Simulation Modeling in Healthcare Services
Arzu BulutThe health system is intricate due to its dynamic nature and critical service requirements. Process optimization for care delivery is becoming more and more important due to rising healthcare expenditures. To support clinical and policy decisions, modeling and simulation have become increasingly important as a means of understanding and predicting pathophysiology, disease development, and disease spread. Simulation is “the imitation of the operation of a real-world process or system over time.” It is a simplified model of a system of interest used to study specific aspects of the system. Simulation is a powerful tool for evaluating and analyzing new system designs, modifications to existing systems, and proposed changes to control systems and operating rules. Simulation has been used for modeling healthcare systems for over forty years. The use of modeling in healthcare is not limited to the management of activities necessary to deliver care alone. It is also used for the study of several topics related to healthcare, for example, air pollution, pharmacokinetics, and food poisoning. In healthcare settings in general, and emergency departments in particular, the use of simulation tools can help improve patient flow and optimize resource utilization, which in turn improves patient treatment processes and satisfaction. The four primary simulation techniques are Agent-Based Simulation, Discrete-Event Simulation (DES), Monte Carlo Simulation (MCS), and System Dynamies (SD). This chapter will give information about the simulation notion and simulation modeling techniques, its uses in health care, and reflections on the healthcare system.
Referanslar
- Abdelghany, M., & Eltawil, A. B. (2017). Linking approaches for multi-methods simulation in healthcare systems plan-ning and management. International Journal of Industrial and Systems Engineering, 26(2), 275-290. google scholar
- Abidovna, A. S. (2023). Monte Carlo modeling and its peculiarities in the implementation of marketing analysis in the activities of the enterprise. Gospodarka i Innowacje., 42, 375-380. google scholar
- Adeyemi, s., Demir, e., Yakutcan, U., Adeoti, A., Pascal Kengne, A., Gbenga Kayode, A., ... & Isichei, C. (2021). SmartHIV Manager: a web-based computer simulation system for better management of HIV services. Journal of Public Health and Emergency, 1-9. google scholar
- Aggarwal, R., & Darzi, A. (2006). Training in the operating theatre: is it safe?. Thorax, 61(4), 278-279. google scholar
- Allahi, F., De Leeuw, S., Sabet, E., Kian, R., Damiani, L., Giribone, P., . & Cianci, R. (2018). A review of sYstem dYnamics models applied in social and humanitarian researches. In: S.I. Ao, L. Gelman, D.W.L. Hukins, A. Hunter and A.M. Korsunski, eds., Proceedings of the World Congress on Engineering 2018 (WCE 2018), Imperial College London, London, 4-6 JulY 2018. Vol. 2. Hong Kong: Newswood Limited on behalf of International Association of Engineers, pp. 789-794. https://www.iaeng.org/publication/WCE2018/WCE2018_pp789-794.pdf [Accessed 27 April 2024]. google scholar
- Alwabel, A. S., & Zeng, X. J. (2021). Data-driven modeling of technologY acceptance: A machine learning perspective. Expert SYstems with Applications, 185, 115584. google scholar
- Andreatta, P. B., Woodrum, D. T., BirkmeYer, J. D., Yellamanchilli, R. K., DohertY, G. M., Gauger, P. G., & Minter, R. M. (2006). Laparoscopic skills are improved with LapMentor™ training: results of a randomized, double-blinded studY. Annals of Surgery, 243(6), 854-863. google scholar
- Azar, A. T. (2012). SYstem dYnamics as a useful technique for complex sYstems. International Journal of Industrial and Systems Engineering, 10(4), 377-410. google scholar
- Banks, C. M. (2009). What is modeling and simulation?. Principles of modeling and simulation: A multidisciplinarY approach, 3-24. A John WileY & Sons, Inc., Publication google scholar
