YOĞUN BAKIM ÜNİTELERİNDE HASTA AKIŞININ DEĞERLENDİRMESİ: 3. BASAMAK HASTANELER İÇİN SİMÜLASYON MODELLEMESİ

Yıl 2023, Cilt: 26 Sayı: 4, 1009 - 1032, 28.12.2023

Alkan Durmuş Ali Özdemir

https://doi.org/10.61859/hacettepesid.1314024

Öz

Yoğun bakım ünitelerinde hasta akışının modellenmesi, süreçlerin daha iyi anlaşılmasına ve bu modellerin kullanımı yoğun bakım sistemlerinin işlevselliğinin artırılmasına katkıda bulunabilir. Yoğun bakım ünitelerinde (YBÜ) hasta akışının kötü yönetimi, hasta beklemelerine ve hastaların reddedilmesine neden olabilir. Ayrıca YBÜ yönetimi kapasite yönetimi ve planlaması açısından önemli zorluklarla karşı karşıya kalır. Bu araştırma, 3. basamak kamu üniversite hastanesinde yoğun bakım hastaların akışının ayrık olay simülasyonu yöntemiyle modellenmesine ve kapasite ihtiyacına odaklanmaktadır. Yoğun bakım ihtiyacı olan ve biten hastaların servisler arasındaki geçişlerinde gecikmeler yaşanabilmektedir. Bu çalışmanın amacı, Yoğun Bakım Ünitesi (YBÜ) hastalarının kabul, yoğun bakım yatağı bekleme ve taburculuk süreçlerindeki kısıtlamaları simüle ederek, hastane yönetim politikalarının performansını değerlendirmek ve mevcut yatak sayısında hasta bekleme sürelerinin minimize edildiği bir senaryoda gereken yatak sayısını hesaplamaktır. Ayrıca, diğer servis yataklarının dolu olması nedeniyle geciken taburculuk sürecinin alternatif bir politika önerisiyle ele alınması hedeflenmektedir. Oluşturulan simülasyon modeliyle, YBÜ hizmetlerinin mevcut durumunu hasta bekleme süreleri açısından azaltılabileceği bulunmuştur. Tam zamanında hasta taburculukları YBÜ yataklarına nakledilecek hastaların ortalama bekleme sürelerinin azaltılabileceği gözlemlenmiştir.

Anahtar Kelimeler

Yoğun Bakım Ünitesi, Hasta Akışı, Kapasite Planlaması, Simülasyon

Destekleyen Kurum

yok

Proje Numarası

yok

ASSESSMENT OF PATIENT FLOW IN INTENSIVE CARE UNITS: SIMULATION MODELING FOR TERTIARY HOSPITALS

Öz

Modeling patient flow in intensive care systems can contribute to a better understanding of processes and the utilization of these models can enhance the functionality of intensive care systems. Poor management of patient flow in intensive care units (ICUs) can lead to patient waiting times and patient rejections. Additionally, ICU management faces significant challenges in terms of capacity management and planning. This article focuses on modeling the flow of intensive care patients in a tertiary public university hospital using discrete event simulation method and addressing capacity needs. Delays can occur in the transitions between services for patients in need of intensive care and those who have completed their care. The aim is to evaluate the performance of patient flow management policies and calculate the required number of beds when the waiting times for patients are minimized in the current bed capacity, via simulations of the constraints in the admission, intensive care bed waiting, and discharge processes of Intensive Care Unit (ICU) patients. Furthermore, the study aims to address the delayed discharge process due to the unavailability of other service beds, by proposing an alternative policy. The simulation model created has been found to reduce waiting times for patients in the current state of the ICU services. It has been observed that the discharge of patients in a just-in-time manner has reduced the average waiting times for patients to be transferred to ICU beds.

