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

References

• Abdel‐Hamid, T. K. (2002). Modeling the dynamics of human energy regulation and its implications for obesity treatment. System Dynamics Review: The Journal of the System Dynamics Society, 18(4), 431–471. https://doi.org/10.1002/sdr.240
   Web of Science ®Google Scholar
• Ahmad, N., Ghani, N. A., Kamil, A. A., & Mar Taha, R. (2012). Emergency department problems: A call for hybrid simulation. In Proceedings of the World Congress on Engineering (pp. 1–10). Newswood Ltd.
   Google Scholar
• Ahmad, N., Ghani, N. A., Kamil, A. A., & Mat Tahar, R. (2013). Modeling emergency department using a hybrid simulation approach. In G. Yang, S. Ao, L. Gelman (Eds.), IAENG Transactions on Engineering Technologies: Special Volume of the World Congress on Engineering 2012 (pp. 701–711). Springer Netherlands.
   Google Scholar
• Ahmad, N., Ghani, N. A., Kamil, A. A., & Tahar, R. M. (2014). Managing resource capacity using hybrid simulation. In AIP Conference Proceedings (pp. 504–511). American Institute of Physics Inc.
   Google Scholar
• Anagnostou, A., Groen, D., Taylor, S. J. E., Suleimenova, D., Abubakar, N., Saha, A., Mintram, K., Ghorbani, M., Daroge, H., Islam, T., Xue, Y., Okine, E., & Anokye, N. (2022). FACS-CHARM: A hybrid agent-based and discrete-event simulation approach for COVID-19 management at regional level. In 2020 Winter Simulation Conference (pp. 1223–1234). IEEE.
   Google Scholar
• Anagnostou, A., Nouman, A., & Taylor, S. J. E. (2013). Distributed hybrid agent-based discrete event emergency medical services simulation. In 2013 Winter Simulation Conference (pp. 1625–1636). IEEE.
   Google Scholar
• Anagnostou, A., & Taylor, S. J. E. (2014). Towards a methodology for building large-scale distributed hybrid agent-based and discrete-event simulations: The case of emergency medical services. In 2014 Operational Research Society Simulation Workshop, SW 2014 (pp. 14–25). Operational Research Society.
   Google Scholar
• Angelopoulou, A., & Mykoniatis, K. (2022). Hybrid modelling and simulation of the effect of vaccination on the COVID-19 transmission. Journal of Simulation, 18(1), 88–99. https://doi.org/10.1080/17477778.2022.2062260
   Web of Science ®Google Scholar
• Barlas, Y. (1996). Formal aspects of model validity and validation in system dynamics. System Dynamics Review: The Journal of the System Dynamics Society, 12(3), 183–210. https://doi.org/10.1002/(SICI)1099-1727(199623)12:3<183:AID-SDR103>3.0.CO;2-4
   Web of Science ®Google Scholar
• Barnes, S., Golden, B., & Price, S. (2013). Applications of agent-based modeling and simulation to healthcare operations management. In B.T Denton (Eds.), Handbook of healthcare operations management: Methods and applications (pp. 45–74). Springer New York.
   Google Scholar
• Bell, D., Cordeaux, C., Stephenson, T., Dawe, H., Lacey, P., & O’Leary, L. (2016). Designing effective hybridization for whole system modeling and simulation in healthcare. In 2016 Winter Simulation Conference (pp. 1511–1522). IEEE.
   Google Scholar
• Brailsford, S. C. (2007). Tutorial: Advances and challenges in healthcare simulation modeling. In 2007 Winter Simulation Conference (pp. 1436–1448). IEEE.
   Google Scholar
• Brailsford, S. C., Desai, S. M., & Viana, J. (2010). Towards the holy grail: Combining system dynamics and discrete-event simulation in healthcare. In 2010 Winter Simulation Conference (pp. 2293–2303). IEEE.