- Barber, P., & Löpez-Valcârcel, B. G. (2010). Forecasting the need for medical specialists in Spain: Application of a system dYnamics model. Human Resources for Health, 8, 1-9. google scholar
- Barrows, H. S. (1993). An overview of the uses of standardized patients for teaching and evaluating clinical skills. AAMC. Academic medicine, 68(6), 443-51. google scholar
- Bayer, S., Petsoulas, C., Cox, B., Honeyman, A., & Barlow, J. (2010). Facilitating stroke care planning through simulation modelling. Health Informatics Journal, 16(2), 129-143. google scholar
- Berger-Estilita, J. M., Noronha, A. B., Goltz, K., Berger, D., & Greif, R. (2023). Simulation in cardiac surgery: current evi-dence. Signa Vitae. google scholar
- Beshgetoor, D., & Wade, D. (2007). Use of actors as simulated patients in nutritional counseling. Journal of Nutrition Education snd Behavior, 39(2), 101-102. google scholar
- Bradley, P. (2006). The history of simulation in medical education and possible future directions. Medical Educati-on, 40(3), 254-262. google scholar
- Brice, S. N., Harper, P. R., Gartner, D., & 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, 1011104. google scholar
- Carson, J. S. (2005). Introduction to modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005. (pp. 8-pp). IEEE. google scholar
- Chahal, K., Eldabi, T., & Young, T. (2013). A conceptual framework for hybrid system dynamics and discrete event simu-lation for healthcare. Journal of Enterprise Information Management, 26(1/2), 50-74. google scholar
- Chemweno, P., Thijs, V., Pintelon, L., & Van Horenbeek, A. (2014). Discrete Event Simulation Case Study: Diagnostic Path for Stroke Patients in A Stroke Unit. Simulation Modelling Practice and Theory, 48, 45-57. google scholar
- Chen, I. H. A., Ghazi, A., Sridhar, A., Stoyanov, D., Slack, M., Kelly, J. D., & Collins, J. W. (2021). Evolving robotic surgery training and improving patient safety, with the integration of novel technologies. World Journal of Urology, 39, 2883-2893. google scholar
- Chen, Yan, Yong-Hong Kuo, Hari Balasubramanian, and Chaobai Wen. “Using simulation to examine appointment over-booking schemes for a medical imaging center.” In 2015 Winter simulation conference (WSC), pp. 1307-1318. IEEE, 2015 google scholar
- Coelli, F. C., Ferreira, R. B., Almeida, R. M., & Pereira, W. C. (2007). Computer Simulation and Discrete-Event Models in The Analysis of A Mammography Clinic Patient Flow. Comput Methods Programs Biomed, 87(3), 201-207. google scholar
- Cohen, E. R., Feinglass, J., Barsuk, J. H., Barnard, C., O’Donnell, A., McGaghie, W. C., & Wayne, D. B. (2010). Cost savings from reduced catheter-related bloodstream infection after simulation-based education for residents in a me-dical intensive care unit. Simulation in Healthcare, 5(2), 98-102. google scholar
- Cohen, J., Cohen, s. A., Vora, K. C., Xue, X., Burdick, J. s., Bank, s., ... & Villanueva, G. (2006). Multicenter, randomized, controlled trial of virtual-reality simulator training in acquisition of competency in colonoscopy. Gastrointesti-nal Endoscopy, 64(3), 361-368. google scholar
- Collins, A. J., sabz Ali Pour, F., & Jordan, C. A. (2023). Past challenges and the future of discrete event simulation. The Journal of Defense Modeling and Simulation, 20(3), 351-369. google scholar
- Crew B (2020) Worth the cost? A closer look at the da Vinci robot’s impact on prostate cancer surgery. Nat Index. google scholar
- Currie, C. s., 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. google scholar
- Davidsson, P., & Verhagen, H. (2017). Types of simulation. simulating social Complexity: A Handbook, 23-37. springer. google scholar
- Delp, s. L., Loan, J. P., Hoy, M. G., Zajac, F. E., Topp, E. L., & Rosen, J. M. (1990). An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Transactions on Biomedical engineering, 37(8), 757-767. google scholar
- Devapriya, P., strömblad, C. T., Bailey, M. D., Frazier, s., Bulger, J., Kemberling, s. T., & Wood, K. E. (2015). stratBAM: a discrete-event simulation model to support strategic hospital bed capacity decisions. Journal of Medical Sys-tems, 39, 1-13. google scholar