Anahtar Kelimeler

Intensive Care Unit, Patient Flow, Capacity Planning, Simulation

Proje Numarası

yok

Kaynakça

• Abhicharttibutra, K., Wichaikhum, O. A., Kunaviktikul, W., Kunaviktikul, W., & Nantsupawat, R. (2018). Occupancy rate and adverse patient outcomes in general hospitals in Thailand: a predictive study. Nursing & health sciences, 20(3), 387-393.
• Alban, A., Chick, S. E., Lvova, O., & Sent, D. (2020). A Simulation Model to Evaluate the Patient Flow in an Intensive Care Unit under Different Levels of Specialization. 2020 Winter Simulation Conference (WSC) (s. 922-933). Orlando: IEEE.
• Azcarate, C., Esparza, L., & Mallor, F. (2020). The problem of the last b e d: Contextualization and a new simulation framework for analyzing physician decisions. Omega, 96, 1-20.
• Bahalkeh, E., Hasan, I., & Yih, Y. (2022). The relationship between intensive care unit length of stay information and its operational performance. Healthcare Analytics, 2, 1-10.
• Bai, J., Fügener, A., Schoenfelder , J., & Brunner , J. O. (2018). Operations research in intensive care unit management: a literature review. Health Care Management Science, 21, 1–24 .
• Bakker, J., Damen, J., van Zanten, A. R., & Hubben, J. H. (2003). Admission and discharge criteria for intensive care departments. Ned Tijdschr Geneeskd, 147(3), 110-115.
• Banks , J., Carson II · , J. S., Nelson, B. L., & Nicol , D. M. (2005). Discrete-Event System Simulation. Prentice Hall. Bard, J. F., Shu, Z., Morrice, D. J., Wang, D. E., Poursani, R., & Leykum, L. (2016). Improving patient flow at a family health clinic. Health Care Manag Sci., 19(2), 170-191.
• Barton, P., Bryan, S., & Robinson, S. (2008). Modelling in the Economic Evaluation of Healthcare: Selecting the Appropriate Approach. J Health Serv Res Policy, 9(2), 9-16.
• Batun, S., & Begen, M. A. (2013). Optimization in Healthcare Delivery Modeling: Methods and Applications. B. T. Denton içinde, Handbook of Healthcare Operations Management (s. 75-121). New York: Springer Science+Business Media.
• Bhattacharjee, P., & Ray, P. K. (2014). Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, 78, 299-312.
• Birta, L. G., & Arbez, G. (2013). Modelling and simulation. London: Springer.
• Bone, R. C., McElwee, N. E., Eubanks, D. H., & Gluck, E. H. (1993). Analysis of indications for intensive care unit admission. Clinical efficacy assessment project: American College of Physicians. Chest., 104(6), 1806-1811.
• Botros, A. R., Razik, G. M., Alanwer, K. M., & Abd El salam, M. M. (2021). Patients' characteristics, occupancy rate and quality of performance of Emergency Intensive Care Unit at Zagazig University Hospital, Egypt: A descriptive Study. European Journal of Molecular & Clinical Medicine, 8(3), 3722-3737.
• Bountourelis, T., Ulukus, M. Y., Kharoufeh, J. P., & Nabors, S. G. ( 2013). The Modeling, Analysis, and Management of Intensive Care Units. B. T. Denton içinde, Handbook of Healthcare Operations Management (s. 153-182). New York: Springer Science+Business Media .
• Brailsford, S. C. (2007). Advances and challenges in healthcare simulation modeling:tutorial. Proceedings of the 39th conference on Winter Simulation (s. 1436-1448). Washington: IEEE.
• Brailsford, S., Harper, P., Patel, B., & Pitt , M. (2009). Brailsford, S., Harper, P., Patel, B. et al. An analysis of the academic literature on simulation and modelling in health care. J Simulation, 3, 130–140.
• Brideau, L. P. (2004). Flow: Why Does It Matter? Frontiers of Health Services Management, 20(4).
• Bruyneel, A., Larcin, L., Martins, D., Bulcke, J., Leclercq, P., & Pirson, M. (2023). Cost comparisons and factors related to cost per stay in intensive care units in Belgium. BMC Health Services Research, 23, 1-15.
• Bukowski, L. (2019). Modelling and Simulation of Logistic Networks. Switzerland: Springer Nature Switzerland AG.
• Burdett, R., & Kozan, E. (2016). A multi-criteria approach for hospital capacity analysis. Eur. J. Oper. Res., 255, 505–521.
• Carson, J. S. (2005). Introduction to modeling and simulation. In Proceedings of the Winter Simulation Conference (s. 16-23). Orlando: IEEE.
• Cochran, J. K., & Bharti, A. (2006). Stochastic bed balancing of an obstetrics hospital. Health Care Management Science, 9(1), 31-45.
• Cooper, R. B. (1981). Queueing theory. Proceedings of the ACM '81 conference (s. 119–122). New York: Association for Computing Machinery.
