Hybrid simulation in healthcare: a systematic exploration of models, applications, and emerging trends

Figures & data

Table 1. The limitation of M&S methods used for healthcare problems.

M&S Approach	Limitation	Sample studies
DES	Complicated to represent social behaviour. Require a significant amount of data. Mostly capture detailed complexity.	S. Brailsford and Schmidt (2003), Jacobson et al. (2013), Chahal et al. (2013)
SD	Not suitable for a detailed perspective. Mostly capture dynamic complexity. Mainly deal with macro-level problems.	Viana et al. (2014), Eldabi (2021), S. C. Brailsford et al. (2010)
ABS	Individual-level perspective. Involves high skills in computation. Difficult to process visually.	Barnes et al. (2013), Eldabi (2021), Joo et al. (2013)

Table 2. Literature search for hybrid simulation in healthcare.

Category	Search Terms
Hybrid Simulation	(“Hybrid Simulation”) OR
Combined Simulations	[(“Discrete Event Simulation” AND “System Dynamics”) OR (“Discrete Event Simulation” AND “System Dynamics” AND “Agent-Based Simulation”) OR (“Discrete Event Simulation” AND “Agent Based Simulation”) OR (“Agent-Based Simulation” AND “System Dynamics”)]
Healthcare	AND (“Healthcare” OR “Disease” OR “Medic*”)

Figure 1. Phases of systematic literature review.

Table 3. PPMO framework (adapted from Mustafee et al. (2021)).

(A) Profiling Research (P)	(B) Problem Definition and Context (P)
Publication characteristics (publication year, publication type etc.) Type of paper (literature review paper, application paper, framework)	The motivation of hybridisation Context of application Case study
(C) Model Development and Implementation (M)	(D) Study Outcome (O)
A hybrid approach/combination M&S software and other tools Linking approaches The way of model connections Verification and validation	Level of implementation Future research direction

Figure 2. A synopsis of HS literature in healthcare.

Table 4. The motivation of Hybridisation.

Motivation	Description	Example Studies
Broad Representation/Complex System Representation	Interconnectivity is a primary driver for using HS, as it allows for a more comprehensive representation of the system rather than focusing on isolated parts.	Ahmad et al. (2014), Bell et al. (2016), Djanatliev and Meier (2016), Viana et al. (2012)
Improve Decision Making	A significant utility of simulation is its role in enhancing decision-making processes. HS improve decision-making capabilities, making it a key motivation for its application.	Nouman et al. (2013), Zulkepli and Eldabi (2016), Mielczarek and Zabawa (2018), Li, Zhang, Chi, et al. (2020), Zhou et al. (2023)
Increase Accuracy	HS can potentially offer greater modelling accuracy when compared to singular simulation methods. This enhancement in accuracy is a notable reason for the application of HS.	Lassnig et al. (2019), Elliott et al. (2019), D. Evenden et al. (2020), Hamza et al. (2021)
Innovation/ Policy Testing	HS, with its capacity to integrate both holistic and detailed views of systems, offers improved insights into policy design. This makes policy testing a significant motivator for HS use.	Djanatliev and German (2013), Kolominsky-Rabas et al. (2015), Day et al. (2014)
Reusability	The ability to reuse system components is increasingly valued, especially given the time-intensive nature of developing HS models from scratch. Some studies have focused on demonstrating how HS can facilitate reusability	Anagnostou and Taylor (2014), Lather and Eldabi (2020)

Table 5. HS frameworks in healthcare.

Authors	Contribution	Framework Components	Used Methods
Chahal and Eldabi (2008)	It proposed three-way of model a combination	Hierarchical Mode Process-Environment Mode Integration	DES-SD
Chahal et al. (2013)	It improved the previous framework by defining the information flow	Parallel Interaction Cyclic Interaction	DES-SD
Zulkepli and Eldabi (2015)	It is a guiding framework for the conceptual phase	Conceptual Phase Modelling Phase Model Communication Phase	DES-SD
Fakhimi (2016)	It considers sustainability factors for complex healthcare modelling.	Conceptual Phase Design and Combination Phase TBL Analysis Phase	DES-SD
Djanatliev and German (2015)	It introduces domain-specific HS.	Level Identification Horizontal Paradigm Linking Vertical Paradigm Linking	DES-SD-ABS

Table 6. Combination of DES and SD Application¹.