- dos santos, V. H., Kotiadis, K., & scaparra, M. P. (2020). A review of hybrid simulation in healthcare. In 2020 winter simulation conference (wsc) (pp. 1004-1015). 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 (WSC) (pp. 1500-1514). IEEE. google scholar
- Epps, C., White, M. L., & Tofil, N. (2013). Mannequin based simulators. The Comprehensive Textbook of Healthcare Si-mulation, 209-232. google scholar
- Evenden, D., Harper, P. R., Brailsford, S. C., & Harindra, V. (2005). System Dynamics modeling of Chlamydia infection for screening intervention planning and cost-benefit estimation. IMA Journal of Management Mathematics, 16(3), 265-279. google scholar
- Fitzgerald, K., Pelletier, L., & Reznek, M. A. (2017). A queue-based Monte Carlo analysis to support decision making for implementation of an emergency department fast track. Journal of Healthcare Engineering, 2017, 1-8 google scholar
- Gaba, D. M., & DeAnda, A. (1988). A comprehensive anesthesia simulation environment: re-creating the operating room for research and training. Anesthesiology, 69(3), 387-394. google scholar
- Good, M. L. (1990). Anaesthesia simulators and training devices. Anaesthesia, 45(7), 525-526. google scholar
- Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: a review of the literature. Journal of Simulation, 4, 42-51. google scholar
- Gupta, U. G. (1997). Using citation analysis to explore the intellectual base, knowledge dissemination, and research impact of Interfaees (1970-1992). Interfaces, 27(2), 85-101. google scholar
- Gurusamy, K., Aggarwal, R., Palanivelu, L., & Davidson, B. R. (2008). Systematic review of randomized controlled trials on the effectiveness of virtual reality training for laparoscopic surgery. Journal of British Surgery, 95(9), 1088-1097. google scholar
- Heitzmann N., Seidel T., Opitz A., Hetmanek A., Wecker C., Fischer M., Ufer S., Schmidmaier R., Neuhaus B., Siebeck M., Stürmer K., Obersteiner A., Reiss K., Girwidz R., Fischer F. (2019). Facilitating diagnostic competences in simula-tions: A conceptual framework and a research agenda for medical and teacher education. Frontline Learning Research, 7(4), 1-24. google scholar
- Henderson, S. G., Biller, B., Hsieh, M. H., Shortle, J., Tew, J. D., Barton, R. R., & Brailsford, S. (2007). Tutorial: Advances and challenges in healthcare simulation modeling. In Proceedings of the 2007 Winter Simulation Conference, Washington, DC, USA (pp. 9-12). google scholar
- Hoot, N. R., LeBlanc, L. J., Jones, I., Levin, S. R., Zhou, C., Gadd, C. S., & Aronsky, D. (2008). Forecasting Emergency De-partment Crowding: A Discrete Event Simulation. Ann Emerg Med, 52(2), 116-125. google scholar
- Ingalls, R. (2011). Introduction to simulation. Proceedings - Winter Simulation Conference. 1374-1388. google scholar
- Irvine, S., & Martin, J. (2014). Bridging the gap: from simulation to clinical practice. The Clinical Teacher, 11(2), 94-98. google scholar
- Jain, A. (2009). Simulation in medical education: Scholarly report on ML Web assignment google scholar
- Karadayi, M. A., Gökmen, Y. G., Kasap, L. G., & Tozan, H. (2019). Sağlıkta güncel simülasyon yaklaşımları: Bir derleme çalışması. International Journal of Advances in Engineering and Pure Sciences, 31(1), 1-16. google scholar
- Kasaie, P., Dowdy, D. W., & Kelton, W. D. (2013, December). An agent-based simulation of a tuberculosis epidemic: un-derstanding the timing of transmission. In 2013 Winter Simulations Conference (WSC) (pp. 2227-2238). IEEE. google scholar
- Ker, J., & Bradley, P. (2010). Simulation in medical education. Understanding medical education: Evidence, Theory and Practice, 164-180. google scholar
- Khalifa, Y. M., Bogorad, D., Gibson, V., Peifer, J., & Nussbaum, J. (2006). Virtual reality in ophthalmology training. Survey of Ophthalmology, 51(3), 259-273. google scholar