• Costa, A. X., Ridley, S. A., Shahani, A. K., Harper, P. R., De Senna, V., & Nielsen, M. S. (2003). Mathematical modelling and simulation for planning critical care capacity. Anaesthesia, 58(4), 320-327.
• Damiani, L., Guizzi, G., Giribone, P., Revetria, R., & Romano, E. (2016). Different approaches for studying interruptible industrial processes: Application of two different simulation techniques. F. Miranda, & C. Abreu içinde, Handbook of Research on Computational Simulation and Modeling in Engineering (s. 69-104). Hershey: IGI Global.
• Davies, R., & Davies, H. (1994). Modelling patient flows and resource provision in health systems. Omega, 22(2), 123–131.
• Dehkordi, A., & Sadat, S. (2017). Sustaining critical care: using evidence-based simulation to evaluate ICU management policies. Health Care Management Science, 20, 532–547.
• Devaraj, S., Ow, T. T., & Kohli, R. (2013). Examining the impact of information technology and patient flow on healthcare performance: A Theory of Swift and Even Flow (TSEF) perspective. Journal of Operations Management, 31, 181–192.
• Dobson, G., Lee, H.-H., & Pinker, E. J. (2008, 4 8). Patient Flow in an ICU. Simon School Working Paper No. 08-21, s. 1-33.
• Egol, A., Fromm, R., Guntupalli, K. K., Fitzpatrick, M., Kaufman, D., Nasraway, S., . . . Zimmerman, J. (1999). Guidelines for intensive care unit admission,discharge, and triage. Intensivmed, 36, 545–551.
• Elbeyli, S., & Krishnan, P. (2000). In-patient flow analysis using promodel simulation package. Delaware: University of Delaware.
• El-Bouri, R., Taylor, T., Youssef, A., Zhu, T., & Clifton, D. A. (2021). Machine learning in patient flow: a review. Prog Biomed Eng (Bristol)., 3(2), 1-23.
• El‐Darzi, E., Vasilakis, C., Chaussalet, T., & Mi, P. H. (1998). A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department. Health Care Management Science, 1, 143–149.
• Forbus, J. J., & Berleant, D. (2022). Discrete-Event Simulation in Healthcare Settings: A Review. Modelling, 3(4), 417-433.
• Gaba, D. M. (2004). The future vision of simulation in health care. Qual Saf Health Care, 13, 2-10.
• Green, L. (2005). Capacity planning and management in hospitals. Operations Res Health Care. (s. 15-41).
• Green, L. V. (2002). How many hospital beds? INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 39(4), 400-412.
• Griffin, J., Xia, S., Peng, S., & Keskinocak , P. (2012). Improving patient flow in an obstetric unit. Health Care Management Science, 15, 1–14.
• Gromova, E. A., & Pupentsova, S. V. (2020). Simulation modelling as a method of risk analysis in real estate valuation. In IOP Conference Series: Materials Science and Engineering. IOP Publishing.
• Günal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1, 17–25.
• Hadjipavlou, G., Titchell, J., Heath, C., Siviter, R., & Madder, H. (2020). Using probabilistic patient flow modelling helps generate individualised intensive care unit operational predictions and improved understanding of current organisational behaviours. Journal of the Intensive Care, 21(3), 221-229.
• Hagen, M. S., Jopling, J. K., Buchman, T. G., & Lee, E. K. (2013). Priority queuing models for hospital intensive care units and impacts to severe case patients. AMIA Annu Symp Proc., 1, 841–850.
• Hall, R., Belson, D., Murali, P., & Dessouky, M. (2006). In Patient flow: Reducing delay in healthcare delivery. R. Hall, D. Belson, P. Murali, & M. Dessouky içinde, Modeling patient flows through the healthcare system (s. 1-44). Boston: Springer.
• Hall, R., Belson, D., Murali, P., & Dessouky, M. (2013). Modeling Patient Flows Through the Health care System. R. Hall içinde, Patient flow (s. 3-43). New York: © Springer Science+Business Media.
• Hasan, I., Bahalkeh, E., & Yih, Y. (2020). Evaluating intensive care unit admission and discharge policies using a discrete event simulation model. Simulation, 96(6), 501-518.
• Henning, R. J., McClish, D., Daly, B., Nearman, H., Franklin, C., & Jackson, D. (1987). Clinical characteristics and resource utilization of ICU patients: implications for organization of intensive care. Crit Care Med., 15(3), 264-269.
• Hulshof, P. J., Kortbeek, N., Boucherie, R. J., Hans , E. W., & Bakker , P. J. (2012). Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS. Health Systems, 1, 129–175.
