 2005. p. 13984.
2. Vincent C, Amalberti R. Safety strategies in hospitals. In: Safer healthcare.
Cham: Springer International Publishing. p. 7391.
3. Nemeth CP, Nunnally M, OConnor MF, Brandwijk M, Kowalsky J, Cook RI.
Regularly irregular: how groups reconcile cross-cutting agendas and
demand in healthcare. Cogn Tech Work. 2007;9:13948.
4. Macrae C, Draycott T. Delivering high reliability in maternity care: in situ
simulation as a source of organisational resilience. Saf Sci. 2016. https://doi.
org/10.1016/j.ssci.2016.10.019.
5. Crichton M, OConnor P, Flin R. Safety at the sharp end: a guide to non-
technical skills. Hampshire: Ashgate Publishing, Ltd; 2013.
Zhang et al. Advances in Simulation (2018) 3:15 Page 9 of 16
6. Dieckmann P, Zeltner LG, Helsø A-M. Hand-it-on: an innovative simulation
on the relation of non-technical skills to healthcare. Adv Simul. 2016;1:30.
7. Ulmanen P, Szebehely M. From the state to the family or to the market?
Consequences of reduced residential eldercare in Sweden: from the state to
the family. Int J Soc Welf. 2015;24:8192.
8. Hagihara A, Hasegawa M, Hinohara Y, Abe T, Motoi M. The aging
population and future demand for emergency ambulances in Japan. Intern
Emerg Med. 2013;8:4317.
9. Kriz WC. Creating effective learning environments and learning
organizations through gaming simulation design. Simul Gaming. 2003;34:
495511.
10. Kriz WC. Types of gaming simulation applications. Simul Gaming. 2017;48:37.
11. Meijer S. The power of sponges: comparing high-tech and low-tech gaming
for innovation. Simul Gaming. 2015;46:51235.
12. Jun JB, Jacobson SH, Swisher JR. Application of discrete-event simulation in
health care clinics: a survey. J Oper Res Soc. 1999;50:10923.
13. Brailsford SC. System dynamics: whats in it for healthcare simulation
modelers. In: Proceedings of the winter simulation conference; 2008. p.
147883.
14. Koelling P, Schwandt MJ. Health systems: a dynamic system-benefits from
system dynamics. In: Proceedings of the winter simulation conference; 2005.
p. 13217.
15. Mustafee N, Katsaliaki K, Taylor SJE. Profiling literature in healthcare
simulation. Simulation. 2010;86:5438.
16. Schaefer JJ, Vanderbilt AA, Cason CL, Bauman EB, Glavin RJ, Lee FW, et al.
Literature review: instructional design and pedagogy science in healthcare
simulation. Simul Healthc. 2011;6:3041.
17. Nestel D, Groom J, Eikeland-Husebø S, OʼDonnell JM. Simulation for learning and
teaching procedural skills: the state of the science. Simul Healthc. 2011;6:103.
18. DeRienzo CM, Shaw RJ, Meanor P, Lada E, Ferranti J, Tanaka D. A discrete
event simulation tool to support and predict hospital and clinic staffing.
Health Informatics J. 2016;23:12433.
19. Devapriya P, Strömblad CTB, Bailey MD, Frazier S, Bulger J, Kemberling ST, et
al. StratBAM: a discrete-event simulation model to support strategic hospital
bed capacity decisions. J Med Syst. 2015;39:130.
20. Bhattacharjee P, Ray PK. Simulation modelling and analysis of appointment
system performance for multiple classes of patients in a hospital: a case
study. Oper Res Health Care. 2016;8:7184.
21. Vasilakis C, Sobolev BG, Kuramoto L, Levy AR. A simulation study of
scheduling clinic appointments in surgical care: individual surgeon versus
pooled lists. J Oper Res Soc. 2007;58:20211.
22. Jørgensen P, Jacobsen P, Poulsen JH. Identifying the potential of changes
to blood sample logistics using simulation. Scand J Clin Lab Invest. 2013;73:
27985.
23. Rashwan W, Ragab MA, Abo-Hamad W, Arisha A. Bed blockage in Irish
hospitals: system dynamics methodology. In: Proceedings of the winter
simulation conference; 2013. p. 39845.
24. Rashwan W, Abo-Hamad W, Arisha A. A system dynamics view of the acute
bed blockage problem in the Irish healthcare system. Eur J Oper Res. 2015;
247:27693.
25. Brailsford SC, Lattimer VA, Tarnaras P, Turnbull JC. Emergency and on-
demand health care: modelling a large complex system. J Oper Res Soc.
2004;55:3442.
26. Lane DC, Monefeldt C, Rosenhead JV. Looking in the wrong place for
healthcare improvements: a system dynamics study of an accident and
emergency department. J Oper Res Soc. 2000;51:51831.
27. Azzi A, Persona A, Sgarbossa F, Bonin M. Drug inventory management and
distribution: outsourcing logistics to third-party providers. Strateg
Outsourcing Int J. 2013;6:4864.
28. Kalton A, Falconer E, Docherty J, Alevras D, Brann D, Johnson K. Multi-agent-
based simulation of a complex ecosystem of mental health care. J Med
Syst. 2016;40:39.
29. Marcon E, Chaabane S, Sallez Y, Bonte T, Trentesaux D. A multi-agent
system based on reactive decision rules for solving the caregiver routing
problem in home health care. Simul Model Pract Theory. 2017;74:13451.
30. Jetly G, Rossetti CL, Handfield R. A multi-agent simulation of the
pharmaceutical supply chain. J Simul. 2012;6:21526.
31. Escudero-Marin P, Pidd M. Using ABMS to simulate emergency departments.
In: Proceedings of the winter simulation conference; 2011. p. 123950.
32. Mustafee N, Katsaliaki K. The blood supply game. In: Proceedings of the
winter simulation conference; 2010. p. 32738.
33. Katsaliaki K, Mustafee N, Kumar S. A game-based approach towards
facilitating decision making for perishable products: an example of blood
supply chain. Expert Syst Appl. 2014;41:404359.
34. Basole RC, Bodner DA, Rouse WB. Healthcare management through
organizational simulation. Decis Support Syst. 2013;55:55263.
35. Mattarelli E, Fadel KJ, Weisband SP. Design of a role-playing game to study the
trajectories of health care workers in an operating room. In: Proceedings of
conference on human factors in computing systems; 2006. p. 10916.
36. Brailsford SC, Desai SM, Viana J. Towards the holy grail: combining system
dynamics and discrete-event simulation in healthcare. In: Proceedings of
the winter simulation conference; 2010. p. 2293303.
37. Zulkepli J, Eldabi T, Mustafee N. Hybrid simulation for modelling large
systems: an example of integrated care model. In: Proceedings of the winter
simulation conference; 2012.
38. zen MB, Zabawa J. Modeling healthcare demand using a hybrid simulation
approach. In: Proceedings of the winter simulation conference; 2016. p.
153546.
39. Shah N. Pharmaceutical supply chains: key issues and strategies for
optimisation. Comput Chem Eng. 2004;28:92941.
40. Gaba DM. Adapting space science methods for describing and planning
research in simulation in healthcare: science traceability and decadal
surveys. Simul Healthc. 2012;7:2731.
41. Liu Z, Rexachs D, Epelde F, Luque E. An agent-based model for
quantitatively analyzing and predicting the complex behavior of emergency
departments. J Comput Sci. 2017;21(Suppl C):1123.
42. Kolb EMW, Schoening S, Peck J, Lee T. Reducing emergency department
overcrowding: five patient buffer concepts in comparison. In: Proceedings
of the winter simulation conference; 2008. p. 151625.
43. Kotiadis K, Tako AA, Vasilakis C. A participative and facilitative conceptual
modelling framework for discrete event simulation studies in healthcare. J
Oper Res Soc. 2014;65:197213.
44. Demir E, Gunal MM, Southern D. Demand and capacity modelling for acute
services using discrete event simulation. Health Syst. 2017;6:3340.
45. Reynolds M, Vasilakis C, McLeod M, Barber N, Mounsey A, Newton S, et al.
Using discrete event simulation to design a more efficient hospital
pharmacy for outpatients. Health Care Manag Sci. 2011;14:22336.
46. Pehlivan C, Augusto V, Xie X. Admission control in a pure loss healthcare
network: MDP and DES approach. In: Proceedings of the winter simulation
conference; 2013. p. 5465.
47. Medeiros DJ, Swenson E, DeFlitch C. Improving patient flow in a hospital
emergency department. In: Proceedings of the winter simulation
conference; 2008. p. 152631.
48. Ferrin DM, Miller MJ, McBroom DL. Maximizing hospital finanacial impact
and emergency department throughput with simulation. In: Proceedings of
the winter simulation conference; 2007. p. 156673.
49. Dehlendorff C, Kulahci M, Andersen KK. Designing simulation experiments
with controllable and uncontrollable factors for applications in healthcare.
Appl Stat. 2011;60:3149.
50. Duguay C, Chetouane F. Modeling and improving emergency department
systems using discrete event simulation. Simulation. 2007;83:31120.
51. Kittipittayakorn C, Ying K-C. Using the integration of discrete event and
agent-based simulation to enhance outpatient service quality in an
orthopedic department. J Healthc Eng. 2016;2016.
52. Persson MJ, Persson JA. Analysing management policies for operating room
planning using simulation. Health Care Manag Sci. 2010;13:18291.
53. Guo M, Wagner M, West C. Outpatient clinic schedulinga simulation
approach. In: Proceedings of the winter simulation conference; 2004.
p. 19817.
54. Wijewickrama A, Takakuwa S. Simulation analysis of appointment
scheduling in an outpatient department of internal medicine. In:
Proceedings of the winter simulation conference; 2005. p. 226473.
55. Johansson B, Jain S, Montoya-Torres J, Hngan J. Integrating balanced
scorecard and simulation modeling to improve emergency department
performance in Irish hospitals. In: Proceedings of the winter simulation
conference; 2010. p. 234051.
56. Lin Y, Zhang J. An ant colony optimization approach for efficient admission
scheduling of elective inpatients. In: Proceedings of the annual conference
on genetic and evolutionary computation; 2011. p. 156.
57. Helm JE, AhmadBeygi S, Van Oyen MP. Design and analysis of hospital
admission control for operational effectiveness. Prod Oper Manag. 2011;20:
35974.
Zhang et al. Advances in Simulation (2018) 3:15 Page 10 of 16
58. Yokouchi M, Aoki S, Sang H, Zhao R, Takakuwa S. Operations analysis and
appointment scheduling for an outpatient chemotherapy department. In:
Proceedings of the winter simulation conference; 2012.