- Khurma, N., Bacioiu, G. M., & Pasek, Z. J. (2008). Simulation-Based Verification of Lean Improvement for Emergency Room Process. Proceedings of the 40th Conference on Winter Simulation Conference, 1490-1499. google scholar
- Kneebone, R. (2003). Simulation in surgical training: educational issues and practical implications. Medical Educati-on, 37(3), 267-277. google scholar
- Kneebone, R., Arora, S., King, D., Bello, F., Sevdalis, N., Kassab, E., ... & Nestel, D. (2010). Distributed simulation-acces-sible immersive training. Medical Teacher, 32(1), 65-70. google scholar
- Krishnan, D. G., Keloth, A. V., & Ubedulla, S. (2017). Pros and cons of simulation in medical education: A review. Educa-tion, 3(6), 84-87. google scholar
- Lahanas, V., Georgiou, E., & Loukas, C. (2016). Surgical simulation training systems: box trainers, virtual reality and augmented reality simulators. Int. J. Adv. Robot. Autom, 1(2), 1-9. google scholar
- Lane, C., & Rollnick, S. (2007). The use of simulated patients and role-play in communication skills training: a review of the literature to August 2005. Patient Education and Counseling, 67(1-2), 13-20. google scholar
- Lane, D. C., Monefeldt, C., & Rosenhead, J. V. (2000). Looking in the wrong place for healthcare improvements: A sys-tem dynamics study of an accident and emergency department. Journal of the operational Research Society, 51, 518-531. google scholar
- Lane, M. s., Mansour, A. H., & Harpell, J. L. (1993). Operations researeh techniques: A longitudinal update 1973-1988. Interfaces, 23(2), 63-68. google scholar
- Law, A. M., Kelton, W. D., & Kelton, W. D. (2007). simulation modeling and analysis (Vol. 3). New York: Mcgraw-hill. google scholar
- Lesosky, M., McGeer, A., simor, A., Green, K., Low, D. E., & Raboud, J. (2011). Effect of patterns of transferring patients among healthcare institutions on rates of nosocomial methicillin-resistant staphylococcus aureus transmissi-on: a Monte Carlo simulation. Infection Control & Hospital Epidemiology, 32(2), 136-147. google scholar
- Liu, P., & Wu, s. (2016). An agent-based simulation model to study accountable care organizations. Health Care Mana-gement Science, 19, 89-101. google scholar
- Lopes, M. A., Almeida, Â. S., & Almada-Lobo, B. (2018). Forecasting the medical workforce: a stochastic agent-based simulation approach. Health Care Management Science, 21, 52-75. google scholar
- Lovegrove, C., Novara, G., Mottrie, A., Guru, K. A., Brown, M., Challacombe, B., ... & Ahmed, K. (2016). Structured and modular training pathway for robot-assisted radical prostatectomy (RARP): validation of the RARP assessment score and learning curve assessment. European Urology, 69(3), 526-535. google scholar
- Macal, C., & North, M. (2010). Tutorial on agent-based modelling and simulation. J Simul, 4, 151-162. google scholar
- Mahmud, R., & Buyya, R. (2019). Modeling and simulation of fog and edge computing environments using the iFogSim toolkit. Fog and edge computing: Principles and paradigms, 1. google scholar
- Maran, N. J., & Glavin, R. J. (2003). Low-to high-fidelity simulation-a continuum of medical education?. Medical Educa-tion, 37, 22-28. google scholar
- Maria, A. (1997). Introduction to modeling and simulation. In Proceedings of the 29th conference on Winter simulation (pp. 7-13). google scholar
- McGaghie, W. C., & Issenberg, S. B. (2009). Simulations in assessment. Assessment in Health Professions Education, 245-268. google scholar
- Metropolis, N., & Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247), 335-341. google scholar
- Mielczarek, B., & Zabawa, J. (2007). Monte Carlo simulation model to study the inequalities in access to EMS services. In Proceedings 21st European Conference on Modelling and Simulation, EMCS (pp. 1-6). google scholar
- Mielczarek, B. (2016). Review of modelling approaches for healthcare simulation. Operations Research and Decisions, 26, 55-72. google scholar
- Mingers, J., & White, L. (2010). A review of the recent contribution of systems thinking to operational research and management science. European Journal of Operational Research, 207(3), 1147-1161. google scholar
