R E S E A R C H Open Access
A systematic literature review of simulation
models for non-technical skill training in
, Thomas Grandits
, Karin Pukk Härenstam
, Jannicke Baalsrud Hauge
and Sebastiaan Meijer
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
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: email@example.com
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
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 .
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 .
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 . To create meaningful sim-
ulations for training the non-technical skills used in
coordination , 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 . 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. , 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-test”final 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
; 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 user’s 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 . System dynamics focus on the ef-
fect of structure on behavior . Instead of addressing
individual transactions, system dynamics is commonly
used for higher level problems, such as strategic decision
making, management controls, or policy changes .
Agent-based simulation is based on a “bottom-up”con-
struction for the provision of emergent phenomena
based on individual interactions of resource units .
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 , a single simula-
tion technique , 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. , 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
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.Keywordssuchas“healthcare,”“patient flow,”
“pharma*,”“blood,”and “drug”specify the issues ad-
dressed. Keywords such as “simulation,”“system dynam-
ics,”“simulator,”and “game”specify the research methods
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
(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.
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
TI = ((“healthcare”OR “health SAME care”)
AND (“system SAME dynamics”OR “patient
SAME flow”OR “gam*”)
TI = (“healthcare”OR “health SAME care OR care”)
AND TS = (“pharma*”OR “blood”OR “drug”)
AND TI = (“simulation”OR “system SAME dynamic*”
OR “simulator*”OR “gam*”)
ACM recordAbstract:(+(“health care”“healthcare”)+
(“system dynamics”“patient flow”“gam*”))
JSTOR ti:(“healthcare”OR “health care”) AND
(“system dynamics”OR “patient flow”
OR “game”OR “simulation”)
ti:((“drug”OR “hospital”OR “blood”OR “pharmaceutical”)
AND (“system dynamics”OR “patient flow”OR “game”
OR “simulation”OR “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.
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 . Deva-
priya et al. also developed a decision-supporting tool
based on discrete-event simulation for the strategic plan-
ning of hospital bed capacity . Bhattacharjee et al.
analyzed appointment scheduling policies for patients to
be treated by a medical scanning machine . Vasilakis
et al. developed a discrete-event simulation to study how
long it took for patients to obtain their appointments
from their referral . 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 . 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 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 . The focus was twofold:
testing the policies for solving delayed discharges and
envisaging the counterproductive and unintended conse-
quences of these new policies . Brailsford et al. simu-
lated patient flow perspectives to identify system-wide
bottlenecks . 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
DES (63); ABS (3);
SD (3); mixed (4),
Single department (65),
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.
DES (50); SD (3);
ABS (3); gaming (1);
mixed (26), Misc.(5)
Single department (56),
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 [203–214] DES (8); Misc. (4) Single department (7),
Arena (3); Simul8(2); ProModel (1); MedModel
(1); OMNeT++ (1); ExtendSim (1); Misc. (3)
Simulation is used to identify impact factors in
[215–222] DES (2); ABS (1);
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 [223–230] DES (4); ABS (2);
mixed (1); Misc. (1)
Single department (2),
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 [231–235] DES (1); ABS (2);
Cross-institutional (5) Arena (2); Python (1); AnyLogic (1); NetLogo
These studies use simulations to support the
modeling and analysis of improvements in the
Supply chain [236–243] DES (5), gaming (1);
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
[244–258] DES (8); mixed (2);
Single department (3),
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.
[32,259–264] DES (4); gaming (2);
ABS (1); Misc. (3)
Microsoft Excel (2); Arena (1); MedModel (1);
ProModel (1); JADE (1); VBA (1); Misc. (3)
These studies use simulations for operational
[265–267] DES (1); Misc. (2) Cross-institutional (3) Arena (1); Misc. (2) These studies focus on the design of the
[27,268,269] SD (1); Misc. (2) Cross-department (1),
Qnet2000 (1); iThink (1); Misc. (1) Simulation is used for understanding the
interactive rule between service vendor and
Misc. [31,36,38,270–281] DES (4); SD (3); ABS
(1); gaming (1);
mixed (3); Misc. (4)
Single department (5),
Arena (4); AnyLogic (2); iThink (2); Simul8 (1); NetLogo (1); Microsoft Excel (1); Powersim (1); Misc.
Methodology [12–15,39,282–286,287–305]Reviews, surveys, and methodological reflections
and comparisons of logistics simulations in other
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 .
One paper investigated logistical outsourcing  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 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 . 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 . Bidding decisions
made by distributers and suppliers in the pharmaceutical
industry were studied in Jetly et al.’s work . 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
. 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 .
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 . 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 .
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 . Zulkepli
modeled an integrated ICU by combining system dy-
namics and discrete-event simulation . The greatest
advantage of hybrid modeling is the ability to integrate
different simulation approaches and empirical data from
different sources .
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
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 . We identified few publications on
this subject during this period. The period 2013–2017
showed the largest output, but the volume was still not able
to catch up with that of papers related to patient-centric
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 .
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 . By involving these operational experts in
participatory simulations, we can assess their percep-
tion of processes and healthcare system operations .
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
[26,44–46]. System dynamics and agent-based simulation
might require formal methods and mathematics pertain-
ing to system design, such as differential equations ,
decision theories , and game-based approaches .
Complex socio-technical systems, such as air traffic con-
trols , 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 , 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 .
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 yet—in many cases, the ana-
lysis of material-centric logistics is attached to a larger
research project pertaining to physical distribution and
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
Planning Reasoning, negotiation, distributed
Experience, awareness, perception
Lower boundary of
Qualitative workflow Casual loop Objected-oriented programming Low-tech material
Higher boundary of
Differential equations Agent system High-tech graphic and interaction
Applicable area All Staffing decision making,
Staffing decision making, transport,
hospital design, network distribution
Staffing decision making, supply
chain management, network
distribution and dispatching
Tools Arena, Simio, Simu8,
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
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 , which was noted by
Persson and Persson . Therefore, a synthesis of the
literature after 1998 should not distort the analysis.
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.
ABS: Agent-based simulation; DES: Discrete-event simulation;
Misc: Miscellaneous; Mixed: Hybrid simulation; SD: Systems dynamics
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.
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
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal
Institute of Technology, 2010, Röntgenvägen 1, 14152 Huddinge, Sweden.
School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal
Institute of Technology, Hälsovägen 11, 14152 Huddinge, Sweden.
Emergency Department, Karolinska University Hospital, Tomtebodavägen 18a,
17177 Stockholm, Sweden.
Department of Learning, Informatics, Management
and Ethics, Karolinska Institute, Tomtebodavägen 18a, 17177 Stockholm,
School of Industrial Engineering and Management, Royal Institute of
Technology, Mariekällgatan 3, 15144 Södertälje, Sweden.
Received: 6 March 2018 Accepted: 25 June 2018
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