Optimal Equipment Capacity Planning in the Neonatal Intensive Care Unit with Simulation-Optimization Approach

Year 2024, Volume: 37 Issue: 2, 895 - 910

Müfide Narlı Yusuf Kuvvetli Ali Kokangül

https://doi.org/10.35378/gujs.1247829

Abstract

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal numbers of equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics.

Keywords

Simulation-optimization, Capacity planning, Modelling, Neonatal intensive care

References

• [1] Tyrrell, C. S., Mytton, O. T., Gentry, S. V., Thomas-Meyer, M., Allen, J. L. Y., Narula, A. A., and Pari, A. A. A., “Managing intensive care admissions when there are not enough beds during the COVID-19 pandemic: a systematic review”, Thorax, 76(3): 302-312, (2021).
• [2] Stark, A. R., “Levels of neonatal care”, Pediatrics, 114(5): 1341-1347, (2004).
• [3] https://www.saglik.gov.tr, Access Date: 01.06.2021.
• [4] Yeast, J. D., Poskin, M., Stockbauer, J. W., and Shaffer, S., “Changing patterns in regionalization of perinatal care and the impact on neonatal mortality”, American Journal of Obstetrics and Gynecology, 178(1): 131-135, (1998).
• [5] Towers, C. V., Bonebrake, R., Padilla, G., and Rumney, P., “The effect of transport on the rate of severe intraventricular hemorrhage in very low birth weight infants”, Obstetrics & Gynecology, 95(2): 291-295, (2000).
• [6] Mohamed, M. A., and Aly, H., “Transport of premature infants is associated with increased risk for intraventricular haemorrhage”, Archives of Disease in Childhood-Fetal and Neonatal Edition, 95(6): F403-F407, (2010).
• [7] Helenius, K., Longford, N., Lehtonen, L., Modi, N., and Gale, C., “Association of early postnatal transfer and birth outside a tertiary hospital with mortality and severe brain injury in extremely preterm infants: observational cohort study with propensity score matching”, BMJ, 367, l5678, (2019).
• [8] Peliowski, A., Canadian Paediatric Society, Fetus and Newborn Committee, and Fetus and Newborn Committee, “Inhaled nitric oxide use in newborns”, Paediatrics & Child Health, 17(2): 95-97, (2012).
• [9] Joynt, G., Gomersall, C., Tan, P., Lee, A., Cheng, C., and Wong, E., “Prospective evaluation of patients refused admission to an intensive care unit: triage, futility and outcome”, Intensive Care Medicine, 27(9): 1459-1465, (2001).
• [10] Narli, N., Kırımi, E., and Uslu, S., “Turkish Neonatal Society guideline on the safe transport of newborn”, Turkish Archives of Pediatrics/Türk Pediatri Arşivi, 53(Suppl 1), S18, (2018).
• [11] https://www.neonatology.org.tr, Access Date: 01.06.2021.
• [12] Fetter, R. B., and Thompson, J. D., “The simulation of hospital systems”, Operations Research, 13: 689– 711, (1965).
• [13] Bowers, J., “Balancing operating theatre and bed capacity in a cardiothoracic centre”, Health Care Management Science, 16(3): 236-244, (2013).
• [14] Haghighinejad, H. A., Kharazmi, E., Hatam, N., Yousefi, S., Hesami, S. A., Danaei, M., and Askarian, M., “Using queuing theory and simulation modelling to reduce waiting times in an Iranian emergency department”, International Journal of Community Based Nursing and Midwifery, 4(1): 11, (2016).
• [15] Zychlinski, N., Mandelbaum, A., Momčilović, P., and Cohen, I., “Bed blocking in hospitals due to scarce capacity in geriatric institutions—cost minimization via fluid models”, Manufacturing & Service Operations Management, 22(2): 396-411, (2020).
• [16] Rutherford, A. R., Zimmerman, S. L., Moeini, M., Barket, R., Ahkioon, S., and Griesdale, D. E., Simulation Model of a Multi-Hospital Critical Care Network. In 2022 Winter Simulation Conference (WSC), IEEE, 951-960, (2022).