   Google Scholar
• Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721–737. https://doi.org/10.1016/j.ejor.2018.10.025
   Web of Science ®Google Scholar
• Brailsford, S., & Schmidt, B. (2003). Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation. European Journal of Operational Research, 150(1), 19–31. https://doi.org/10.1016/S0377-2217(02)00778-6
   Web of Science ®Google Scholar
• Brailsford, S. C., Viana, J., Rossiter, S., Channon, A. A., & Lotery, A. J. (2013a). Hybrid simulation for health and social care: The way forward, or more trouble than it’s worth? In 2013 Winter Simulation Conference (pp. 258–269). IEEE.
   Google Scholar
• Brailsford, S. C., Viana, J., Rossiter, S., Channon, A. A., & Lotery, A. J. (2013b). Hybrid simulation for health and social care: The way forward, or more trouble than it’s worth? In 2013 Winter Simulation Conference. (pp. 258–269). IEEE.
   Google Scholar
• Brice, S. N., Harper, P. R., Behrens, D. A., & Behrens, D. A. (2023). Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system dynamics. Frontiers in Public Health, 10(2023), 1011104. https://doi.org/10.3389/fpubh.2022.1011104
   PubMedGoogle Scholar
• Chahal, K., & Eldabi, T. (2008). Applicability of hybrid simulation to different modes of governance in UK healthcare. In 2008 Winter Simulation Conference. (pp. 1469–1477). IEEE.
   Google Scholar
• Chahal, K., & Eldabi, T. (2010). A multi-perspective comparison for selection between system dynamics and discrete event simulation. International Journal of Business Information Systems, 6(1), 4–17. https://doi.org/10.1504/IJBIS.2010.034001
   Google Scholar
• Chahal, K., & Eldabi, T. (2011). Hybrid simulation and modes of governance in UK healthcare. Transforming Government: People, Process and Policy, 5(2), 143–154. https://doi.org/10.1108/17506161111131177
   Google Scholar
• Chahal, K., Eldabi, T., Young, T., & Mustafee, N. (2013). A conceptual framework for hybrid system dynamics and discrete event simulation for healthcare. Journal of Enterprise Information Management, 26(1), 50–74. https://doi.org/10.1108/17410391311289541
   Google Scholar
• Cooke, D., Rohleder, T., & Rogers, P. (2010). A dynamic model of the systemic causes for patient treatment delays in emergency departments. Journal of Modelling in Management, 5(3), 287–301. https://doi.org/10.1108/17465661011092650
   Google Scholar
• Currie, C. S. M., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 14(2), 83–97. https://doi.org/10.1080/17477778.2020.1751570
   Web of Science ®Google Scholar
• Day, T. E., Ravi, N., Xian, H., & Brugh, A. (2014). Sensitivity of diabetic retinopathy associated vision loss to screening interval in an agent-based/discrete event simulation model. Computers in Biology and Medicine, 47(1), 7–12. https://doi.org/10.1016/j.compbiomed.2014.01.007
   PubMedGoogle Scholar
• Djanatliev, A., Bazan, P., & German, R. (2014). Partial paradigm hiding and reusability in hybrid simulation modeling using the frameworks Health-DS and i7-AnyEnergy. In 2014 Winter Simulation Conference. (pp. 1723–1734). IEEE.
   Google Scholar
• Djanatliev, A., & German, R. (2013). Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation. In 2013 Winter Simulation Conference (pp. 270–281). IEEE.
   Google Scholar
• Djanatliev, A., & German, R. (2015). Towards a guide to domain-specific hybrid simulation. In 2015 Winter Simulation Conference (pp. 1609–1620). IEEE.
   Google Scholar
• Djanatliev, A., German, R., Kolominsky-Rabas, P., & Hofmann, B. M. (2012). Hybrid simulation with loosely coupled system dynamics and agent-based models for Prospective Health Technology Assessments. In 2012 Winter Simulation Conference (pp. 1–12). IEEE.
   Google Scholar
• Djanatliev, A., & Meier, F. (2016). Hospital processes within an integrated system view: A hybrid simulation approach. In 2016 Winter Simulation Conference (pp. 1364–1375). IEEE.
   Google Scholar
• Dos Santos, V. H., Kotiadis, K., & Scaparra, M. P. (2020). A Review of Hybrid Simulation in Healthcare. In 2020 Winter Simulation Conference (pp. 1004–1015). IEEE.
