Araştırma Makalesi
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SAĞLIK KAYNAKLARI İÇİN KESİKLİ-OLAY SİMÜLASYONUN VE DENEYSEL OPTİMİZASYON TASARIMININ UYGULANMASI-YOZGAT BOZOK ÜNİVERSİTESİ ARAŞTIRMA VE UYGULAMA HASTANESİ

Yıl 2021, , 63 - 85, 30.06.2021
https://doi.org/10.33461/uybisbbd.929432

Öz

Sağlık sistemleri içerisinde yer alan en önemli yapılar hastanelerdir. Acil servisler ise hastanelerin en önemli birimleri olmakla beraber hastaların çoğunlukla bu birimden hastaneye girişleri gerçekleşmektedir. Bu sebeple acil servis birimleri sağlık yönetimi açısından önemlidir. Bu çalışma için Yozgat Bozok Üniversitesi Araştırma ve Uygulama Hastanesinin acil servis birimine ait veriler kullanılarak acil serviste tedavi edilen hasta sayısının arttırılmasını, hasta bekleme süresinin azaltılmasını, hastanın acil serviste geçirmesi gereken sürenin azaltılmasını ve kaynaklara ait verimliliğin maksimum seviyeye çıkarılması amaçlanmıştır. Bu çalışmanın metodolojisi üç ana aşamadan oluşmaktadır. İlk aşamada acil servis bölümüne ait kesikli-olay simülasyon modeli oluşturulmuştur. Geçerliliği test edilen simülasyon modeline ait karar değişkenlerden oluşan tüm kombinasyonları içeren ve bu çalışmanın ikinci aşaması olan deney tasarımı modeli oluşturulmuştur. Deney tasarımı ve simülasyon modellerinden elde edilen sonuçların istatistiksel analizi gerçekleştirilerek çalışmanın üçüncü aşaması olan optimizasyon modelleri geliştirilmiştir. Simülasyon ve deney tasarımında ele alınan karar değişkenlerin sahip olduğu maksimum ve minimum değerler göz önünde bulundurularak araştırmanın amaçlarına ve karar değişkenlere ait optimum sonuçlar elde edilmiştir.

Destekleyen Kurum

Yozgat Bozok Üniversitesi Bilimsel Araştırma Proje Koordinatörlüğü

Proje Numarası

6602A-İİBF/20-430

Teşekkür

Bu çalışmaya 6602a-İİBF/20-430 numaralı Bilimsel Araştırma Projeleri ile destek sağlayan Yozgat Bozok Üniversitesi Proje Koordinasyon Uygulama ve Araştırma Merkezi’ne teşekkürlerimizi sunarız.