59. Khurma N, Salamati F, Pasek ZJ. Simulation of patient discharge process and
its improvement. In: Proceedings of the winter simulation conference; 2013.
p. 245262.
60. Kooij R, Mes MRK, Hans EW. Simulation framework to analyze operating
room release mechanisms. In: Proceedings of the winter simulation
conference; 2014. p. 114455.
61. Huggins A, Claudio D, Waliullah M. A detailed simulation model of an
infusion treatment center. In: Proceedings of the winter simulation
conference; 2014. p. 1198209.
62. Prodel M, Augusto V, Xie X. Hospitalization admission control of emergency
patients using markovian decision processes and discrete event simulation.
In: Proceedings of the winter simulation conference; 2014. p. 143344.
63. Giesen E, Ketter W, Zuidwijk R. Dynamic agent-based scheduling of
treatments: evidence from the Dutch youth health care sector. In:
Müller JP, Ketter W, Kaminka G, Wagner G, Bulling N, editors. Multiagent
system technologies. Cham: Springer International Publishing; 2015.
p. 17399.
64. Chen Y, Kuo YH, Balasubramanian H, Wen C. Using simulation to examine
appointment overbooking schemes for a medical imaging center. In:
Proceedings of the winter simulation conference; 2015. p. 130718.
65. Crowe S, Vasilakis C, Gallivan S, Bull C, Fenton M. Informing the
management of pediatric heart transplant waiting lists: complementary use
of simulation and analytical modeling. In: Proceedings of the winter
simulation conference; 2015. p. 165465.
66. Chen P-S, Robielos RAC, Palaña PKVC, Valencia PLL, GY-H C. Scheduling
patientsappointments: allocation of healthcare service using simulation
optimization. J Healthc Eng. 2015;6:25980.
67. Kim S-H, Chan CW, Olivares M, Escobar G. ICU admission control: an
empirical study of capacity allocation and its implication for patient
outcomes. Manag Sci. 2015;61:1938.
68. Khanna S, Boyle J, Good N, Bell A, Lind J. Analysing the emergency
department patient journey: discovery of bottlenecks to emergency
department patient flow. Emerg Med Australas. 2017;29:1823.
69. Kim BBJ, Delbridge TR, Kendrick DB. Adjusting patients streaming initiated
by a wait time threshold in emergency department for minimizing
opportunity cost. Int J Health Care Qual Assur. 2017;30:51627.
70. Ozcan YA, Tànfani E, Testi A. Improving the performance of surgery-based
clinical pathways: a simulation-optimization approach. Health Care Manag
Sci. 2017;20:115.
71. Bozzetto M, Rota S, Vigo V, Casucci F, Lomonte C, Morale W, et al. Clinical
use of computational modeling for surgical planning of arteriovenous fistula
for hemodialysis. BMC Med Inform Decis Mak. 2017;17:26.
72. Vile JL, Allkins E, Frankish J, Garland S, Mizen P, Williams JE. Modelling
patient flow in an emergency department to better understand demand
management strategies. J Simul. 2017;11:11527.
73. Demir E, Southern D. Enabling better management of patients: discrete
event simulation combined with the STAR approach. J Oper Res Soc. 2017;
68:57790.
74. Chepenik L, Pinker E. The impact of increasing staff resources on patient
flow in a psychiatric emergency service. Psychiatr Serv. 2017;68:4705.
75. Saltzman R, Roeder T, Lambton J, Param L, Frost B, Fernandes R. The impact
of a discharge holding area on the throughput of a pediatric unit. Serv Sci.
2017;9:12135.
76. Toth DJA, Khader K, Slayton RB, Kallen AJ, Gundlapalli AV, OHagan JJ, et al.
The potential for interventions in a long-term acute care hospital to reduce
transmission of carbapenem-resistant enterobacteriaceae in affiliated
healthcare facilities. Clin Infect Dis. 2017;65:5817.
77. Steward D, Glass TF, Ferrand YB. Simulation-based design of ed operations
with care streams to optimize care delivery and reduce length of stay in the
emergency department. J Med Syst. 2017;41:162.
78. Tako AA, Kotiadis K. PartiSim: a multi-methodology framework to
support facilitated simulation modelling in healthcare. Eur J Oper Res.
2015;244:55564.
79. Monks T, Pearn K, Allen M. Simulation of stroke care systems. In:
Proceedings of the winter simulation conference; 2015. p. 1391402.
80. Djanatliev A, Meier F. Hospital processes within an integrated system view:
a hybrid simulation approach. In: Proceedings of the winter simulation
conference; 2016. p. 136475.
81. Alahäivälä T, Oinas-Kukkonen H. Understanding persuasion contexts in
health gamification: a systematic analysis of gamified health behavior
change support systems literature. Int J Med Inform. 2016;96:6270.
82. Zhong X, Lee HK, Li J. From production systems to health care delivery
systems: a retrospective look on similarities, difficulties and opportunities. Int
J Prod Res. 2017;55:421227.
83. Lim ME, Worster A, Goeree R, Tarride J-É. Simulating an emergency
department: the importance of modeling the interactions between
physicians and delegates in a discrete event simulation. BMC Med Inform
Decis Mak. 2013;13:59.
84. Vanderby S, Carter MW. An evaluation of the applicability of system
dynamics to patient flow modelling. J Oper Res Soc. 2010;61:157281.
85. Chockalingam A, Jayakumar K, Lawley MA. A stochastic control approach to
avoiding emergency department overcrowding. In: Proceedings of the
winter simulation conference; 2010. p. 2399411.
86. Djanatliev A, German R. Prospective healthcare decision-making by
combined system dynamics, discrete-event and agent-based simulation. In:
Proceedings of the winter simulation conference; 2013. p. 27081.
87. Wolstenholme E. A patient flow perspective of U.K. health services: exploring
the case for new intermediate careinitiatives. Syst Dyn Rev. 1999;15:25371.
88. Jansen FJA, Etman LFP, Rooda JE, Adan IJBF. Aggregate simulation
modeling of an MRI department using effective process times. In:
Proceedings of the winter simulation conference; 2012.
89. Zambrano F, Concha P, Ramis F, Neriz L, Bull M, Veloz P, et al. Improving
patient access to a public hospital complex using agent simulation. In:
Proceedings of the winter simulation conference; 2016. p. 127788.
90. Lamé G, Jouini O, Cardinal JS-L, Carvalho M, Tournigand C, Wolkenstein P.
Patient-hospital communication: a platform to improve outpatient
chemotherapy. In: Proceedings of the winter simulation conference; 2016. p.
2099110.
91. April J, Better M, Glover F, Kelly J, Laguna M. Enhancing business process
management with simulation optimization. In: Proceedings of the winter
simulation conference; 2006. p. 6429.
92. Bowers J, Ghattas M, Mould G. Exploring alternative routes to realising the
benefits of simulation in healthcare. J Oper Res Soc. 2012;63:145766.
93. Chavis J, Cochran AL, Kocher KE, Washington VN, Zayas-Cabán G. A
simulation model of patient flow through the emergency department to
determine the impact of a short stay unit on hospital congestion. In:
Proceedings of the winter simulation conference; 2016. p. 198293.
94. Patvivatsiri L. A simulation model for bioterrorism preparedness in an
emergency room. In: Proceedings of the winter simulation conference;
2006. p. 5018.
95. Comas M, Castells X, Hoffmeister L, Román R, Cots F, Mar J, et al. Discrete-
event simulation applied to analysis of waiting lists: evaluation of a
prioritization system for cataract surgery. Value Health. 2008;11:120313.
96. Kuhl ME. A simulation study of patient flow for day of surgery admission. In:
Proceedings of the winter simulation conference; 2012.
97. Miller M, Ferrin D, Shahi N. Estimating patient surge impact on boarding
time in several regional emergency departments. In: Proceedings of the
winter simulation conference; 2009. p. 190615.
98. Roure M, Halley Q, Augusto V. Modelling and simulation of an outpatient
surgery unit. In: Proceedings of the winter simulation conference; 2015.
p. 152536.
99. Doğan NÖ, Unutulmaz O. Lean production in healthcare: a simulation-based
value stream mapping in the physical therapy and rehabilitation department
of a public hospital. Total Qual Manag Bus Excell. 2016;27:6480.
100. Batarseh OG, Goldlust EJ, Day TE. SysML for conceptual modeling and
simulation for analysis: a case example of a highly granular model of an
emergency department. In: Proceedings of the winter simulations
conference; 2013. p. 2398409.
101. Schonherr O, Rose O. A general model description for discrete processes. In:
Proceedings of the winter simulation conference; 2011. p. 220113.
102. Southard PB, Chandra C, Kumar S. RFID in healthcare: a six sigma DMAIC
and simulation case study. Int J Health Care Qual Assur. 2012;25:291321.
103. Santibáñez P, Chow VS, French J, Puterman ML, Tyldesley S. Reducing
patient wait times and improving resource utilization at British Columbia
Cancer Agencys ambulatory care unit through simulation. Health Care
Manag Sci. 2009;12:392407.
104. Konrad RA, Lawley MA. Input modeling for hospital simulation models
using electronic messages. In: Proceedings of the winter simulation
conference; 2009. p. 13447.
Zhang et al. Advances in Simulation (2018) 3:15 Page 11 of 16
105. Hagtvedt R, Ferguson M, Griffin P, Jones GT, Keskinocak P. Cooperative
strategies to reduce ambulance diversion. In: Proceedings of the winter
simulation conference; 2009. p. 186174.
106. Maull RS, Smart PA, Harris A, AA-F K. An evaluation of fast trackin A&E: a
discrete event simulation approach. Serv Ind J. 2009;29:92341.
107. Roberts SD. Tutorial on the simulation of healthcare systems. In:
Proceedings of the winter simulation conference; 2011. p. 140819.
108. Hosseini S, Jannat S. Discrete event simulation technique for evaluating
performance of oncology department: a case study. In: Proceedings of the
winter simulation conference; 2015. p. 134354.
109. Levin S, Garifullin M. Simulating wait time in healthcare: accounting for
transition process variability using survival analyses. In: Proceedings of the
winter simulation conference. 2015. p. 125260.
110. Levin S, Dittus R, Aronsky D, Weinger M, France D. Evaluating the effects of
increasing surgical volume on emergency department patient access. Qual
Saf Health Care. 2011;20:14652.