   Google Scholar
• Eldabi, T. (2021). Systemic characteristics to support hybrid simulation modeling. In 2021 Winter Simulation Conference (pp. 1–10). IEEE.
   Google Scholar
• Eldabi, T., Balaban, M., Brailsford, S., Mustafee, N., Nance, R. E., Onggo, B. S., & Sargent, R. G. (2016). Hybrid simulation: Historical lessons, present challenges and futures. In 2016 Winter Simulation Conference (pp. 1388–1403). IEEE.
   Google Scholar
• Eldabi, T., Brailsford, S., Djanatliev, A., Kunc, M., Mustafee, N., & Osorio, A. F. (2018). Hybrid simulation challenges and opportunities: A life-cycle approach. In 2018 Winter Simulation Conference (pp. 1500–1514). IEEE.
   Google Scholar
• Elliott, T. M., Lord, A., Simms, L. A., Radford-Smith, G., Valery, P. C., & Gordon, L. G. (2019). Evaluating a risk assessment tool to improve triaging of patients to colonoscopies. Internal Medicine Journal, 49(10), 1292–1299. https://doi.org/10.1111/imj.14267
   PubMed Web of Science ®Google Scholar
• Evenden, D. C., Brailsford, S. C., Kipps, C. M., Roderick, P. J., & Walsh, B. (2021). Hybrid simulation modelling for dementia care services planning. Journal of the Operational Research Society, 72(9), 2147–2159. https://doi.org/10.1080/01605682.2020.1772020
   Web of Science ®Google Scholar
• Evenden, D., Brailsford, S., Kipps, C., Roderick, P., & Walsh, B. (2020). Computer simulation of dementia care demand heterogeneity using hybrid simulation methods: Improving population-level modelling with individual patient decline trajectories. Public Health, 186, 197–203. https://doi.org/10.1016/j.puhe.2020.07.018
   PubMed Web of Science ®Google Scholar
• Fakhimi, M. (2016). A Generic Hybrid Modelling and Simulation Framework for Sustainable Development Analysis in Healthcare Context [ Doctoral Dissertation]. University of Surrey United Kingdom.
   Google Scholar
• Fakhimi, M., Anagnostou, A., Stergioulas, L., & Taylor, S. J. E. (2014). A hybrid agent-based and Discrete Event Simulation approach for sustainable strategic planning and simulation analytics. In 2014 Winter Simulation Conference (pp. 1573–1584). IEEE.
   Google Scholar
• Fakhimi, M., Probert, J., & Mustafee, N. (2013). Operations research within UK healthcare: A review. Journal of Enterprise Information Management, 26(1), 21–49. https://doi.org/10.1108/17410391311289532
   Google Scholar
• Frerichs, L. M., Araz, O. M., Huang, T. T., & Pappalardo, F. (2013). Modeling social transmission dynamics of unhealthy behaviors for evaluating prevention and treatment interventions on childhood obesity. Public Library of Science ONE, 8(12), e82887. https://doi.org/10.1371/journal.pone.0082887
   Google Scholar
• Gao, A., Osgood, N. D., An, W., & Dyck, R. F. (2014). A tripartite hybrid model architecture for investigating health and cost impacts and intervention tradeoffs for diabetic end-stage renal disease. In Winter Simulation Conference (pp. 1676–1687). IEEE.
   Google Scholar
• Giannouchos, T. V., Biskupiak, J., Moss, M. J., Brixner, D., Andreyeva, E., & Ukert, B. (2021). Trends in outpatient emergency department visits during the COVID-19 pandemic at a large, urban, academic hospital system. The American Journal of Emergency Medicine, 40, 20–26. https://doi.org/10.1016/j.ajem.2020.12.009
   PubMed Web of Science ®Google Scholar
• Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17–25. https://doi.org/10.1057/hs.2012.8
   Google Scholar
• Guo, X., Tong, J., Chen, P., Fan, W., & Cherifi, H. (2021). The suppression effect of emotional contagion in the COVID-19 pandemic: A multilayer hybrid modelling and simulation approach. Public Library of Science ONE, 16(7), e0253579. https://doi.org/10.1371/journal.pone.0253579
   Google Scholar
• Hamza, N., Majid, M. A., & Hujainah, F. (2021). SIM-PFED: A simulation-based decision making model of patient flow for improving patient throughput time in emergency department. IEEE Access, 9, 103419–103439. https://doi.org/10.1109/ACCESS.2021.3098625
   Web of Science ®Google Scholar
• Harper, A., Mustafee, N., & Feeney, M. (2017). A hybrid approach using forecasting and discrete-event simulation for endoscopy services. In 2017 Winter Simulation Conference (pp. 1583–1594). IEEE.
