Improving emergency department resource planning: a multiple case study

References

• Ahmad, N., Ramli, R., Hew, J. Z., Teo, A. H., Ghani, N. A., & Abduljabbar, W. K. (2017). A generic simulation optimization model of emergency department resource capacity. Journal of Engineering and Applied Sciences, 12(6), 1558–1565.
   Google Scholar
• Ahmed, M. A., & Alkhamis, T. M. (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research, 198(3), 936–942.
   Web of Science ®Google Scholar
• Azadeh, A., Ahvazi, M. P., Haghighii, S. M., & Keramati, A. (2016). Simulation optimization of an emergency department by modeling human errors. Simulation Modelling Practice and Theory, 67, 117–136.
   Web of Science ®Google Scholar
• Barak-Corren, Y., Israelit, S. H., & Reis, B. Y. (2017). Progressive prediction of hospitalisation in the emergency department: Uncovering hidden patterns to improve patient flow. Emergency Medicine Journal : EMJ, 34(5), 308–314. emermed-2014.
   Web of Science ®Google Scholar
• Bergs, J., Heerinckx, P., & Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. International Emergency Nursing, 22(2), 112–115.
   PubMed Web of Science ®Google Scholar
• Bisschop, J. R. (2011). AIMMS 3.14: The user guide. Netherlands: Haarlem: Paragon Decision Technology BV.
   Google Scholar
• Boyle, J., Wallis, M., Jessup, M., Crilly, J., Lind, J., Miller, P., & Fitzgerald, G. (2008, August). Regression forecasting of patient admission data. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 3819–3822). Vancouver, BC: IEEE.
   Google Scholar
• Brasil. Ministério da Saúde. Portaria nº 10, de 3 de janeiro de 2017 (2017). Redefine as diretrizes de modelo assistencial e financiamento de UPA 24h de Pronto Atendimento como Componente da Rede de Atenção às Urgências, no âmbito do Sistema Único de Saúde. Gabinete do Ministro, Brasília, DF.
   Google Scholar
• Brasil. Ministério da Saúde. Portaria nº 2.648, de 7 de novembro de 2011 (2011). Redefine as diretrizes para implantação do Componente Unidade de Pronto Atendimento (UPA 24h) e do conjunto de serviços de urgência 24 (vinte e quatro) horas da Rede de Atenção às Urgências, em conformidade com a Política Nacional de Atenção às Urgências. Gabinete do Ministro, Brasília, DF.
   Google Scholar
• Chen, T. L., & Wang, C. C. (2016). Multi-objective simulation optimization for medical capacity allocation in emergency department. Journal of Simulation, 10(1), 50–68.
   Web of Science ®Google Scholar
• Daldoul, D., Nouaouri, I., Bouchriha, H., & Allaoui, H. (2018). A stochastic model to minimize patient waiting time in an emergency department. Operations Research for Health Care, 18, 16–25.
   Web of Science ®Google Scholar
• Feng, Y. Y., Wu, I. C., & Chen, T. L. (2015). Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Management Science, 20(1), 1–21.
   Web of Science ®Google Scholar
• Ghanes, K., Wargon, M., Jouini, O., Jemai, Z., Diakogiannis, A., Hellmann, R., … Koole, G. (2015). Simulation-based optimization of staffing levels in an emergency department. Simulation, 91(10), 942–953.
   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(1), 42–51.
   Web of Science ®Google Scholar
• Guo, H., Gao, S., Tsui, K. L., & Niu, T. (2017). Simulation optimization for medical staff configuration at emergency department in Hong Kong. IEEE Transactions on Automation Science and Engineering, 14(4), 1655–1665.
   Web of Science ®Google Scholar
• Guo, H., Goldsman, D., Tsui, K. L., Zhou, Y., & Wong, S. Y. (2016). Using simulation and optimisation to characterise durations of emergency department service times with incomplete data. International Journal of Production Research, 54(21), 6494–6511.
   Web of Science ®Google Scholar
• Hall, R. (2013). Patient flow: Reducing delay in healthcare delivery (Vol. 206). Springer Science & Business Media.
   Google Scholar
• Harzi, M., Condotta, J. F., Nouaouri, I., & Krichen, S. (2017). Scheduling patients in emergency department by considering material resources. Procedia Computer Science, 112, 713–722.
   Google Scholar
• Holden, R. J. (2011). Lean thinking in emergency departments: A critical review. Annals of Emergency Medicine, 57(3), 265–278.
   PubMed Web of Science ®Google Scholar
• Hopp, W. J., & Lovejoy, W. S. (2013). Hospital operations. Upper Saddle River: FT Press.
   Google Scholar
• Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38(9), 107.