111. Mahapatra S, Koelling CP, Patvivatsiri L, Fraticelli B, Eitel D, Grove L.
Pairing emergency severity index5-level triage data with computer
aided system design to improve emergency department access and
throughput. In: Proceedings of the winter simulation conference;
2003. p. 191725.
112. McClean S, Barton M, Garg L, Fullerton K. A modeling framework that
combines Markov models and discrete-event simulation for stroke patient
care. ACM Trans Model Comput Simul. 2011;21:25.
113. Reindl S, Mönch L, Mönch M, Scheider A. Modeling and simulation of
cataract surgery processes. In: Proceedings of the winter simulation
conference; 2009. p. 193745.
114. Takakuwa S, Wijewickrama A. Optimizing staffing schedule in light of
patient satisfaction for the whole outpatient hospital ward. In: Proceedings
of the winter simulation conference; 2008. p. 15008.
115. Takakuwa S, Katagiri D. Modeling of patient flows in a large-scale outpatient
hospital ward by making use of electronic medical records. In: Proceedings
of the winter simulation conference; 2007. p. 152331.
116. Takakuwa S, Shiozaki H. Functional analysis for operating emergency
department of a general hospital. In: Proceedings of the winter simulation
conference; 2004. p. 200311.
117. Coelli FC, Ferreira RB, Almeida RMVR, Pereira WCA. Computer simulation
and discrete-event models in the analysis of a mammography clinic patient
flow. Comput Methods Prog Biomed. 2007;87:2017.
118. Rohleder TR, Lewkonia P, Bischak DP, Duffy P, Hendijani R. Using simulation
modeling to improve patient flow at an outpatient orthopedic clinic. Health
Care Manag Sci. 2011;14:13545.
119. Wang T, Guinet A, Belaidi A, Besombes B. Modelling and simulation of
emergency services with ARIS and Arena. Case study: the emergency
department of Saint Joseph and Saint Luc Hospital. Prod Plan Control. 2009;
20:48495.
120. Abo-Hamad W, Arisha A. Multi-criteria framework for emergency
department in Irish hospital. In: Proceedings of the winter simulation
conference; 2012.
121. Abo-Hamad W, Arisha A. Simulation-based framework to improve patient
experience in an emergency department. Eur J Oper Res. 2013;224:15466.
122. Wang Y, Lee LH, Chew EP, Lam SSW, Low SK, Ong MEH, et al. Multi-
objective optimization for a hospital inpatient flow process via discrete
event simulation. In: Proceedings of the winter simulation conference; 2015.
p. 362231.
123. Zhao Y, Peng Q, Strome T, Weldon E, Zhang M, Chochinov A. Bottleneck
detection for improvement of emergency department efficiency. Bus
Process Manag J. 2015;21:56485.
124. Rashwan W, Habib H, Arisha A, Courtney G, Kennelly S. An integrated
approach of multi-objective optimization model for evaluating new
supporting program in Irish hospitals. In: Proceedings of the winter
simulation conference; 2016. p. 190415.
125. Rashwan W, Arisha A. Modeling behavior of nurses in clinical medical unit
in university hospital: burnout implications. In: Proceedings of the winter
simulation conference; 2015. p. 388091.
126. Bair AE, Song WT, Chen Y, Morris BA. The impact of inpatient boarding on
emergency department crowding: a discrete-event simulation study. In:
Proceedings of the spring computer simulation conference; 2009.
127. Bountourelis T, Eckman D, Luangkesorn L, Schaefer A, Nabors SG, Clermont
G. Sensitivity analysis of an ICU simulation model. In: Proceedings of the
winter simulation conference; 2012.
128. Hamrock E, Paige K, Parks J, Scheulen J, Levin S. Discrete event simulation
for healthcare organizations: a tool for decision making. J Healthc Manag.
2013;58:11024.
129. Centeno MA, Albacete C, Terzano DO, Carrillo M, Ogazon T. Project and
process improvements in healthcare organizations: a simulation study of
the radiology department at JMH. In: Proceedings of the winter simulation
conference; 2000. p. 197884.
130. Ramis FJ, Palma JL, Baesler FF. The use of simulation for process
improvement at an ambulatory surgery center. In: Proceedings of the
winter simulation conference; 2001. p. 14014.
131. Pasin F, Jobin MH, Cordeau JF. An application of simulation to analyse
resource sharing organisations among health-care organisations. Int J Oper
Prod Manag. 2002;22:38193.
132. Wiinamaki A, Dronzek R. Using simulation in the architectural concept
phase of an emergency department design. In: Proceedings of the winter
simulation conference; 2003. p. 19126.
133. Samaha S, Armel WS, Starks DW. The use of simulation to reduce the length
of stay in an emergency department. In: Proceedings of the winter
simulation conference; 2003. p. 190711.
134. Blasak RE, Starks DW, Armel WS, Hayduk MC. Healthcare process analysis:
the use of simulation to evaluate hospital operations between the
emergency department and a medical telemetry unit. In: Proceedings of
the winter simulation conference; 2003. p. 188793.
135. Baesler FF, Jahnsen HE, DaCosta M. The use of simulation and design
of experiments for estimating maximum capacity in an emergency
room. In: Proceedings of the winter simulation conference; 2003.
p. 19036.
136. Centeno MA, Giachetti R, Linn R, Ismail AM. A simulation-ilp based tool for
scheduling ER staff. In: Proceedings of the winter simulation conference;
2003. p. 19308.
137. Schenk JR, Huang D, Zheng N, Allen TT. Multiple fidelity simulation
optimization of hospital performance under high consequence event
scenarios. In: Proceedings of the winter simulation conference; 2005.
p. 93642.
138. Spry CW, Lawley MA. Evaluating hospital pharmacy staffing and work
scheduling using simulation. In: Proceedings of the winter simulation
conference; 2005. p. 225663.
139. Hay AM, Valentin EC, Bijlsma RA. Modeling emergency care in hospitals:
a paradox-the patient should not drive the process. In: Proceedings of the
winter simulation conference; 2006. p. 43945.
140. Wijewickrama AKA, Takakuwa S. Simulation analysis of an outpatient
department of internal medicine in a university hospital. In: Proceedings of
the winter simulation conference; 2006. p. 42532.
141. Ballard SM, Kuhl ME. The use of simulation to determine maximum capacity
in the surgical suite operating room. In: Proceedings of the winter
simulation conference; 2006. p. 4338.
142. Taaffe K, Johnson M, Steinmann D. Improving hospital evacuation planning
using simulation. In: Proceedings of the winter simulation conference; 2006.
p. 50915.
143. VanBerkel PT, Blake JT. A comprehensive simulation for wait time reduction
and capacity planning applied in general surgery. Health Care Manag Sci.
2007;10:37385.
144. Rico F, Salari E, Centeno G. Emergency departments nurse allocation to face
a pandemic influenza outbreak. In: Proceedings of the winter simulation
conference; 2007. p. 12928.
145. Miller M, Ferrin D, Ashby M, Flynn T, Shahi N. Merging six emergency
departments into one: a simulation approach. In: Proceedings of the winter
simulation conference; 2007. p. 15748.
146. Patvivatsiri L, Montes EJ Jr, Xi O. Modeling bioterrorism preparedness with
simulation in rural healthcare system. In: Proceedings of the winter
simulation conference; 2007. p. 115560.
147. Paul JA, Hariharan G. Hospital capacity planning for efficient disaster
mitigation during a bioterrorist attack. In: Proceedings of the winter
simulation conference; 2007. p. 113947.
148. Song WT, Bair AE, Chih M. A simulation study on the impact of physician
starting time in a physical examination service. In: Proceedings of the winter
simulation conference; 2008. p. 155362.
149. Protil RM, Stroparo JR, Bichinho GL. Applying computer simulation to
increase the surgical center occupation rate at a university hospital in
Curitiba-Brazil. In: Proceedings of the winter simulation conference; 2008.
p. 160916.
Zhang et al. Advances in Simulation (2018) 3:15 Page 12 of 16
150. Nielsen AL, Hilwig H, Kissoon N, Teelucksingh S. Discrete event simulation as
a tool in optimization of a professional complex adaptive system. Stud
Health Technol Inform. 2008;136:247.
151. Huschka TR, Denton BT, Narr BJ, Thompson AC. Using simulation in the
implementation of an outpatient procedure center. In: Proceedings of the
winter simulation conference; 2008. p. 154752.
152. Oddoye JP, Jones DF, Tamiz M, Schmidt P. Combining simulation and goal
programming for healthcare planning in a medical assessment unit. Eur J
Oper Res. 2009;193:25061.
153. Raunak M, Osterweil L, Wise A, Clarke L, Henneman P. Simulating patient
flow through an emergency department using process-driven discrete
event simulation. In: Proceedings of the international conference on
software engineering; 2009. p. 7383.
154. Efe K, Raghavan V, Choubey S. Simulation modeling movable hospital assets
managed with RFID sensors. In: Proceedings of the winter simulation
conference; 2009. p. 205464.
155. Ferrand Y, Magazine M, Rao U. Comparing two operating-room-allocation
policies for elective and emergency surgeries. In: Proceedings of the winter
simulation conference; 2010. p. 236474.
156. Zeltyn S, Marmor YN, Mandelbaum A, Carmeli B, Greenshpan O,
Mesika Y, et al. Simulation-based models of emergency departments:
operational, tactical, and strategic staffing. ACM Trans Model Comput
Simul. 2011;21:24.
157. Weng S-J, Tsai B-S, Wang L-M, Chang C-Y, Gotcher D. Using simulation and
data envelopment analysis in optimal healthcare efficiency allocations. In:
Proceedings of the winter simulation conference; 2011. p. 1295305.
158. Geis GL, Pio B, Pendergrass TL, Moyer MR, Patterson MD. Simulation to
assess the safety of new healthcare teams and new facilities. Simul Healthc.
2011;6:125.
159. Cabrera E, Luque E, Taboada M, Epelde F, Iglesias ML. ABMS optimization
for emergency departments. In: Proceedings of the winter simulation
conference; 2012.
160. Kuo Y-H, Leung JM, Graham CA. Simulation with data scarcity: developing a
simulation model of a hospital emergency department. In: Proceedings of
the winter simulation conference; 2012.
161. Mustafee N, Lyons T, Rees P, Davies L, Ramsey M, Williams MD. Planning of
bed capacities in specialized and integrated care units: incorporating bed
blockers in a simulation of surgical throughput. In: Proceedings of the
winter simulation conference; 2012.
162. Rashwan W, Ragab M, Abo-Hamad W, Arisha A. Evaluating policy
interventions for delayed discharge: a system dynamics approach. In:
Proceedings of the winter simulation conference; 2013. p. 246374.