   Google Scholar
• Helal, M., Rabelo, L., Sepúlveda, J., & Jones, A. (2007). A methodology for integrating and synchronizing the system dynamics and discrete event simulation paradigms. Proceedings of the 25th International Conference of the System Dynamics Society, 3(3), 1–24.
   Google Scholar
• Homer, J. B., & Hirsch, G. B. (2006). System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96(3), 452–458. https://doi.org/10.2105/AJPH.2005.062059
   PubMed Web of Science ®Google Scholar
• Jacobson, S. H., Hall, S. N., & Swisher, J. R. (2013). Discrete-event simulation of health care systems. In R. Hall (Eds.), International Series in Operations Research and Management Science (pp. 273–309). Springer.
   Google Scholar
• Jalali, M., Rahmandad, H., Bullock, S., & Ammerman, A. (2014). Dynamics of obesity interventions inside organizations. In The 32nd International Conference of the System Dynamics Society, System Dynamics Society.
   Google Scholar
• Jones, S. S., & Evans, R. S. (2008, November 8–12). An agent based simulation tool for scheduling emergency department physicians. In AMIA 2008, American Medical Informatics Association Annual Symposium Washington, DC, USA: AMIA,
   Google Scholar
• Joo, J., Kim, N., Wysk, R. A., Rothrock, L., Son, Y. J., Oh, Y. G., & Lee, S. (2013). Agent-based simulation of affordance-based human behaviors in emergency evacuation. Simulation Modelling Practice and Theory, 32, 99–115. https://doi.org/10.1016/j.simpat.2012.12.007
   Web of Science ®Google Scholar
• Kar, E., Eldabi, T., & Fakhimi, M. (2022). Hybrid simulation in healthcare: A review of the literature. In 2022 Winter Simulation Conference (pp. 1211–1222). IEEE.
   Google Scholar
• Katsaliaki, K., & Mustafee, N. (2011). Applications of simulation within the healthcare context. Journal of the Operational Research Society, 62(8), 1431–1451. https://doi.org/10.1057/jors.2010.20
   PubMed Web of Science ®Google Scholar
• Kittipittayakorn, C., & Ying, K.-C. (2016). Using the integration of discrete event and agent-based simulation to enhance outpatient service quality in an orthopedic department. Journal of Healthcare Engineering, 2016, 1–8. https://doi.org/10.1155/2016/4189206
   Web of Science ®Google Scholar
• Kolominsky-Rabas, P. L., Djanatliev, A., Wahlster, P., Gantner-Bär, M., Hofmann, B., German, R., Sedlmayr, M., Reinhardt, E., Schüttler, J., Kriza, C., Niederländer, C., Prokosch, H. U., Lenz, R., Baumgärtel, P., Schöffski, O., Emmert, M., Meier, F., Aisenbrey, A. … Miethe, M. (2015). Technology foresight for medical device development through hybrid simulation: The ProHTA Project. Technological Forecasting & Social Change, 97, 105–114. https://doi.org/10.1016/j.techfore.2013.12.005
   Web of Science ®Google Scholar
• Landa, P., Sonnessa, M., Tànfani, E., & Testi, A. (2018). Multiobjective bed management considering emergency and elective patient flows. International Transactions in Operational Research, 25(1), 91–110. https://doi.org/10.1111/itor.12360
   Web of Science ®Google Scholar
• Lassnig, A., Rienmueller, T., Kramer, D., Leodolter, W., Baumgartner, C., & Schroettner, J. (2019). A novel hybrid modeling approach for the evaluation of integrated care and economic outcome in heart failure treatment. BMC Medical Informatics and Decision Making, 19(1), 1–18. https://doi.org/10.1186/s12911-019-0944-3
   PubMed Web of Science ®Google Scholar
• Lather, J. I., & Eldabi, T. (2020). The benefits of a hybrid simulation hub to deal with pandemics. In 2020 Winter Simulation Conference (pp. 992–1003). IEEE.