- Mustafee, N., Brailsford, S., Djanatliev, A., Eldabi, T., Kunc, M., & Tolk, A. (2017). Purpose and benefits of hybrid simulati-on: contributing to the convergence of its definition. In 2017 winter simulation conference (WSC) (pp. 1631-1645). IEEE. google scholar
- Mustafee, N., Katsaliaki, K., & Taylor, S. J. (2010). Profiling literature in healthcare simulation. Simulation, 86(8-9), 543-558. google scholar
- Nance, R. E. (1996). A history of discrete event simulation programming languages. In History of programming langu-ages—II (pp. 369-427). google scholar
- Nianogo, R. A., & Arah, O. A. (2015). Agent-based modeling of noncommunicable diseases: a systematic review. Ameri-can Journal of Public Health, 105(3), e20-e31. google scholar
- Nilsson, P. M., Russell, L., Ringsted, C., Hertz, P., & Konge, L. (2015). Simulation-based training in flexible fibreoptic in-tubation: a randomised study. European Journal of Anaesthesiology| EJA, 32(9), 609-614. google scholar
- Norman, G. R., Tugwell, P., & Feightner, J. W. (1982). A comparison of resident performance on real and simulated pati-ents. Academic Medicine, 57(9), 708-15. google scholar
- Ouda, E., Sleptchenko, A., & Simsekler, M. C. E. (2023). Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments. Simulation Modelling Practice and Theory, 102823 google scholar
- Oxford English Dictionary (OED) Online (2024). https://www.oed.com [Accessed 25 February 2024]. google scholar
- Patlolla, P., Gunupudi, V., Mikler, A. R., & Jacob, R. T. (2006). Agent-based simulation tools in computational epidemi-ology. In Innovative Internet Community Systems: 4th International Workshop, IICS 2004, Guadalajara, Mexico, June 21-23, 2004. Revised Papers 4 (pp. 212-223). Springer Berlin Heidelberg. google scholar
- Pearce, E., & Sivaprasad, S. (2020). A review of advancements and evidence gaps in diabetic retinopathy screening models. Clinical Ophthalmology, 3285-3296. google scholar
- Peng, Y., Qu, X., & Shi, J. (2014). A hybrid simulation and genetic algorithm approach to determine the optimal sche-duling templates for open access clinics admitting walk-in patients. Computers & Industrial Engineering, 72, 282-296. google scholar
- Pentland, A., & Liu, A. (1999). Modeling and prediction of human behavior. Neural Computation, 11(1), 229-242. google scholar
- Pessöa, L. a. M., Lins, M. P. e., Da Silva, A. C. M., & Fiszman, R. (2015). Integrating soft and hard operational researeh to improve surgical centre management at a university hospital. European Journal of Operational Research, 245(3), 851-861. google scholar
- Platz, A., & Knapheide, C. (2000). Interaetive design using the example of a eomplex medieal applieation. International Journal of Human-Computer Interaction, 12(3-4), 431-440. google scholar
- Pombo-Romero, J., Varela, L. M., & Rieoy, C. J. (2013). Diffusion of innovations in soeial interaetion systems. An agent-ba-sed model for the introduetion of new drugs in markets. The European Journal of Health Economics, 14, 443-455. google scholar
- Qureshi, S. M., Purdy, N., Mohani, A., & Neumann, W. P. (2019). Predieting the effeet of nurse-patient ratio on nurse workload and eare quality using diserete event simulation. Journal of Nursing Management, 27(5), 971-980. google scholar
- Railsbaek, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a praetieal introduetion. Prineeton University Press. google scholar
- Rangarajan, K., Davis, H., & Pueher, P. H. (2020). Systematie review of virtual hapties in surgieal simulation: a valid edueational tool?. Journal of Surgical Education, 77(2), 337-347. google scholar
- Rashwan, W., Abo-Hamad, W., & Arisha, A. (2015). A system dynamies view of the aeute bed bloekage problem in the Irish healtheare system. European Journal of Operational Research, 247(1), 276-293. google scholar
- Rezniek, R. K., & MaeRae, H. (2006). Teaehing surgieal skills—ehanges in the wind. New England Journal of Medicine, 355(25), 2664-2669. google scholar