163. Shin SY, Balasubramanian H, Brun Y, Henneman PL, Osterweil LJ. Resource
scheduling through resource-aware simulation of emergency departments.
In: Proceedings of the international workshop on software engineering in
health care; 2013. p. 6470.
164. Amyot D. Real-time simulations to support operational decision making in
healthcare. In: Proceedings of the summer computer simulation conference;
2013. p. 6470.
165. Verma S, Gupta A. Improving services in outdoor patient departments by
focusing on process parameters: a simulation approach. In: Proceedings of
the winter simulation conference; 2013. p. 225061.
166. Yaylali E, Simmons J, Taheri J. Systems engineering methods for enhancing
the value stream in public health preparedness: the role of Markov models,
simulation, and optimization. Public Health Rep. 2014;129:14553.
167. Pinto LR, Perpétuo IHO, de Campos FCC, Ribeiro YCNMB. Analysis of
hospital bed capacity via queuing theory and simulation. In: Proceedings of
the winter simulation conference; 2014. p. 128192.
168. Ozen A, Balasubramanian H, Samra P, Ehresman M, Li H, Fairman T, et al.
The impact of hourly discharge rates and prioritization on timely access to
inpatient beds. In: Proceedings of the winter simulation conference; 2014.
p. 121020.
169. Kalbasi A, Krishnamurthy D, Rolia J, Singhal S. Simulation by example for
complex systems. In: Proceedings of the winter simulation conference; 2014.
p. 97485.
170. Wurzer G, Lorenz WE. Causality in hospital simulation based on utilization
chains. In: Proceedings of the symposium on simulation for architecture
and urban design; 2014.
171. Aboueljinane L, Sahin E, Jemai Z, Marty J. A simulation study to improve the
performance of an emergency medical service: application to the French
Val-de-Marne department. Simul Model Pract Theory. 2014;47:4659.
172. Ghanes K, Jouini O, Jemai Z, Wargon M, Hellmann R, Thomas V, et al. A
comprehensive simulation modeling of an emergency department: a case
study for simulation optimization of staffing levels. In: Proceedings of the
winter simulation conference; 2014. p. 142132.
173. Zhou Z, Wang Y, Li L. Process mining based modeling and analysis of
workflows in clinical carea case study in a Chicago outpatient clinic. In:
Proceedings of the IEEE international conference on networking, sensing
and control; 2014. p. 5905.
174. van Buuren M, Kommer GJ, van der Mei R, Bhulai S. A simulation model for
emergency medical services call centers. In: Proceedings of the winter
simulation conference; 2015. p. 84455.
175. Franck T, Augusto V, Xie X, Gonthier R, Achour E. Performance evaluation of
an integrated care for geriatric departments using discrete-event simulation.
In: Proceedings of the winter simulation conference; 2015. p. 133142.
176. Ghanes K, Wargon M, Jouini O, Jemai Z, Diakogiannis A, Hellmann R, et al.
Simulation-based optimization of staffing levels in an emergency
department. Simulation. 2015;91:94253.
177. Carmen R, Defraeye M, Van Nieuwenhuyse I. A decision support system for
capacity planning in emergency departments. Int J Simul Model. 2015;14:299312.
178. dos Santos M, Quintal RS, da PAC, Gomes CFS. Simulation of operation of
an integrated information for emergency pre-hospital care in Rio de Janeiro
municipality. Procedia Comput Sci. 2015;55:9318.
179. Agor J, McKenzie K, Ozaltin O, Mayorga M, Parikh RS, Huddleston J.
Simulation of triaging patients into an internal medicine department to
validate the use of an optimization based workload score. In: Proceedings
of the winter simulation conference; 2016. p. 37089.
180. Lee W, Shin K, Lee H-R, Shin H, Lee T. A structured approach for
constructing high fidelity ED simulation. In: Proceedings of the winter
simulation conference; 2016. p. 195060.
181. Pujowidianto NA, Lee LH, Pedrielli G, Chen C-H, Li H. Constrained
optimizaton for hospital bed allocation via discrete event simulation with
nested partitions. In: Proceedings of the winter simulation conference; 2016.
p. 191625.
182. Augusto V, Xie X, Prodel M, Jouaneton B, Lamarsalle L. Evaluation of
discovered clinical pathways using process mining and joint agent-based
discrete-event simulation. In: Proceedings of the winter simulation
conference; 2016. p. 213546.
183. Tiwari V, Sandberg WS. Perioperative bed capacity planning guided by
theory of constraints. In: Proceedings of the winter simulation conference;
2016. p. 1894903.
184. Thorwarth M, Rashwan W, Arisha A. An analytical representation of flexible
resource allocation in hospitals. Flex Serv Manuf J. 2016;28:14865.
185. Kuo Y-H, Rado O, Lupia B, Leung JMY, Graham CA. Improving the efficiency
of a hospital emergency department: a simulation study with indirectly
imputed service-time distributions. Flex Serv Manuf J. 2016;28:12047.
186. KadıD, Kuvvetli Y, Çolak S. Performance analysis of a university hospital
blood laboratory via discrete event simulation. Simulation. 2016;92:47384.
187. Cimellaro GP, Piqué M. Resilience of a hospital emergency department
under seismic event. Adv Struct Eng. 2016;19:82536.
188. Zhong X, Lee HK, Williams M, Kraft S, Sleeth J, Welnick R, et al. Staffing ratio
analysis in primary care redesign: a simulation approach. In: Matta A, Sahin
E, Li J, Guinet A, Vandaele NJ, editors. Health care systems engineering for
scientists and practitioners. New York: Springer; 2016. p. 13344.
189. Lambton J, Roeder T, Saltzman R, Param L, Fernandes R. Using simulation to
model improvements in pediatric bed placement in an acute care hospital.
Jona J Nurs Adm. 2017;47:8893.
190. Bakker M, Tsui K-L. Dynamic resource allocation for efficient patient
scheduling: a data-driven approach. J Syst Sci Syst Eng. 2017;26:44862.
191. Weng S-J, Xu Y-Y, Gotcher D, Wang L-M. A pilot study of available bed
forecasting system (ABFS) in the emergency healthcare network. In:
Proceedings of the summer computer simulation conference; 2017.
192. Tànfani E, Testi A. Improving surgery department performance via
simulation and optimization. In: Proceedings of the IEEE workshop on
health care management; 2010.
193. Friemann F, Schönsleben P. Reducing global supply chain risk exposure of
pharmaceutical companies by further incorporating warehouse capacity
planning into the strategic supply chain planning process. J Pharm Innov.
2016;11:16276.
194. Ramis FJ, Baesler F, Berho E, Neriz L, Sepulveda JA. A simulator to improve
waiting times at a medical imaging center. In: Proceedings of the winter
simulation conference; 2008. p. 15727.
Zhang et al. Advances in Simulation (2018) 3:15 Page 13 of 16
195. Zeinali F, Mahootchi M, Sepehri MM. Resource planning in the emergency
departments: a simulation-based metamodeling approach. Simul Model
Pract Theory. 2015;53:12338.
196. Wang J, Zhong X, Li J, Howard PK. Modeling and analysis of care delivery
services within patient rooms: a system-theoretic approach. IEEE Trans
Autom Sci Eng. 2014;11:37993.
197. Luangkesorn KL, Bountourelis T, Schaefer A, Nabors S, Clermont G. The case
against utilization: deceptive performance measures in inpatient care
capacity models. In: Proceedings of the winter simulation conference; 2012.
198. Holm LB, Dahl FA. Simulating the influence of a 45% increase in patient
volume on the emergency department of Akershus University Hospital. In:
Proceedings of the winter simulation conference; 2010. p. 245561.
199. Miller MJ, Ferrin DM, Szymanski JM. Simulating six sigma improvement
ideas for a hospital emergency department. In: Proceedings of the winter
simulation conference; 2003. p. 19269.
200. Mackay M, Qin S, Clissold A, Hakendorf P, Ben-Tovim D, McDonnell G.
Patient flow simulation modelling-an approach conducive to multi-
disciplinary collaboration towards hospital capacity management. In:
Proceedings of the international congress on modelling and simulation;
2013. p. 506.
201. Mes M, Bruens M. A generalized simulation model of an integrated
emergency post. In: Proceedings of the winter simulation conference; 2012.
202. Khurma N, Bacioiu GM, Pasek ZJ. Simulation-based verification of lean
improvement for emergency room process. In: Proceedings of the winter
simulation conference; 2008. p. 14909.
203. Centeno MA, Lee MA, Lopez E, Fernandez HR, Carrillo M, Ogazon T. A
simulation study of the labor and delivery rooms at JMH. In: Proceeding of
the winter simulation conference; 2001. p. 1392400.
204. Ramakrishnan S, Nagarkar K, DeGennaro M, Srihari K, Courtney AK, Emick F.
A study of the CT scan area of a healthcare provider. In: Proceedings of the
winter simulation conference; 2004. p. 202531.
205. Ruohonen T, Neittaanmäki P, Teittinen J. Simulation model for improving
the operation of the emergency department of special health care. In:
Proceedings of the winter simulation conference; 2006. p. 4538.
206. Bountourelis T, Luangkesorn L, Schaefer A, Maillart L, Nabors SG, Clermont G.
Development and validation of a large scale ICU simulation model with blocking.
In: Proceedings of the winter simulation conference; 2011. p. 114353.
207. Al-Araidah O, Boran A, Wahsheh A. Reducing delay in healthcare delivery at
outpatients clinics using discrete event simulation. Int J Simul Model. 2012;
11:18595.
208. Pasupathy R. Performance evaluation in a laboratory medicine unit. In:
Proceedings of the winter simulation conference; 2013. p. 39723.
209. Taylor SJE, Abbott P, Young T, Grocott-Mason R. Student modeling &
simulation projects in healthcare: experiences with Hillingdon Hospital. In:
Proceedings of the winter simulation conference; 2014. p. 365061.
210. Oh C, Novotny AM, Carter PL, Ready RK, Campbell DD, Leckie MC. Use of a
simulation-based decision support tool to improve emergency department
throughput. Oper Res Health Care. 2016;9:2939.
211. Eskandari H, Riyahifard M, Khosravi S, Geiger CD. Improving the emergency
department performance using simulation and MCDM methods. In:
Proceedings of the winter simulation conference; 2011. p. 121122.
212. Mielczarek B, Uziałko J. Using simulation to forecast the demand for hospital
emergency services at the regional level. In: Proceedings of the winter
simulation conference; 2012.