   Google Scholar
• Lättilä, L., Hilletofth, P., & Lin, B. (2010). Hybrid simulation models - When, why, how? Expert Systems with Applications, 37(12), 7969–7975. https://doi.org/10.1016/j.eswa.2010.04.039
   Web of Science ®Google Scholar
• Liu, Y., Moyaux, T., Bouleux, G., & Cheutet, V. (2023). Hybrid Simulation Modelling of Emergency Departments for Resource Scheduling. Journal of Simulation, (2023), 1–16. https://doi.org/10.1080/17477778.2023.2187321
   Google Scholar
• Li, Y., Zhang, Y., & Cao, L. (2020). Evaluation and selection of hospital layout based on an integrated simulation method. In 2020 Winter Simulation Conference (pp. 2560–2568). IEEE.
   Google Scholar
• Li, Y., Zhang, Y., Chi, H., Han, Y., & Yu, T. (2020). Scenario-based optimization strategy analysis on a gastroenterology clinic: Using an integrated simulation method. Construction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress, 706–714. https://doi.org/10.1061/9780784482865.075
   Google Scholar
• Mielczarek, B. (2019). Combining simulation techniques to understand demographic dynamics and forecast hospital demands. In 2019 Winter Simulation Conference (pp. 1114–1125). IEEE.
   Google Scholar
• Mielczarek, B., & Zabawa, J. (2016). Modeling healthcare demand using a hybrid simulation approach. In 2016 Winter Simulation Conference (pp. 1535–1546). IEEE.
   Google Scholar
• Mielczarek, B., & Zabawa, J. (2017). Simulation model for studying impact of demographic, temporal, and geographic factors on hospital demand. In 2017 Winter Simulation Conference (pp. 4498–4500). IEEE.
   Google Scholar
• Mielczarek, B., & Zabawa, J. (2018). Impact of population ageing on hospital demand. In SIMULTECH 2018 - Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 459–466). SciTePress.
   Google Scholar
• Mielczarek, B., & Zabawa, J. (2021). Modelling demographic changes using simulation: Supportive analyses for socioeconomic studies. Socio-Economic Planning Sciences, 74, 100938. https://doi.org/10.1016/j.seps.2020.100938
   Web of Science ®Google Scholar
• Mielczarek, B., Zabawa, J., & Dobrowolski, W. (2018). The impact of demographic trends on future hospital demand based on a hybrid simulation model. In 2018 Winter Simulation Conference (pp. 1476–1487). IEEE.
   Google Scholar
• Miksch, F., Jahn, B., Espinosa, K. J., Chhatwal, J., Siebert, U., Popper, N., & Maciel de Freitas, R. (2019). Why should we apply ABM for decision analysis for infectious diseases?—an example for dengue interventions. Public Library of Science ONE, 14(8), e0221564. https://doi.org/10.1371/journal.pone.0221564
   Google Scholar
• Miller, R. L., Levine, R. L., McNall, M. A., Khamarko, K., & Valenti, M. T. (2011). A dynamic model of client recruitment and retention in community-based HIV prevention programs. Health Promotion Practice, 12(1), 135–146. https://doi.org/10.1177/1524839909332137
   PubMedGoogle Scholar
• Morgan, J. S., Belton, V., & Howick, S. (2016). Lessons from mixing or methods in practice: Using DES and SD to explore a radiotherapy treatment planning process. Health Systems, 5(3), 166–177. https://doi.org/10.1057/hs.2016.4
   Web of Science ®Google Scholar
• Morgan, J. S., Howick, S., & Belton, V. (2017). A toolkit of designs for mixing discrete event simulation and system dynamics. European Journal of Operational Research, 257(3), 907–918. https://doi.org/10.1016/j.ejor.2016.08.016
   Web of Science ®Google Scholar
• Morgan, J., Howick, S., & Belton, V. (2011). Designs for the complementary use of system dynamics and discrete-event Simulation. In 2011 Winter Simulation Conference (pp. 2710–2722). IEEE.