- Riehter, A., & Mauskopf, J. A. (1998). Mm1 Monte Carlo simulation in health eare models. Value in Health, 1(1), 84-85. google scholar
- Ridge, J. C., Jones, S. K., Nielsen, M. S., & Shahani, A. K. (1998). Capaeity planning for intensive eare units. European Journal of Operational Research, 105(2), 346-355. google scholar
- Roberts, S. D. (2011). Tutorial on the simulation of healtheare systems. In Proeeedings of the 2011 winter simulation eonferenee (wse) (pp. 1403-1414). IEEE. google scholar
- Robertson, K., Hegarty, K., O’Connor, V., & Gunn, J. (2003). Women teaehing women’s health: issues in the establishment of a elinieal teaehing assoeiate program for the well woman eheek. Women & Health, 37(4), 49-65. google scholar
- Salmon, A., Raehuba, S., Briseoe, S., & Pitt, M. (2018). A struetured literature review of simulation modelling applied to Emergeney Departments: Current patterns and emerging trends. Operations Research for Health Care, 19, 1-13. google scholar
- Satava, R. M. (1993). Virtual reality surgieal simulator: the first steps. Surgical Endoscopy, 7, 203-205. google scholar
- Satava, R. M. (2001). Surgieal edueation and surgieal simulation. World Journal of Surgery, 25(11), 1484-1489. google scholar
- Shanthikumar, J. G., & Sargent, R. G. (1983). A unifying view of hybrid simulation/analytie models and modeling. Ope-rations Research, 31(6), 1030-1052. google scholar
- Taboada, M., Cabrera, E., Epelde, F., Iglesias, M. L., & Luque, E. (2013). Using an agent-based simulation for predieting the effeets of patients derivation polieies in emergeney departments. Procedia Computer Science, 18, 641-650. google scholar
- Tako, A. A., & Robinson, S. (2012). The applieation of diserete event simulation and system dynamies in the logisties and supply ehain eontext. Decision Support Systems, 52(4), 802-815. google scholar
- Tan, Y., Jiao, L., Shuai, C., & Shen, L. (2018). A system dynamies model for simulating urban sustainability performanee: A China ease study. Journal of Cleaner Production, 199, 1107-1115. google scholar
- Thorwarth, M. (2011) A Simulation-based Deeision Support System to Improve Healtheare Faeilities Performanee - ela-borated on an Irish Emergeney Department. Doetoral Thesis. Teehnologieal University Dublin. google scholar
- Thorwarth, M., Rashwan, W., & Arisha, A. (2016). An analytieal representation of flexible resouree alloeation in hospitals. Flexible Services and Manufacturing Journal, 28(1), 148-165. google scholar
- Türker, E., & Tanrıkulu, Y. (2020). Kardiyopulmoner resüsitasyonda haptik simülasyon kullanımı. Paramedik ve Acil Sağlık Hizmetleri Dergisi, 1(2), 65-72. google scholar
- Watson, M. C., Norris, P., & Granas, A. G. (2006). A systematic review of the use of simulated patients and pharmacy practice research. International Journal of Pharmacy Practice, 14(2), 83-93. google scholar
- Wayne, D. B., Didwania, A., Feinglass, J., Fudala, M. J., Barsuk, J. H., & McGaghie, W. C. (2008). Simulation-based education improves quality of care during cardiac arrest team responses at an academic teaching hospital: a case-control study. Chest, 133(1), 56-61. google scholar
- Weller, J. M., Nestel, D., Marshall, S. D., Brooks, P. M., & Conn, J. J. (2012). Simulation in clinical teaching and learning-. Medical Journal of Australia, 196(9), 594-594. google scholar
- Wind, L. A., Van Dalen, J., Muijtjens, A. M., & Rethans, J. J. (2004). Assessing simulated patients in an educational setting: the MaSP (Maastricht Assessment of Simulated Patients). Medical Education, 38(1), 39-44. google scholar
- Yalçintürk, A. A., & Dikeç, G. (2021). Geropsikiyatri Hemşireliğinde Simülasyon Kullanımı. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 3(1), 29-36. google scholar
- Zeng, Z., Ma, X., Hu, Y., Li, J., & Bryant, D. (2012). ASimulation Study to Improve Quality of Care in The Emergency De-partment of A Community Hospital. J Emerg Nurs, 38(4), 322-328. google scholar