213. Perimal-Lewis L. Health intelligence: discovering the process model using
process mining by constructing start-to-end patient journeys. In:
Proceedings of the Australasian workshop on health informatics and
knowledge management; 2014. p. 5967.
214. Hosseini N, Taaffe K. Evaluation of optimal scheduling policy for
accommodating elective and non-elective surgery via simulation. In:
Proceedings of the winter simulation conference; 2014. p. 137786.
215. Esengul Tayfur TK. Allocation of resources for hospital evacuations via
simulation. In: Proceedings of the winter simulation conference; 2007.
p. 114854.
216. Ashby M, Miller M, Ferrin D, Flynn T. Simulating the patient move:
transitioning to a replacement hospital. In: Proceedings of the winter
simulation conference; 2007. p. 15625.
217. Pérez ES, Yepez LA, de la Mota IF. Simulation and optimization of the pre-
hospital care system of the National University of Mexico using travelling
salesman problem algorithms. In: Proceedings of the summer computer
simulation conference; 2010. p. 36470.
218. Güttinger D, Godehardt E, Zinnen A. Optimizing emergency supply for
mass events. In: Proceedings of the 4th international ICST (Institute for
Computer Sciences, social-informatics and telecommunications
engineering) conference on simulation tools and techniques; 2011.
p. 12533.
219. Noreña D, Yamín L, Akhavan-Tabatabaei R, Ospina W. Using discrete event
simulation to evaluate the logistics of medical attention during the relief
operations in an earthquake in Bogota. In: Proceedings of the winter
simulation conference; 2011. p. 266678.
220. Ullrich C, Van Utterbeeck F, Dejardin E, Debacker M, Dhondt E. Pre-hospital
simulation model for medical disaster management. In: Proceedings of the
winter simulation conference; 2013. p. 24323.
221. Noei S, Santana H, Sargolzaei A, Noei M. Reducing traffic congestion using
geo-fence technology: application for emergency car. In: Proceedings of the
international workshop on emerging multimedia applications and services
for smart cities; 2014. p. 1520.
222. Moon I-C, Bae JW, Lee J, Kim D, Lee H, Lee T, et al. EMSSIM: emergency
medical service simulator with geographic and medical details. In:
Proceedings of the winter simulation conference; 2015. p. 127284.
223. Gibson IW. An approach to hospital planning and design using discrete
event simulation. In: Proceedings of the winter simulation conference; 2007.
p. 15019.
224. Miller MJ, Ferrin DM, Shahi N, LaVecchia R. Allocating outpatient clinic
services using simulation and linear programming. In: Proceedings of the
winter simulation conference; 2008. p. 163744.
225. Ashby M, Ferrin D, Miller M, Shahi N. Discrete event simulation: optimizing
patient flow and redesign in a replacement facility. In: Proceedings of the
winter simulation conference; 2008. p. 16326.
226. Boucherie RJ, Hans EW, Hartmann T. Health care logistics and space:
accounting for the physical build environment. In: Proceedings of the
winter simulation conference; 2012.
227. Wurzer G. In-process agent simulation for early stages of hospital planning.
Math Comput Model Dyn Syst. 2013;19:33143.
228. Wurzer G, Lorenz WE, Rössler M, Hafner I, Popper N, Glock BMODYPLAN.
Early-stage hospital simulation with emphasis on cross-clinical treatment
chains. In: Proceedings of the symposium on simulation for architecture
and urban design; 2015. p. 1603.
229. Schaumann D, Pilosof NP, Date K, Kalay YE. A study of human behavior
simulation in architectural design for healthcare facilities. Ann Dell Ist Super
Sanita. 2016;52:2432.
230. Cimellaro GP, Malavisi M, Mahin S. Using discrete event simulation models
to evaluate resilience of an emergency department. J Earthq Eng.
2017;21:20326.
231. Pulat PS, Kasap S, Splinter GL. Simulation study of an ideal primary care
delivery system. Simulation. 2001;76:7886.
232. Moeke D, van de Geer R, Koole G, Bekker R. On the performance of small-
scale living facilities in nursing homes: a simulation approach. Oper Res
Health Care. 2016;11:2034.
233. Maroufkhani A, Lanzarone E, Castelnovo C, Di Mascolo M. A discrete event
simulation model for the admission of patients to a home care
rehabilitation service. In: Matta A, Sahin E, Li J, Guinet A, Vandaele NJ,
editors. Health care systems engineering for scientists and practitioners.
New York: Springer; 2016. p. 91100.
234. Bhattacharjee P, Ray PK. Patient flow modelling and performance analysis of
healthcare delivery processes in hospitals: a review and reflections. Comput
Ind Eng. 2014;78:299312.
235. Hu S, Heim JA. Developing domain-specific simulation objects for modeling
clinical laboratory operations. In: Proceedings of the winter simulation
conference; 2014. p. 134152.
236. Workman RW. Simulation of the drug development process: a case study
from the pharmaceutical industry. In: Proceedings of the winter simulation
conference; 2000. p. 19958.
237. Blau GE, Pekny JF, Varma VA, Bunch PR. Managing a portfolio of
interdependent new product candidates in the pharmaceutical industry.
J Prod Innov Manag. 2004;21:22745.
238. Chen Y, Mockus L, Orcun S, Reklaitis GV. Simulation-optimization approach
to clinical trial supply chain management with demand scenario forecast.
Comput Chem Eng. 2012;40:8296.
239. Perez-Escobedo JL, Azzaro-Pantel C, Pibouleau L. Multiobjective strategies
for New Product Development in the pharmaceutical industry. Comput
Chem Eng. 2012;37:27896.
Zhang et al. Advances in Simulation (2018) 3:15 Page 14 of 16
240. Chen Y, Pekny JF, Reklaitis GV. Integrated planning and optimization of
clinical trial supply chain system with risk pooling. Ind Eng Chem Res. 2013;
52:15265.
241. El Afia A, Mezouar H. A global mapping of the Moroccan supply chain of
hospital drugs, and a simulation of the dispensation process. In:
Proceedings of the international conference on big data, cloud and
applications; 2017.
242. Huyghe J, Nouwen M, Vanattenhoven J. Involving end-users in game based
ideation: a case study in hospital logistics. In: Proceedings of the Nordic
conference on human-computer interaction; 2016.
243. Best AM, Dixon CA, Kelton WD, Lindsell CJ, Ward MJ. Using discrete event
computer simulation to improve patient flow in a Ghanaian acute care
hospital. Am J Emerg Med. 2014;32:91722.
244. Caputo AC, Pelagagge PM. Management criteria of automated order
picking systems in high-rotation high-volume distribution centers. Ind
Manag Data Syst. 2006;106:135983.
245. Katsaliaki K, Brailsford SC. Using simulation to improve the blood supply
chain. J Oper Res Soc. 2007;58:21927.
246. Vila-Parrish AR, Ivy JS, King RE. A simulation-based approach for inventory
modeling of perishable pharmaceuticals. In: Proceedings of the winter
simulation conference; 2008. p. 15328.
247. Jung JY, Blau G, Pekny JF, Reklaitis GV, Eversdyk D. Integrated safety stock
management for multi-stage supply chains under production capacity
constraints. Comput Chem Eng. 2008;32:257081.
248. Mustafee N, Taylor SJE, Katsaliaki K, Brailsford S. Facilitating the analysis of a
UK national blood service supply chain using distributed simulation.
Simulation. 2009;85:11328.
249. Rossetti MD, Liu Y. Simulating SKU proliferation in a health care supply chain.
In: Proceedings of the winter simulation conference; 2009. p. 236574.
250. Ren C, Wang W, He M, Shao B, Wang Q, Dong J. The use of simulation for
Global Supply Network rationalization. In: Proceedings of IEEE international
conference on service operations and logistics, and informatics. 2010. p. 27681.
251. Babaï MZ, Syntetos AA, Dallery Y, Nikolopoulos K. Dynamic re-order point
inventory control with lead-time uncertainty: analysis and empirical
investigation. Int J Prod Res. 2009;47:246183.
252. Baesler F, Bastías A, Nemeth M, Martínez C. Blood centre inventory analysis
using discrete simulation. In: Proceedings of the winter simulation
conference; 2012.
253. Onggo BS. Elements of a hybrid simulation model: a case study of the
blood supply chain in low-and middle-income countries. In: Proceedings of
the winter simulation conference; 2014. p. 1597607.
254. Gebicki M, Mooney E, Chen S-J, Mazur LM. Evaluation of hospital
medication inventory policies. Health Care Manag Sci. 2014;17:21529.
255. Duan Q, Liao TW. Optimization of blood supply chain with shortened shelf
lives and ABO compatibility. Int J Prod Econ. 2014;153:11329.
256. Baesler F, Nemeth M, Martínez C, Bastías A. Analysis of inventory strategies
for blood components in a regional blood center using process simulation.
Transfusion. 2014;54:32330.
257. Wang K-M, Ma Z-J. Age-based policy for blood transshipment during blood
shortage. Transp Res Part E Logist Transp Rev. 2015;80:16683.
258. Leung N-HZ, Chen A, Yadav P, Gallien J. The impact of inventory
management on stock-outs of essential drugs in Sub-Saharan Africa:
secondary analysis of a field experiment in Zambia. PLoS One. 2016;11:
e0156026.
259. Yurtkuran A, Emel E. Simulation based decision-making for hospital
pharmacy management. In: Proceedings of the winter simulation
conference; 2008. p. 153946.
260. Lee YM. Analyzing dispensing plan for emergency medical supplies in the
event of bioterrorism. In: Proceedings of the winter simulation conference;
2008. p. 26008.
261. Lee YM, Ghosh S, Ettl M. Simulating distribution of emergency relief
supplies for disaster response operations. In: Proceedings of the winter
simulation conference; 2009. p. 2797808.
262. Ozdamar L. Planning helicopter logistics in disaster relief. Spectrum. 2011;33:
65572.
263. Kulkarni NS, Niranjan S. Multi-echelon network optimization of
pharmaceutical cold chains: a simulation study. In: Proceedings of the
winter simulations conference; 2013. p. 348698.
264. Postacchini L, Ciarapica FE, Bevilacqua M, Mazzuto G, Paciarotti C. A way for
reducing drug supply chain cost for a hospital district: a case study. J Ind
Eng Manag. 2016;9:20730.
265. Abdelkafi C, Beck BHL, David B, Druck C, Horoho M. Balancing risk and costs
to optimize the clinical supply chain-a step beyond simulation. J Pharm
Innov. 2009;4:96106.