   Google Scholar
• Mustafee, N., Brailsford, S., Djanatliev, A., Eldabi, T., Kunc, M., & Tolk, A. (2017). Purpose and benefits of hybrid simulation: Contributing to the convergence of its definition. In 2017 Winter Simulation Conference (pp. 1631–1645). IEEE.
   Google Scholar
• Mustafee, N., Katsaliaki, K., & Taylor, S. J. E. (2021). Distributed approaches to supply chain simulation. ACM Transactions on Modeling and Computer Simulation, 31(4), 1–31. https://doi.org/10.1145/3466170
   Web of Science ®Google Scholar
• Mustafee, N., Powell, J., Brailsford, S. C., Diallo, S., Padilla, J., & Tolk, A. (2015). Hybrid simulation studies and hybrid simulation systems: Definitions, challenges, and benefits. In 2015 Winter Simulation Conference (pp. 1678–1692). IEEE.
   Google Scholar
• Nguyen, L. K. N., Megiddo, I., & Howick, S. (2020). Simulation models for transmission of health care–associated infection: A systematic review. American Journal of Infection Control, 48(7), 810–821. https://doi.org/10.1016/j.ajic.2019.11.005
   PubMed Web of Science ®Google Scholar
• Nguyen, L. K. N., Megiddo, I., Howick, S., & Struchiner, C. J. (2022). Hybrid simulation modelling of networks of heterogeneous care homes and the interfacility spread of Covid-19 by sharing staff. PloS Computational Biology, 18(1), e1009780. https://doi.org/10.1371/journal.pcbi.1009780
   PubMed Web of Science ®Google Scholar
• Niyonkuru, D., & Wainer, G. A. (2015). Discrete-event modeling and simulation for embedded systems. Computing in Science & Engineering, 17(5), 52–63. https://doi.org/10.1109/MCSE.2015.89
   Web of Science ®Google Scholar
• Nouman, A., Anagnostou, A., & Taylor, S. J. E. (2013). Developing a distributed agent-based and DES simulation using poRtico and repast. In Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT (pp. 97–104). Institute of Electrical and Electronics Engineers Inc.
   Google Scholar
• Ormerod, P., & Rosewell, B. (2006). Validation and verification of agent-based models in the social sciences. In F. Squazzoni (Eds.), Epistemological Aspects of Computer Simulation in the Social Sciences Lecture Notes in Computer Science (Vol. 5466, pp. 130–140). Springer.
   Google Scholar
• Palmer, R., Fulop, N. J., & Utley, M. (2018). A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings. Health Systems, 7(1), 29–50. https://doi.org/10.1057/s41306-017-0024-9
   PubMed Web of Science ®Google Scholar
• Penny, K. E. E., Bayer, S., & Brailsford, S. (2023). A hybrid simulation approach for planning health and social care services. Journal of Simulation, 17(3), 312–325. https://doi.org/10.1080/17477778.2022.2035275
   Web of Science ®Google Scholar
• Raunak, M., & Olsen, M. (2015). Quantifying validation of discrete event simulation models. In 2014 Winter Simulation Conference (pp. 628–639). IEEE.
   Google Scholar
• Siebers, P. O., MacAl, C. M., Garnett, J., Buxton, D., & Pidd, M. (2010). Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation, 4(3), 204–210. https://doi.org/10.1057/jos.2010.14
   Web of Science ®Google Scholar
• Stephenson, B., Lanzas, C., Lenhart, S., Ponce, E., Bintz, J., Dubberke, E. R., & Day, J. (2020). Comparing intervention strategies for reducing Clostridioides difficile transmission in acute healthcare settings: An agent-based modeling study. BMC Infectious Diseases, 20(1), 1–17. https://doi.org/10.1186/s12879-020-05501-w
   Web of Science ®Google Scholar
• Sugiyama, T., Goryoda, S., Inoue, K., Sugiyama-Ihana, N., & Nishi, N. (2017). 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 Services Research, 17(1), 1–11. https://doi.org/10.1186/s12913-017-2784-0
   PubMedGoogle Scholar
• Sumari, S., Ibrahim, R., Zakaria, N. H., & Ab Hamid, A. H. (2013). Comparing three simulation model using taxonomy: System dynamic simulation, discrete event simulation and agent based simulation. International Journal of Management Excellence, 1(3), 54–59. https://doi.org/10.17722/ijme.v1i3.17
   Google Scholar
• Tako, A. A., Eldabi, T., Fishwick, P., Krejci, C. C., & Kunc, M. (2019). Panel - towards conceptual modeling for hybrid simulation: Setting the scene. In 2019 Winter Simulation Conference (pp. 1267–1279). IEEE.