   PubMed Web of Science ®Google Scholar
• Kelton, W. D., Sadowski, R. P., & Sadowski, D. A. (2002). Simulation with ARENA. New York: McGraw-hill.
   Google Scholar
• Kuo, Y. H., Rado, O., Lupia, B., Leung, J. M., & Graham, C. A. (2016). Improving the efficiency of a hospital emergency department: A simulation study with indirectly imputed service-time distributions. Flexible Services and Manufacturing Journal, 28(1–2), 120–147.
   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.
   Web of Science ®Google Scholar
• Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis. Singapore: McGraw-Hill.
   Google Scholar
• Lewis, C. P., & Aghababian, R. V. (1996). Disaster planning, part I: Overview of hospital and emergency department planning for internal and external disasters. Emergency Medicine Clinics, 14(2), 439–452.
   Google Scholar
• Litvak, E. (2009). Managing patient flow in hospitals: Strategies and solutions (2nd ed.). Oakbrook Terrace IL: Illinois Joint Commission Resources.
   Google Scholar
• Liu, Z., Rexachs, D., Epelde, F., & Luque, E. (2017). A simulation and optimization based method for calibrating agent-based emergency department models under data scarcity. Computers & Industrial Engineering, 103, 300–309.
   Web of Science ®Google Scholar
• Mazier, A., Xie, X., & Sarazin, M. (2010, August). Scheduling inpatient admission under high demand of emergency patients. In Automation Science and Engineering (CASE), 2010 IEEE Conference on (pp. 792–797). Toronto, Canada: IEEE.
   Google Scholar
• Nahhas, A., Awaldi, A., & Reggelin, T. (2017). Simulation and the emergency department overcrowding problem. Procedia Engineering, 178, 368–376.
   Google Scholar
• Omar, E. R., Garaix, T., Augusto, V., & Xie, X. (2015). A stochastic optimization model for shift scheduling in emergency departments. Health Care Management Science, 18(3), 289–302.
   Web of Science ®Google Scholar
• Othman, S. B., Zgaya, H., Hammadi, S., Quilliot, A., Martinot, A., & Renard, J. M. (2016). Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling. Journal of Biomedical Informatics, 64, 25–43.
   Web of Science ®Google Scholar
• Rabbani, M., Farshbaf-Geranmayeh, A., & Yazdanparast, R. (2018). A simulation optimization approach for integrated resource allocation in an emergency department, pharmacy, and lab. Intelligent Decision Technologies, 12(2),187–212.
   Web of Science ®Google Scholar
• Shirazi, B. (2016). Fast track system optimization of emergency departments: Insights from a computer simulation study. International Journal of Modeling, Simulation, and Scientific Computing, 7(03), 1650015.
   Google Scholar
• Shonick, W. (1970). A stochastic model for occupancy-related random variables in general-acute hospitals. Journal of the American Statistical Association, 65(332), 1474–1500.
   Google Scholar
• Sir, M. Y., Nestler, D., Hellmich, T., Das, D., Laughlin, M. J., Jr, Dohlman, M. C., & Pasupathy, K. (2017). Optimization of multidisciplinary staffing improves patient experiences at the Mayo clinic. Interfaces, 47(5), 425–441.
   Web of Science ®Google Scholar
• Smith, C. A., Barry, R., & Brubaker, C. E. (2007). Going lean: Busting barriers to patient flow. American College of Healthcare Executives Management, Series Editorial Board. Chigago, IL: Health Administration Press.
   Google Scholar
• Strauss, R. W. (Ed.). (2014). Strauss and Mayer’s emergency department management. New York, NY: McGraw-Hill Education Medical.
   Google Scholar
• Vaghasiya, M. R., Murphy, M., O’Flynn, D., & Shetty, A. (2014). The emergency department prediction of disposition (EPOD) study. Australasian Emergency Nursing Journal, 17(4), 161–166.
   Google Scholar
• Van Sambeek, J. R. C., Cornelissen, F. A., Bakker, P. J. M., & Krabbendam, J. J. (2010). Models as instruments for optimizing hospital processes: A systematic review. International Journal of Health Care Quality Assurance, 23(4), 356–377.
   PubMedGoogle Scholar
• Wang, T. K., Yang, T., Yang, C. Y., & Chan, F. T. (2015). Lean principles and simulation optimization for emergency department layout design. Industrial Management & Data Systems, 115(4), 678–699.
   Web of Science ®Google Scholar
• Yousefi, M., Yousefi, M., Ferreira, R. P. M., Kim, J. H., & Fogliatto, F. S. (2018). Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artificial Intelligence in Medicine, 84, 23–33.
   PubMed Web of Science ®Google Scholar
• Zeinali, F., Mahootchi, M., & Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation Modelling Practice and Theory, 53, 123–138.
   Web of Science ®Google Scholar