266. Alfonso E, Xie X, Augusto V, Garraud O. Modelling and simulation of blood
collection systems: improvement of human resources allocation for better
cost-effectiveness and reduction of candidate donor abandonment. Vox
Sang. 2013;104:22533.
267. Cho S-H, Jang H, Lee T, Turner J. Simultaneous location of trauma centers and
helicopters for emergency medical service planning. Oper Res. 2014;62:75171.
268. Liao H-C, Chang H-H. The optimal approach for parameter settings based
on adjustable contracting capacity for the hospital supply chain logistics
system. Expert Syst Appl. 2011;38:47907.
269. Akcay A, Martagan T. Stochastic simulation under input uncertainty for
contract-manufacturer selection in pharmaceutical industry. In: Proceedings
of the winter simulation conference; 2016. p. 2292303.
270. Jacobs EA, Bickel WK. Modeling drug consumption in the clinic using
simulation procedures: demand for heroin and cigarettes in opioid-
dependent outpatients. Exp Clin Psychopharmacol. 1999;7:41226.
271. Royston G, Dost A, Townshend J, Turner H. Using system dynamics to help
develop and implement policies and programmes in health care in
England. Syst Dyn Rev. 1999;15:293313.
272. Mustafee N, Taylor SJE, Katsaliaki K, Brailsford S. Distributed simulation with
COTS simulation packages: a case study in health care supply chain simulation.
In: Proceedings of the winter simulation conference; 2006. p. 113642.
273. Muller J, Popke C, Urbat M, Zeier A, Plattner H. A simulation of the
pharmaceutical supply chain to provide realistic test data. In: Proceedings of
the international conference on advances in system simulation; 2009. p. 449.
274. Devi SP, Rao KS, Krishnaswamy S, Wang S. System dynamics model for
simulation of the dynamics of corneal transplants. OPSEARCH. 2010;47:28492.
275. Ng Adam TS, Sy C, Li J. A system dynamics model of Singapore healthcare
affordability. In: Proceedings of the winter simulation conference; 2011. p. 130618.
276. Djanatliev A, German R, Kolominsky-Rabas P, Hofmann BM. Hybrid
simulation with loosely coupled system dynamics and agent-based models
for prospective health technology assessments. In: Proceedings of the
winter simulation conference; 2012.
277. zen MB. Estimating future demand for hospital emergency services at the
regional level. In: Proceedings of the winter simulation conference; 2013. p.
238697.
278. Elleuch H, Hachicha W, Chabchoub H. A combined approach for supply
chain risk management: description and application to a real hospital
pharmaceutical case study. J Risk Res. 2014;17:64163.
279. Padilla JJ, Diallo SY, Kavak H, Sahin O, Sokolowski JA, Gore RJ. Semi-
automated initialization of simulations: an application to healthcare. J Def
Model Simul. 2015;13:17182.
280. Taaffe K, Zinouri N, Kamath AG. Integrating simulation modeling and mobile
technology to improve day-of-surgery patient care. In: Proceedings of the
winter simulation conference; 2016. p. 21112.
281. Pitt M, Monks T, Crowe S, Vasilakis C. Systems modelling and simulation in
health service design, delivery and decision making. BMJ Qual Saf. 2016;25:
3845.
282. Jahangirian M, Borsci S, Shah SGS, Taylor SJE. Causal factors of low
stakeholder engagement: a survey of expert opinions in the context of
healthcare simulation projects. Simulation. 2015;91:51126.
283. Dangerfield BC. System dynamics applications to European health care
issues. J Oper Res Soc. 1999;50:45353.
284. Harrell CR, Price RN. Healthcare simulation modeling and optimization using
MedModel. In: Proceedings of the winter simulation conference; 2000. p. 2037.
285. Joustra P, van der Sluis E, van Dijk NM. To pool or not to pool in hospitals: a
theoretical and practical comparison for a radiotherapy outpatient
department. Ann Oper Res. 2010;178:7789.
286. Morrison BP, Bird BC. Healthcare process analysis: a methodology for
modeling front office and patient care processes in ambulatory health care.
In: Proceedings of the winter simulation conference; 2003. p. 18826.
287. Baldwin LP, Eldabi T, Paul RJ. Simulation in healthcare management: a soft
approach (MAPIU). Simul Model Pract Theory. 2004;12:54157.
288. White KP. A survey of data resources for simulating patient flows in
healthcare delivery systems. In: Proceedings of the winter simulation
conference; 2005. p. 92635.
289. Brailsford S. Overcoming the barriers to implementation of operations
research simulation models in healthcare. Clin Investig Med Med Clin Exp.
2005;28:3125.
Zhang et al. Advances in Simulation (2018) 3:15 Page 15 of 16
290. Eldabi T, Paul RJ, Young T. Simulation modelling in healthcare: reviewing
legacies and investigating futures. J Oper Res Soc. 2007;58:26270.
291. Chahal K, Eldabi T. Applicability of hybrid simulation to different modes of
governance in UK healthcare. In: Proceedings of the winter simulation
conference; 2008. p. 146977.
292. Gupta D, Denton B. Appointment scheduling in health care: challenges and
opportunities. IIE Trans. 2008;40:80019.
293. Brailsford SC, Harper PR, Patel B, Pitt M. An analysis of the academic literature
on simulation and modelling in health care. J Simul. 2009;3:13040.
294. Katsaliaki K, Mustafee N, Taylor SJE, Brailsford S. Comparing conventional
and distributed approaches to simulation in a complex supply-chain health
system. J Oper Res Soc. 2009;60:4351.
295. Forsythe L. Action research, simulation, team communication, and bringing
the tacit into voice society for simulation in healthcare. Simul Healthc.
2009;4:1438.
296. Seropian M, Lavey R. Design considerations for healthcare simulation
facilities. Simul Healthc. 2010;5:33845.
297. Katsaliaki K, Mustafee N. Applications of simulation within the healthcare
context. J Oper Res Soc. 2011;62:143151.
298. LeBlanc VR, Manser T, Weinger MB, Musson D, Kutzin J, Howard SK. The
study of factors affecting human and systems performance in healthcare
using simulation. Simul Healthc. 2011;6:249.
299. Beliën J, Forcé H. Supply chain management of blood products: a literature
review. Eur J Oper Res. 2012;217:116.
300. Jahangirian M, Naseer A, Stergioulas L, Young T, Eldabi T, Brailsford S, et al.
Simulation in health-care: lessons from other sectors. Oper Res. 2012;12:4555.
301. Hong TS, Shang PP, Arumugam M, Yusuff RM. Use of simulation to solve
outpatient clinic problems: a review of the literature. South Afr J Ind Eng.
2013;24:2742.
302. Holm LB, Dahl FA. Simulating the effect of physician triage in the
emergency department of Akershus University Hospital. In: Proceedings of
the winter simulation conference; 2009. p. 1896905.
303. Günal MM, Pidd M. DGHPSim: supporting smart thinking to improve
hospital performance. In: Proceedings of the winter simulation conference;
2008. p. 14849.
304. Yeon N, Lee T, Jang H. Outpatients appointment scheduling with multi-
doctor sharing resources. In: Proceedings of the winter simulation
conference; 2010. p. 331829.
305. Sugiyama T, Goryoda S, Inoue K, Sugiyama-Ihana N, Nishi N. Construction of
a simulation model and evaluation of the effect of potential interventions
on the incidence of diabetes and initiation of dialysis due to diabetic
nephropathy in Japan. BMC Health Serv Res. 2017;17:833.
Zhang et al. Advances in Simulation (2018) 3:15 Page 16 of 16
... We use the term simulation to denote real-life experiential play, instead of virtual play using a digital game. Experiential simulations have a significant history of supporting a range of disciplines particularly in business and healthcare education (Faria, 2001;Gold, 2016;Wolfe & Keys, 1997;Zhang, Grandits, Härenstam, Hauge, & Meijer, 2018). Highly structured business simulations frequently ask students to interact with software-based platforms to perform computations and forecasts, while integrating their use as experiential teaching tools (de Smale, Overmans, Jeuring, & Grint, 2016;Gold, 2016;Tanner, Stewart, Totaro, & Hargrave, 2012;Vlachopoulos & Makri, 2017;Zhang et al., 2018). ...
... Experiential simulations have a significant history of supporting a range of disciplines particularly in business and healthcare education (Faria, 2001;Gold, 2016;Wolfe & Keys, 1997;Zhang, Grandits, Härenstam, Hauge, & Meijer, 2018). Highly structured business simulations frequently ask students to interact with software-based platforms to perform computations and forecasts, while integrating their use as experiential teaching tools (de Smale, Overmans, Jeuring, & Grint, 2016;Gold, 2016;Tanner, Stewart, Totaro, & Hargrave, 2012;Vlachopoulos & Makri, 2017;Zhang et al., 2018). Notably, as found in Tanner et al. (2012), students can improve their decision-making through an iterative decisionmaking process. ...
... In both simulations students tackled political and social issues within a game structure and with rule-based play. Further insights came from healthcare simulations, where student participation within nontechnical healthcare simulations faced an arguably broader set of challenges, since real-life and real-time operational and strategic processes in addition to negotiation and reasoning skills (Zhang et al., 2018). Notably, the simulations described provide a highly structured environment to support gameplay. ...
Chapter
Between 2014 and 2016, the Syria Simulation was delivered as an in-person, tabletop role-playing game to over 700 students at a liberal arts university in Texas. This low-fidelity, 3-h simulation was designed to address global learning programs and course outcomes by asking students to analyze events from multiple geopolitical perspectives while role-playing key actors and organizations in the Syria conflict. Game design was grounded in high-impact curriculum design and instructional design practice while employing game design mechanics to target twenty-first-century competencies and enhance student engagement. A diverse faculty team used a gameful, experiential strategy to challenge students to tackle the complex problem of achieving peace in the Middle East. This gameful approach to learning immersed students within an authentic, experiential setting, challenged their decision-making, and provided opportunities for reflection. Role-play enabled students to play conflict actors and supported cooperative learning goals while shaping student perspectives on the conflict. In this chapter, we discuss the Syria Simulation project through these theoretical lenses and describe the ways in which the game’s design reflects an experiential system of rules, play, and culturally responsive design. We will also describe the game’s design and development process, and its impact on student engagement, and we will explain its integration into a liberal arts curriculum. We also present a model for how narrow identification of learning problems, threshold concepts, strategic design of game mechanics, and an engagement ethos can be used to support powerful, playful, high-impact learning for a range of learning outcomes and student participation.