   Google Scholar
• Tako, A. A., & Robinson, S. (2009). Comparing discrete-event simulation and system dynamics: Users’ perceptions. Journal of the Operational Research Society, 60(3), 296–312. https://doi.org/10.1057/palgrave.jors.2602566
   Web of Science ®Google Scholar
• Vasilakis, C., Sobolev, B. G., Kuramoto, L., & Levy, A. R. (2007). A simulation study of scheduling clinic appointments in surgical care: Individual surgeon versus pooled lists. Journal of the Operational Research Society, 58(2), 202–211. https://doi.org/10.1057/palgrave.jors.2602235
   Web of Science ®Google Scholar
• Viana, J., Brailsford, S. C., Harindra, V., & Harper, P. R. (2014). Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for chlamydia infection. European Journal of Operational Research, 237(1), 196–206. https://doi.org/10.1016/j.ejor.2014.02.052
   Web of Science ®Google Scholar
• Viana, J., Rossiter, S., Channon, A. A., Brailsford, S. C., & Lotery, A. (2012). A multi-paradigm, whole system view of health and social care for age-related macular degeneration. In 2012 Winter Simulation Conference (pp. 1–12). IEEE.
   Google Scholar
• Viana, J., Simonsen, T. B., Dahl, F. A., & Flo, K. (2018). A hybrid discrete event agent based overdue pregnancy outpatient clinic simulation model. In 2018 Winter Simulation Conference (pp. 1488–1499). IEEE.
   Google Scholar
• Viana, J., Simonsen, T. B., Faraas, H. E., Schmidt, N., Dahl, F. A., & Flo, K. (2020). Capacity and patient flow planning in post-term pregnancy outpatient clinics: A computer simulation modelling study. BMC Health Services Research, 20(1), 1–15. https://doi.org/10.1186/s12913-020-4943-y
   Web of Science ®Google Scholar
• Viana, J., Ziener, V. M., Holhjem, M. S., Ponton, I. G., Th⊘gersen, L. J., & Simonsen, T. B. (2017). Optimizing home hospital health service delivery in Norway using a combined geographical information system, agent based, discrete event simulation model. In 2017 Winter Simulation Conference (pp. 1658–1669). IEEE.
   Google Scholar
• Zabawa, J., & Mielczarek, B. (2019). Overcoming challenges in hybrid simulation design and experiment. In Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology–ISAT 2018: Part II. (pp. 207–217). Springer International Publishing.
   Google Scholar
• Zhou, Y., Viswanatha, A., Motaleb, A. A., Lamichhane, P., Chen, K. Y., Young, R., Gurses, A. P., & Xiao, Y. (2023). A predictive decision analytics approach for primary care operations management: A case study of double-booking strategy design and evaluation. Computers & Industrial Engineering, 177(2023), 109069. https://doi.org/10.1016/j.cie.2023.109069
   Google Scholar
• Zulkepli, J., & Eldabi, T. (2015). Towards a framework for conceptual model hybridization in healthcare. In 2015 Winter Simulation Conference (pp. 1597–1608). IEEE.
   Google Scholar
• Zulkepli, J., & Eldabi, T. (2016). Developing integrated patient pathways using hybrid simulation. AIP Conference Proceedings, 1782(1), 040022.
   Google Scholar
• Zulkepli, J., Eldabi, T., & Mustafee, N. (2012). Hybrid simulation for modelling large systems: An example of integrated care model. In 2012 Winter Simulation Conference (pp. 1–12). IEEE.
   Google Scholar