• [17] Bahalkeh, E., Hasan, I., & Yih, Y., “The relationship between intensive care unit length of stay information and its operational performance”, Healthcare Analytics, 2: 100036, (2022).
• [18] Mohamed, I., and Hussein, R. A., “Simulation Optimisation Approach for Managing Bed Capacity in an Intensive Care Unit”, Journal of Information & Knowledge Management, 20(01): 2150001, (2021).
• [19] Williams, E., Szakmany, T., Spernaes, I., Muthuswamy, B., and Holborn, P., “Discrete-event simulation modeling of critical care flow: New hospital, old challenges”, Critical Care Explorations, 2(9): (2020).
• [20] Ouyang, H., Argon, N. T., and Ziya, S., “Allocation of intensive care unit beds in periods of high demand”, Operations Research, 68(2): 591-608, (2020).
• [21] Tierney, L. T., and Conroy, K. M., “Optimal occupancy in the ICU: a literature review”, Australian Critical Care, 27(2): 77-84, (2014).
• [22] Bai, J., Fügener, A., Schoenfelder, J., and Brunner, J. O., “Operations research in intensive care unit management: a literature review”, Health Care Management science, 21(1): 1-24, (2018).
• [23] Kim, S. C., Horowitz, I., Young, K. K., & Buckley, T. A., “Analysis of capacity management of the intensive care unit in a hospital”, European Journal of Operational Research, 115(1): 36-46, (1999).
• [24] Ridge, J. C., Jones, S. K., Nielsen, M. S., and Shahani, A. K., “Capacity planning for intensive care units”, European Journal of Operational Research, 105(2): 346-355, (1998).
• [25] Mallor, F., & Azcárate, C., “Combining optimization with simulation to obtain credible models for intensive care units”, Annals of Operations Research, 221(1): 255-271, (2014).
• [26] Weissman, G. E., Crane-Droesch, A., Chivers, C., Luong, T., Hanish, A., Levy, M. Z., Lubken, J., Becker, M., Draugelis, M. E., Anesi, G. L., Brennan, P. J., Christie, J. D., Hanson, C. W., Mikkelsen, M. E., and Halpern, S. D., “Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic”, Annals of Internal Medicine, 173(1): 21-28, (2020).
• [27] Shoukat, A., Wells, C. R., Langley, J. M., Singer, B. H., Galvani, A. P., and Moghadas, S. M., “Projecting demand for critical care beds during COVID-19 outbreaks in Canada”, CMAJ, 192(19): E489-E496, (2020).
• [28] Oakley, D., Onggo, B. S., and Worthington, D., “Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method”, Health Care Management Science, 23(1): 153-169, (2020).
• [29] Kokangul, A., “A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit”, Computer Methods and Programs in Biomedicine, 90(1): 56-65, (2008).
• [30] Akcali, E., Coˆté, M. J., and Lin, C., “A network flow approach to optimizing hospital bed capacity decisions”, Health Care Management Science, 9(4): 391-404, (2006).
• [31] Vassilacopoulos, G., “A simulation model for bed allocation to hospital inpatient departments”, Simulation, 45(5): 233-241, (1985).
• [32] Harper, P. R., and Shahani, A. K., “Modelling for the planning and management of bed capacities in hospitals”, Journal of the Operational Research Society, 53(1): 11-18, (2002).
• [33] Rodrigues, F. F., Zaric, G. S., and Stanford, D. A., “Discrete event simulation model for planning Level 2 “step-down” bed needs using NEMS”, Operations Research for Health Care, 17: 42-54, (2018).
• [34] Law, A., “Simulation Modeling and Analysis”, 4 rev. ed., McGraw Hill, New York. ISBN: 9780071100519, (2006).
• [35] Law, A.M. &Kelton, W.D., “Simulation Modelling and Analysis”, Second Edition, McGraw-Hill, New York, (1991).
• [36] Chung, C. A. (Ed.)., “Simulation modeling handbook: a practical approach”, CRC Press, (2003).