... The RP diagram (Fig. 6) and SD model (Fig. 2) alluded to the complexity of IP scheduling, and the importance of the task to the smooth running of the CT department. Workflow mapping (Fig. 5) best demonstrated this complexity and the non-technical skill set required to schedule IP exams [57]. It is recommended that formal training using a decision support flow chart such as Fig. 5, be given to staff responsible for scheduling. ...
Article
Full-text available
Demand for Computer Tomography (CT) is growing year on year and the population of Ireland is increasingly aging and ailing. Anecdotally, radiology staff reported increasing levels of workload associated with the patient profile. In this paper, we propose a framework combining discrete event simulation (DES) modeling and soft systems methodologies (SSM) for use in healthcare which captures the staff experience and metrics to evidence workload. The framework was applied in a single-scanner CT department, which completes circa 6000 examinations per year. The scanner case load consists of unscheduled work [inpatient (IP) and emergency department (ED)] and scheduled work [outpatient (OP) and general practitioner (GP)]. The three stage framework is supported by qualitative and quantitative methods and uses DES as a decision support tool. Firstly, workflow mapping and system dynamics are used to conceptualize the problem situation and instigate a preliminary data analysis. Secondly, SSM tools are used to identify components for a DES model and service improvement scenarios. Lastly, the DES model results are used to inform decision-making and identify a satisficing solution. Data from the DES model provided evidence of the differing workload (captured in staff time) for the IP and OP cohorts. For non-contrast examinations, inpatient workload is 2.5 times greater than outpatient. Average IP process delays of 11.9 min were demonstrated compared to less than 1 min for OP. The findings recommend that OP and IP diagnostic imaging be provided separately, for efficiency, workload management and infection control reasons.
... The study reviewed SG and simulations in escalating situations, then focused on SG for natural hazards and tsunami scenarios which contain evacuation decisions. Applications of SGs include combat training, (Hunt et al., 1987), (Yildirim, 2014), (Angelevski & Bogatinov, 2014), (Samčović, 2018) healthcare training, (Khorram-Manesh et al., 2016), (Zhang et al., 2018), industrial emergency management training, (Metello et al., 2008), and civil defence emergencies (Ra et al., 2016), (Mossel et al., 2017). However, a distinction must be made between military or medical professionals who understand the likelihood of applying their learning in an emergency situation and school learners who perceive the risk of an emergency scenario to be low. ...
Article
Full-text available
Earthquake research has expanded over the past few decades. Also, over the past 30 years, earthquakes have become a major study because earthquakes occur every year in each region. The aims of this research is to analyze the top 100 cited articles in the earthquake field from 1991 to 2021. Research give an idea of citation, author, year, journal and country characteristics of these articles using literature review, bibliometric analysis and VOSViewer which the data from Scopus database. The research found that articles is most document type of top 100 cited papers which 2005 was the most published year for the article. The average number of citations per article was calculated as 727 citations per paper. The journal Nature is the primary source of the Nature Publishing Group, which governs the publication of the most influential earthquake studies. Kanamori is recognized as the most productive author who received the highest number of quotes and the most incredible link strength. The United States dominates the production of highly cited articles. The research areas in these papers are mainly emphasized on earthquake, states, geological, sciences, earth, and japan. Further research related to earthquakes can also be directed to the relevance of the tsunami. The results of the Scopus database show 2,098 document results [January 17, 2022] with the title “earthquake tsunami.”.
... A 2018 systematic review revealed a gap in the literature on the efficacy of High-Fidelity Patient Simulation (HFPS). As regards the available tests, HFPS is particularly useful for the training of non-technical skills (Zhang et al., 2018). The review identified two specific themes: a) the use of simulation for clinical performance and b) the use of simulation in assessing self-confidence and perceived competence. ...
Article
Full-text available
BACKGROUND: High-fidelity simulation can be defined as a technique to refine specific human performance in a protected environment, and thanks to the innovation of technologies it has been possible to obtain increasingly realistic performances. The aim of this study is to map and describe the effects of training through a high-fidelity simulation method on the technical and non-technical skills of nursing students in an emergency setting. METHODS: A scoping review was chosen as research methodology within some main databases of biomedical interest: MEDLINE, Scopus, CINAHL, PsycINFO, Academic Search Index, Science Citation Index and ERIC. RESULTS: 530 articles were selected. Of these, 21 met the inclusion criteria and underwent the review process. Participants undergoing the intervention demonstrated better skills than the control group when subjected to a simulation based on realistic scenarios. The selected articles can be divided into two categories: those that focus essentially on non-technical skills and those that study only technical skills. CONCLUSION: Evidence suggests that HFS should include feedback, briefing and debriefing; it should be applied in every area of nursing education; student self-efficacy, confidence and competence are key principles to consider when measuring the effect of a simulation environment. KEYWORDS: Nursing students; High-fidelity simulation; Emergency; Technical Skills; Nontechnical skills.
... A comparative analysis of the content of the published articles regarding Lean implementation in medical analysis laboratories, with emphasis on histopathological testing laboratories, highlighted the following: -Lean, in its various variants, is a managerial method for streamlining business and optimizing technological flow, which can be recommended in the process of technological transfer and digitization of the activity [63][64][65][66][67]; -Although the employees of the laboratories were reluctant to apply the method for fear of dismissal, it is obvious that in such activities, we witness not job destruction but definitely a job content enrichment process, which means digital skills and permanent updating of professional knowledge [68][69][70]; - ...
Article
Full-text available
Important in testing services in medical laboratories is the creation of a flexible balance between quality-response time and minimizing the cost of the service. Beyond the different Lean methods implemented so far in the medical sector, each company can adapt the model according to its needs, each company has its own specifics and organizational culture, and Lean implementation will have a unique approach. Therefore, this paper aims to identify the concerns of specialists and laboratory medical services sector initiatives in optimizing medical services by implementing the Lean Six Sigma method in its various variants: a comparative analysis of the implemented models, with emphasis on measuring externalities and delimiting trends in reforming/modernizing the method, a comprehensive approach to the impact of this method implementation, and an analysis of available databases in order to underline the deficit and information asymmetry. The results highlighted that in the case of clinical laboratories, the Lean Six Sigma method is conducive to a reduction of cases of diagnostic errors and saves time but also faces challenges and employees' resistance in implementation.
... Moreover, Zhang C. et al. [16] indicate the usefulness of modeling for non-technical skill training in health and care servicing. ...
Conference Paper
Full-text available
Nowadays, the study of the behavior of social, economic, and technical queuing systems at different stages of their design and operation is a challenge for simulation modeling. The complexity of the dynamic structure of such systems is owing to a number of factors. Among them are a large number of system characteristics and relations between them, the existence of a large spectrum of distribution laws of random events, the need to collect and analyze current data on the system under study, and the presence of various constraints. Moreover, relations can be represented by functional, statistical, ambiguous, or other mappings. Unfortunately, simulation modeling tools are not widespread in designing and applying business processes, unlike other products of information and computation technologies (system of the office administration, warehouse management, etc.). In this regard, we present an approach to developing web-service for simulation modeling of queuing systems. Within the approach, we automate some stages of web-service development and use. To this end, we apply tools that support distributed computing with parameter variations in a heterogeneous environment, which includes virtualized resources. We provide a multi-agent management of computations. The practical use of the tools is shown on the example of developing a web-service for simulation modeling of a typical health and care institution.
Article
Managing a portfolio of innovation projects is a complex task that requires special approaches and management methods. According to statistics, only 69% of innovation projects correlate with the strategic goals the company originally set for itself. Innovative projects involve working in an environment of constant variability, inaccessibility of information and high risk. The aim of the work is to propose a tool for managing a portfolio of innovative projects using fuzzy logic methods and simulation modeling to improve the performance of project managers and achieve the strategic objectives of the organization, and as a consequence, create a competitive advantage in the market. Research methodology is based on taking into account the theory of fuzzy logic and the theory of system dynamics to create a simulation model by Bass diffusion. The article proposes a tool for managing a portfolio of innovative projects using fuzzy logic and simulation modeling, and provides recommendations for combining the proposed tool with modern PMIS and analytical systems that allow pre-project and post-project analysis, which allows you to manage and monitor the performance of the portfolio throughout the full cycle of its implementation.
This paper is a literature review of ambulance deployment and redeployment modelling approaches in emergency medical services (EMS): mathematical programming, queuing theory, and simulation. The three approaches are presented and compared, highlighting their applications, advantages, and limitations. Mathematical programming has undergone considerable evolution over the years in a continuous effort to propose models considering stochastic aspects of the environment. Therefore, this paper pays special attention to mathematical models and their solution methods. Queueing and simulation models are descriptive and allow the evaluation of the system performance. Recent trends in modelling tend to combine several approaches, sequentially or iteratively, to take advantage of the benefits of each approach.
Thesis
Access to healthcare is a critical public health issue in the United States, especially for veterans. Veterans are older on average than the general U.S. population and are thus at higher risk for chronic disease. Further, veterans report more delays when seeking healthcare. The Veterans Affairs (VA) Healthcare System continuously works to develop policies and technologies that aim to improve veteran access to care. Industrial engineering methods can be effective in analyzing the impact of such policies, as well as designing or modifying systems to better align veteran patients’ needs with providers and resources. This dissertation demonstrates how industrial engineering tools can guide policy decisions to improve healthcare access by connecting veterans with the most appropriate healthcare resources, while highlighting the trade-offs inherent in such decisions. This work comprises four stages: (1) using optimization methods to design a healthcare network when introducing new provider options for chronic disease screening, (2) developing simulation tools to model how access to care is impacted when scheduling policies accommodate patient preferences, and (3) simulating triage strategies for non-emergency care during COVID-19, and (4) evaluating how treatment decisions impact patient access when guided by risk-based prediction models compared to current practice. In the first stage, we consider veteran access to chronic eye disease screening. Ophthalmologists in the VA have developed a platform in which ophthalmic technicians screen patients for major chronic eye diseases during primary care visits. We use mixed-integer programming-based facility location models to understand how the VA can determine which clinics should offer eye screenings, which provider type(s) should staff those clinics, and how to distribute patients among clinics. The results of this work show how the VA can achieve various objectives including minimizing the cost or maximizing the number of patients receiving care. In the second stage, we simulate patients seeking care for gastroesophageal reflux disease with primary care and gastrointestinal providers. This simulation incorporates policies about how to schedule patients for visits in various modalities, including face-to-face and telehealth, and also considers uncertainty in key factors like patient arrivals and demographics. Results of these models can help us understand how scheduling based on these preferences impacts access, including time to first appointment and number of patients seen. Such metrics can guide healthcare administrators as new technologies are introduced that offer options for how patients interact with their providers. In the third stage, we simulate patients seeking non-emergency outpatient care under reduced appointment capacity due to the COVID-19 pandemic. We demonstrate this using endoscopy visits as a central example. We use our simulation model to understand how various strategies for adjusting patient triage and/or clinic operations can mitigate patient backlog and reduce patient waiting times. In the fourth stage, we integrate multiple industrial engineering methods to examine how access is impacted among chronic liver disease patients when predictive modeling is introduced into treatment planning. We developed a simulation model to help clinical decision-makers better understand how using a predictive model may change the care pathway for a specific patient and also impact system decisions, such as required staffing levels and clinical data acquired at specific patient visits. The model also helps clinicians understand the value of specific clinical data (lab values, vitals, etc.) by demonstrating how better or worse inputs to the predictive models have larger system impacts to patient access.