• Karnon, J., Stahl, J., Brennan, A., Caro, J. J., Mar, J., & Möller, J. (2012). Modeling Using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4. Medical Decision Making, 32(5), 701-711.
• Katsaliaki, K., & Mustafee, N. (2011). Applications of simulation within the healthcare context. Journal of the Operational Research Society, 62(8), 1431-1451.
• Kolker, A. (2009). Process modeling of ICU patient flow: effect of daily load leveling of elective surgeries on ICU diversion. J Med Syst., 33, 27–40.
• Kovalchuk, S. V., Funkner, A. A., Metsker, O. G., & Yakovlev, A. N. (2018). Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification. Journal of Biomedical Informatics, 82, 128-142.
• Kriegel, J., Jehle, F., Dieck, M., & Tuttle-Weidinger, L. (2015). Optimizing patient flow in Austrian hospitals – Improvement of patient-centered care by coordinating hospital-wide patient trails. International Journal of Healthcare Management, 8(2), 89-99.
• Kumar, N., Singh, A., & Kulkarni, R. V. (2015). Transcriptional bursting in gene expression: analytical results for general stochastic models. PLoS computational biology. PLoS computational biology, 11(10), 1-22.
• Lakshmi, C., & Iyer, S. A. (2013). Application of queueing theory in health care: A literature review. Operations Research for Health Care, Volume 2(1–2), 25-39.
• Law , A. M. (2007). Simulation and modeling analysis. New York: McGraw Hil.
• Law, A. M., & Kelton , W. D. (1991). Simulation Modeling And Analysis. Singapore: McGraw·HiII.
• Lehoczky, J. P. (1996). Real-time queueing theory. 17th IEEE Real-Time Systems Symposium (s. 186-195). Washington: IEEE.
• Leviner, S. (2020). Patient Flow Within Hospitals: A Conceptual Model. Nursing Science Quarterly, 33(1), 29-34.
• Lin, F., Chaboyer, W., & Wallis, M. (2009). A literature review of organisational, individual and teamwork factors contributing to the ICU discharge process. Australian Critical Care, 22(1), 29–43.
• Litvak, N., Rijsbergen, M., Boucherie, R. J., & Houdenhoven, M. (2008). Managing the overflow of intensive care patients. European Journal of Operational Research, 185(3), 998-1010.
• Lovett, P. B., Illg, M. L., & Sweeney, B. E. (2016). A Successful Model for a Comprehensive Patient Flow Management Center at an Academic Health System. Am J Med Qual., 31(3), 246-55.
• Mahachek, A. R. (1992). Mahachek AR. An introduction to patient flow simulation for health-care managers. Journal of the Society for Health Systems, 3(3), 73-81.
• Mahadevan, B. (2015). Operations Management Theory and Practice. Uttar Pradesh: Pearson.
• Mallor, F., & Azcárate , C. (2014). Combining optimization with simulation to obtain credible models for intensive care units. Annals of Operations Research, 221, 255–271.
• Marik , P. E. (2016). Management of the Critically Ill Geriatric Patient. J. M. O’Donnell, & F. E. Nácul içinde, Management of the Critically Ill Geriatric Patient (s. 743–758). Switzerland: Springer International Publishing Switzerland.
• Marlene Gyldmark CP. A review of cost studies of intensive care units. Crit Care Med 1995; 23: 964–972.
• Marshall, A., Vasilakis, C., & El-Darzi, E. (2005). Length-of-stay patient flow models: recent developments and future directions. Health Care Manag Sci., 8, 213–220.
• Mathews, K. S., & Long, E. F. (2015). A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use. AnnalsATS, 12(6), 886-894.
• McManus, M. L., Long, L. C., Cooper, A., & Litvak, E. (2004). Queuing Theory Accurately Models the Need for Critical Care Resources. Anesthesiology, 100, 1271–1276.
• Memon, R. A., Li, J. P., & Ahmed, J. (2019). Simulation Model for Blockchain Systems Using Queuing Theory. Electronics, 8(234), 1-19.
• Mielczarek, B., & Uziałko-Mydlikowska, J. (2012). Application of computer simulation modeling in the health care sector: a survey. Simulation, 88(2), 197-216.
• Najmuddin, A. F., Ibrahim, I. M., & Ismail, S. R. (2010). A Simulation Approach: Improving Patient Waiting Time for Multiphase Patient Flow of Obstetrics and Gynecology Department (O&G Department) in Local Specialist Centre. WSEAS Trans Math., 9(10), 778-790.
• Nates, J. L., Nunnally, M., Kleinpell, R., Sandralee, B., Goldner, J., Birriel, B., . . . Sprung, C. L. (2016). ICU Admission, Discharge, and Triage Guidelines: A Framework to Enhance Clinical Operations, Development of Institutional Policies, and Further Research. Critical Care Medicine, 44(8), 1553-1602.