Studies	Method	Linking	Validate/Verify	Software
J. Morgan et al. (2011)	Sequential	NA	NA	Vensim/Simul8
S. C. Brailsford et al. (2010)	Enriching	Intermediate Tool	FV/SM	Vensim/Simul8
Viana et al. (2014)	Interaction	Intermediate Tool	SM	Vensim/Simul8
Ahmad et al. (2012)	Interaction	Automated	FV	AnyLogic
Ahmad et al. (2013)	Interaction	Automated	FV	AnyLogic
Ahmad et al. (2014)	Interaction	Automated	FV	AnyLogic
Bell et al. (2016)	Interaction	NA	NA	WSP/Simul8
Mielczarek and Zabawa (2016)	Interaction	NA	SM	ExtendSim
Mielczarek and Zabawa (2021)	Sequential	NA	SM	ExtendSim/Arena
Mielczarek and Zabawa (2017)	NA	NA	NA	ExtendSim/Arena
Mielczarek and Zabawa (2018)	NA	NA	NA	ExtendSim/Arena
Mielczarek et al. (2018)	Sequential	NA	NA	ExtendSim/Arena
Zabawa and Mielczarek (2019)	Sequential	NA	SM	ExtendSim
Mielczarek (2019)	Sequential	Intermediate Tool	SM	ExtendSim/Arena
Zulkepli et al. (2012)	Interaction	Manual	NA	Vensim/Simul8
Zulkepli and Eldabi (2016)	Interaction	Manual	SM	Vensim/Simul8
J. S. Morgan et al. (2016)	Sequential	NA	NA	NA
Lather and Eldabi (2020)	NA	Manual	NA	Vensim/Simul8

^aNA: Not Available or Not Mentioned, FV: Face Validity, SM: Statistical Method EV: External Validity.

Table 7. Combination of DES and ABS.

Studies	Method	Linking	Validate/Verify	Software
S. C. Brailsford et al. (2013a)	Interaction	Automated	FV	AnyLogic
Anagnostou et al. (2013)	Interaction	Automated	NA	Repast Simphony/poRTIco
Nouman et al. (2013)	Integration	Automated	NA	Repast Simphony/poRTIco
Day et al. (2014)	Interaction	Automated	SM	AnyLogic
Fakhimi et al. (2014)	Sequential	Intermediate Tool	NA	Repast/Simul8
Kittipittayakorn and Ying (2016)	Enriching	Automated	SM	AnyLogic
Li, Zhang, Chi, et al. (2020)	NA	Automated	SM	AnyLogic
Hamza et al. (2021)	Interaction	Automated	SM	AnyLogic
Elliott et al. (2019)	Enriching	Automated	SM	AnyLogic
Viana et al. (2017)	NA	Automated	NA	AnyLogic
Viana et al. (2018)	Interaction	Automated	SM-FV	AnyLogic
Viana et al. (2020)	NA	Automated	FV	AnyLogic
Lassnig et al. (2019)	NA	Automated	SM	AnyLogic
Angelopoulou and Mykoniatis (2022)	NA	Automated	SM	AnyLogic
Anagnostou et al. (2022)	Sequential	N/A	N/A	Python-Simpy
Zhou et al. (2023)	N/A	Automated	SM	AnyLogic
Penny et al. (2023)	Enriching	Automated	SM	Simul8
Liu et al. (2023)	Enriching	Automated	N/A	AnyLogic

Table 8. Combination of SD and ABS.

Studies	Method	Linking	Validate/Verify	Software
Djanatliev et al. (2012)	Enriching	Automated	FV-SM	AnyLogic
Djanatliev and German (2013)	Interaction	Automated	FV-SM	AnyLogic
Djanatliev et al. (2014)	Enriching	Automated	NA	AnyLogic
Kolominsky-Rabas et al. (2015)	Enriching	Automated	FV	AnyLogic
D. Evenden et al. (2020)	Interaction	Automated	EV	AnyLogic
Evenden et al. (2021)	Enriching	Automated	EV	AnyLogic
Guo et al. (2021)	Enriching	Automated	SM	AnyLogic
Nguyen et al. (2022)	Interaction	Automated	FV-SM	AnyLogic
Brice et al. (2023)	Sequential	Automated	FV	AnyLogic

Table 9. Combination of DES, SD, and ABS.

Studies	Method	Linking	Validate/Verify	Software
Viana et al. (2012)	Enriching	Automated	NA	AnyLogic
Gao et al. (2014)	Interaction	Manual	SM	AnyLogic
Djanatliev and Meier (2016)	Enriching	Automated	SM	AnyLogic

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