Article
Full-text available
Background: The prevalence of diabetes mellitus is a growing public health concern in Japan. We developed a simulation model to predict the number of people with diabetes and those on dialysis due to diabetic nephropathy. In addition, we used the model to simulate the impact of possible interventions on the number of people with diabetes and those on dialysis due to diabetic nephropathy in the near future. Methods: A simulation model with aging chains for diabetes management was built using system dynamics. The model was calibrated to population data from 2000 to 2015 (sex- and age category-specific population, the prevalence of diabetes, and the number of patients on dialysis due to diabetic nephropathy). We extrapolated the model up to 2035 in order to predict future prevalence of diabetes and related dialysis (base run). We also ran the model, hypothesizing that incidence of diabetes and/or related dialysis would be reduced by half from 2015 to 2025 and that this rate would be maintained until 2035, in order to investigate the effects of hypothetical interventions on future prevalence. Results: The developed model forecasted the population with diabetes to increase until 2028 (5.58 million males and 3.34 million females), and the population on dialysis due to diabetic nephropathy to increase until 2035 (113,000 males and 48,000 females). Simulation experiments suggested that diabetes prevention interventions would decrease the number of patients on dialysis in 2035 by 13.8% in males and 12.6% in females compared to the base run. In contrast, interventions aiming to avoid dialysis initiation for patients with diabetes would decrease the number of patients on dialysis by 37.8% in males and 38.1% in females. Conclusions: We successfully developed a simulation model to project the number of patients with diabetes and those on dialysis due to diabetic nephropathy. Simulation experiments using the model suggested that, as far as the perspective of the next 20 years, intervention to prevent dialysis is an important means of bending the increasing curve of dialysis in the population with diabetes. Simulation analysis may be useful when making and evaluating health policies related to diabetes and other chronic diseases.
Article
Full-text available
Faced with the opportunity to significantly deviate from classic operations, a new emergency department (ED) and novel strategy for patient care delivery were simultaneously initiated with the aid of model-based simulation. To answer the design and implementation questions, a traditional strategy for construction of discrete-eventmodel simulation was employed to define ED operations for a newly constructed facility in terms of workflow, variables, resources, structure, process logic and associated assumptions. Benefits were achieved before, during and after implementation of an unprecedented operations strategy—i.e., the organization of the ED care delivery around four care streams: Critical, Diagnostic, Therapeutic and Fast Track. Prior to opening, it shed light on the range of context variables where benefits might be anticipated, and it facilitated staff understanding and judgements of performance. Two years after opening, the operations data is compared to the simulation with encouraging results that shed light on where to continue pursuit of improvement.
Conference Paper
Full-text available
Pharmaceuticals are an important component of the health system activity. Their contribution to improving the health status is vital. To ensure sound management of the pharmaceutical products logistics (orders, delivery, storage and distribution), a new procurement strategy has been adopted by Morocco in 2012. This strategy is based on a centralized procurement, by which the purchase is grouped by anticipation (the budget for the year in progress / the exercise of the following year), and a decentralized storage and distribution allowing the territory stratification in eight regional depots: Berrechid, Oujda, Al-Hoceima, Laayoune, Agadir, Marrakech, Meknes and Tangier. The challenges this environment is facing justify the focus on this research subject, and the contribution of this work includes a global mapping of the Moroccan supply chain of hospital drugs, and a simulation of the dispensation process comparing the global and the nominative dispensation.
Article
Full-text available
Hospital patients often move from one bed to another for both medical and nonmedical reasons. In a highly utilized quaternary inpatient pediatric unit we have studied, bed and nursing resources are stressed not only by frequent movement of patients but also by the unit’s patient discharge policy. We present a discrete-event simulation model for examining how the unit’s efficiency may be improved by a better discharge policy. In particular, we use the base version of the model to investigate the impact of sending various percentages of discharge-ready patients to a discharge holding area where they can safely wait for a few hours until being picked up by their parent or guardian. Doing so frees up inpatient beds, allowing the unit to serve many more pediatric patients per year. In a revised version of the model we quantify the benefits of helping some patients achieve discharge-ready status a few hours earlier than under current operations. In both cases, our cost analysis shows that the unit could realize...
Conference Paper
The production process inside a hospital surgery department is made up of three distinct main sub-processes: waiting list management, operating theatre planning and scheduling, stay area sizing and organization. Overall department performance depends on how these sub-processes are managed as well as they are integrated. In the literature, they are mainly treated separately recurring to two approaches: optimization or simulation. The complexity of the system often makes the optimization models intractable, whereas simulation seems to be preferred because of its ability to evaluate what if scenarios. The novelty of this paper is to propose an integrated approach to deal with the described issues. The approach is integrated under two points of view: firstly, because it concerns all the three sub-processes together, i.e. from the moment the patient enters the system to the moment it is discharged, and, secondly, because it utilizes both the methods, i.e. simulation and optimization. The proposed holistic inte
Purpose Two different systems for streaming patients were considered to improve efficiency measures such as waiting times (WTs) and length of stay (LOS) for a current emergency department (ED). A typical fast track area (FTA) and a fast track with a wait time threshold (FTW) were designed and compared effectiveness measures from the perspective of total opportunity cost of all patients’ WTs in the ED. The paper aims to discuss these issues. Design/methodology/approach This retrospective case study used computerized ED patient arrival to discharge time logs (between July 1, 2009 and June 30, 2010) to build computer simulation models for the FTA and fast track with wait time threshold systems. Various wait time thresholds were applied to stream different acuity-level patients. National average wait time for each acuity level was considered as a threshold to stream patients. Findings The fast track with a wait time threshold (FTW) showed a statistically significant shorter total wait time than the current system or a typical FTA system. The patient streaming management would improve the service quality of the ED as well as patients’ opportunity costs by reducing the total LOS in the ED. Research limitations/implications The results of this study were based on computer simulation models with some assumptions such as no transfer times between processes, an arrival distribution of patients, and no deviation of flow pattern. Practical implications When the streaming of patient flow can be managed based on the wait time before being seen by a physician, it is possible for patients to see a physician within a tolerable wait time, which would result in less crowded in the ED. Originality/value A new streaming scheme of patients’ flow may improve the performance of fast track system.
Article
Hospital based emergency departments (EDs) are highly integrated service units devoted primarily to handling the needs of patients arriving without prior appointment, and with uncertain conditions. In this context, analysis and management of patient flows play a key role in developing policies and decisions for overall performance improvement. However, patient flows in EDs are considered to be very complex because of the different pathways patients may take and the inherent uncertainty and variability of healthcare processes. The agent-based model provides a flexible platform for studying ED operations, as it predicts the system-level behavior from individual level interactions. In this way, policies such as staffing can be changed and the effect on system performance, such as waiting times and throughput, can be quantified. The overall goal of this study is to develop tools to better understand the complexity, evaluate policy and improve efficiencies of ED units. The main contribution of this paper includes: an agent-based model of ED, a flexible atomic data monitoring layer for agent state tracing, and a master/worker based framework for efficiently executing the model and analyzing simulation data. The presented model has been calibrated to imitate a real ED in Spain, the simulation results have proven the feasibility of using agent-based model to study ED system.
Article
Background.: Carbapenem-resistant Enterobacteriaceae (CRE) are high-priority bacterial pathogens targeted for efforts to decrease transmissions and infections in healthcare facilities. Some regions have experienced CRE outbreaks that were likely amplified by frequent transmission in long-term acute care hospitals (LTACHs). Planning and funding intervention efforts focused on LTACHs is one proposed strategy to contain outbreaks; however, the potential regional benefits of such efforts are unclear. Methods.: We designed an agent-based simulation model of patients in a regional network of 10 healthcare facilities including 1 LTACH, 3 short-stay acute care hospitals (ACHs) and 6 nursing homes (NHs). The model was calibrated to achieve realistic patient flow and CRE transmission and detection rates. We then simulated the initiation of an entirely LTACH-focused intervention in a previously CRE-free region, including active surveillance for CRE carriers and enhanced isolation of identified carriers. Results.: When initiating the intervention at the 1st clinical CRE detection in the LTACH, cumulative CRE transmissions over 5 years across all 10 facilities were reduced by 79-93% compared to no-intervention simulations. This result was robust to changing assumptions for transmission within non-LTACH facilities and flow of patients from the LTACH. Delaying the intervention until the 20th CRE detection resulted in substantial delays in achieving optimal regional prevalence, while still reducing transmissions by 60-79% over 5 years. Conclusions.: Focusing intervention efforts on LTACHs is potentially a highly efficient strategy for reducing CRE transmissions across an entire region, particularly when implemented as early as possible in an emerging outbreak.
Article
Efficient staff rostering and patient scheduling to meet outpatient demand is a very complex and dynamic task. Due to fluctuations in demand and specialist availability, specialist allocation must be very flexible and non-myopic. Medical specialists are typically restricted in sub-specialization, serve several patient groups and are the key resource in a chain of patient visits to the clinic and operating room (OR). To overcome a myopic view of once-off appointment scheduling, we address the patient flow through a chain of patient appointments when allocating key resources to different patient groups. We present a new, data-driven algorithmic approach to automatic allocation of specialists to roster activities and patient groups. By their very nature, simplified mathematical models cannot capture the complexity that is characteristic to the system being modeled. In our approach, the allocation of specialists to their day-to-day activities is flexible and responsive to past and present key resource availability, as well as to past resource allocation. Variability in roster activities is actively minimized, in order to enhance the supply chain flow. With discrete-event simulation of the application case using empirical data, we illustrate how our approach improves patient Service Level (SL, percentage of patients served on-time) as well as Wait Time (days), without change in resource capacity.