• Oddoye, J. P., Jones, D. F., Tamiz, M., & Schmidt, P. (2009). Combining simulation and goal programming for healthcare planning in a medical assessment unit. European Journal of Operational Research, 193(1), 250-261.
• Palvannan, R. K., & Teow , K. L. (2012). Queueing for Healthcarevvv. Journal of Medical Systems, 36, 541–547.
• Paul, R. J. (1991). Recent Developments in Simulation Modelling. The Journal of the Operational Research Society, 42(3), 217-226.
• Peck, E. (2017). Organisational Development in Healthcare. Boca Raton: CRC Press.
• Pendharkar, S. R., Bischak, D. P., & Roger, P. (2015). Using patient flow simulation to improve access at a multidisciplinary sleep centre. Journal of Sleep Research, 24(3), 320-327.
• Rashwan, W., Abo-Hamad, W., & Arisha, A. (2015). A system dynamics view of the acute bed blockage problem in the Irish healthcare system. European Journal of Operational Research, 247(1), 276-293.
• Restrepo, M., Henderson, S. G., & Topalogu, H. (2009). Erlang loss models for the static deployment of ambulances. Health Care Manage. Sci., 12, 67–79.
• Rhodes, A., Ferdinande, P., Flaatten, H., Guidet, B., Metnitz, P. G., & Moreno , R. P. (2012). The variability of critical care bed numbers in Europe. Intensive Care Medicine, 38, 1647–1653.
• Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Manag Sci., 14, 135–145.
• Santos, A., Gurling, J., Dvorak, M. F., Noonan, V. K., Fehlings, M. G., Burns, A. S., . . . Liang, L. (2013). Modeling the patient journey from injury to community reintegration for persons with acute traumatic spinal cord injury in a Canadian centre. PloS one, 8(8), 1-10.
• Shahani, A. K., Ridley, S. A., & Nielsen, M. S. (2008). Modelling patient flows as an aid to decision making for critical care capacities and organisation. Anaesthesia, 63(10), 1074-1080.
• Shannon, R. E. (1998). Introduction to the art and science of simulation. Proceedings of the 1998 Winter Simulation Conference (s. 7-14). Washington: IEEE.
• Shoaib, M., & Ramamohan, V. (2022). Simulation modeling and analysis of primary health center operations. Simulation: Transactions of the Society for Modeling and Simulation International, 98(3), 183–208.
• Smith, H., Varshoei, P., Boushey, R., & Kuziemsky, C. (2020). Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review. J Am Med Inform Assoc., 27(6), 908-916.
• Sobolev, B., Levy, A., & Kuramoto, L. (2013). Access to Surgery and Medical Consequences of Delays. R. Hall içinde, Patient Flow (s. 129-153). New York: Springer Science+Business Media.
• Song, C., & Zhuang, J. (2017). Two-stage security screening strategies in the face of strategic applicants, congestions and screening errors. Annals of Operations Research, 258(2), 237-262.
• Sztrik, J. (2012). Basic queueing theory. Debrecen: University of Debrecen, Faculty of Informatics. Taylor, S. J., Eldabi, T., Riley, G., Paul, R. J., & Pidd, M. (2009). Simulation modelling is 50! Do we need a reality check? Journal of the Operational Research Society, 60, 69-82.
• Villa, S., Barbieri, M., & Lega, F. (2009). Restructuring patient flow logistics around patient care needs: implications and practicalities from three critical cases. Health Care Management Science, 12(2), 155‐165.
• Wallace, D. J., Seymour, C. W., & Kahn, J. M. (2017). Hospital-level changes in adult ICU bed supply in the United States. Crit Care Med., 45(1), 1-18.
• White, K. P., & Ingalls , R. G. (2015). Introduction to Simulation. Proceedings of the 2015 Winter Simulation Conference (s. 1741-1755). California: IEEE Press.
• Williams, J., Dumont, S., Parry-Jones, J., Komenda, I., & Griffith, J. (2015). Mathematical modelling of patient flows to predict critical care capacity required following the merger of two district general hospitals into one. Anaesthesia, 70(1), 32-40.
• Willoughby, K. A., Chan, B. T., & Marques, S. (2016). Using simulation to test ideas for improving speech language pathology services. Eur J Oper Res., 252(2), 657–664.
• Worthington, D. J. (1987). Queueing models or hospital waiting lists. J. Oper. Res. Soc., 38(5), 413–422.
• Zhang, X. (2018). Application of discrete event simulation in health care: a systematic review. BMC Health Serv Res., 18, 1-11.
• Zhao, L., & Lie, B. (2010). Modeling and Simulation of Patient Flow in Hospitals for Resource Utilization. Simul. Notes Eur., 20(2), 41-50.