Kaynakça

  • Altiok, T., & Melamed, B. (2007). Simulation Modeling and Analysis with Arena (1st ed.). Academic Press.
  • Antony, J. (2003). Design of Experiments for Engineers and Scientists. Elsevier.
  • Atalan, A. (2014). Central Composite Design Optimization Using Computer Simulation Approach. Flexsim Quarterly Publication, 5–19. https://www.flexsim.com/wp-content/uploads/2014/07/July2014.pdf
  • Atalan, A. (2018). Türkiye Sağlık Ekonomisi için İstatistiksel Çok Amaçlı Optimizasyon Modelinin Uygulanması. İşletme Ekonomi ve Yönetim Araştırmaları Dergisi, 1(1), 34–51. http://dergipark.gov.tr/download/article-file/414076
  • Atalan, A., & Donmez, C. (2019). Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application. Processes, 7(1), 48. https://doi.org/10.3390/pr7010048
  • Atalan, A., & Donmez, C. C. (2020). Developing Optimization Models to Evluate Healthcare Systems. Sigma Journal of Engineering and Natural Sciences, 38(2), 853–873.
  • Austin, A., & Wetle, V. (2016). The United States Health Care System: Combining Business, Health, and Delivery (3rd ed.). PRENTICE HALL.
  • Clemente, J., Lázaro-Alquézar, A., & Montañés, A. (2019). US state health expenditure convergence: A revisited analysis. Economic Modelling, 83, 210–220. https://doi.org/10.1016/j.econmod.2019.02.011
  • 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. https://doi.org/10.1016/J.ORHC.2018.01.008
  • Dönmez, N. F. K., Atalan, A., & Dönmez, C. Ç. (2020). Desirability Optimization Models to Create the Global Healthcare Competitiveness Index. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-04718-w
  • Eriksen, S., & Wiese, R. (2019). Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries. European Journal of Political Economy. https://doi.org/https://doi.org/10.1016/j.ejpoleco.2019.02.001
  • Fisher, R. A. (1971). The Design of Experiment (Reprinted). Hafner Publishing Company.
  • Goienetxea Uriarte, A., Ruiz Zúñiga, E., Urenda Moris, M., & Ng, A. H. C. (2017). How can decision makers be supported in the improvement of an emergency department? A simulation, optimization and data mining approach. Operations Research for Health Care, 15, 102–122. https://doi.org/10.1016/J.ORHC.2017.10.003
  • Joffres, M. R., Campbell, N. R. C., Manns, B., & Tu, K. (2007). Estimate of the benefits of a population-based reduction in dietary sodium additives on hypertension and its related health care costs in Canada. Canadian Journal of Cardiology, 23(6), 437–443. https://doi.org/10.1016/S0828-282X(07)70780-8
  • Kelton, W. D. (2004). Simulation with Arena (4th ed.). Mass: WCB/McGraw-Hill.
  • McGuire, M., & Iuga. (2014). Adherence and health care costs. Risk Management and Healthcare Policy, 35. https://doi.org/10.2147/RMHP.S19801
  • Mikolajczak, M., & Bellegem, S. Van. (2017). Increasing emotional intelligence to decrease healthcare expenditures: How profitable would it be? Personality and Individual Differences, 116(Supplement C), 343–347. https://doi.org/https://doi.org/10.1016/j.paid.2017.05.014
  • Montgomery, D. C. (2012). Design and Analysis of Experiments (8th ed.). Wiley.
  • Pellegrini, L. C., Rodriguez-Monguio, R., & Qian, J. (2014). The US healthcare workforce and the labor market effect on healthcare spending and health outcomes. International Journal of Health Care Finance and Economics. https://doi.org/10.1007/s10754-014-9142-0
  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research, 219(3), 611–621. https://doi.org/10.1016/j.ejor.2011.10.043
  • Siciliani, L., Stanciole, A., & Jacobs, R. (2009). Do waiting times reduce hospital costs? Journal of Health Economics, 28, 771–780.
  • Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52(Supplement C), 53–64. https://doi.org/https://doi.org/10.1016/j.omega.2014.10.005
  • Tsai, J. C.-H., Liang, Y.-W., & Pearson, W. S. (2010). Utilization of Emergency Department in Patients With Non-urgent Medical Problems: Patient Preference and Emergency Department Convenience. Journal of the Formosan Medical Association, 109(7), 533–542. https://doi.org/10.1016/S0929-6646(10)60088-5
  • Türk Dil Kurumu. (2021). Türk Dil Kurumu Sözlükleri. TDK. https://sozluk.gov.tr/
  • Van Barneveld, T., Jagtenberg, C., Bhulai, S., & Van der Mei, R. (2018). Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation. Socio-Economic Planning Sciences, 62, 129–142. https://doi.org/10.1016/j.seps.2017.11.001
  • Wang, T., Guinet, A., Belaidi, A., & Besombes, B. (2009). Modelling and simulation of emergency services with ARIS and Arena. case study: The emergency department of Saint Joseph and Saint Luc hospital. Production Planning and Control. https://doi.org/10.1080/09537280902938605

APPLICATION OF DISCRETE-EVENT SIMULATION AND EXPERIMENTAL OPTIMIZATION DESIGN FOR HEALTHCARE RESOURCES-YOZGAT BOZOK UNIVERSITY RESEARCH AND APPLICATION HOSPITAL

Yıl 2021, , 63 - 85, 30.06.2021
https://doi.org/10.33461/uybisbbd.929432

Öz

Hospitals are the most important structures in healthcare systems. The emergency services are the most important units of hospitals and the patients mostly enter the hospital from these units. For this reason, emergency units are important in terms of healthcare management. This study has aimed to increase the number of patients treated in the emergency department, to decrease the patient waiting time, to reduce the time that the patient should spend in the emergency department, and to maximize the efficiency of the resources by using the data of the emergency department of Yozgat Bozok University Research and Application Hospital. The methodology of this study consists of three main stages. In the first stage, a discrete-event simulation model belonging to the emergency department was created. The second stage of this study, design of experiment which includes all combinations of decision variables belonging to the simulation model whose validity was tested, was created. The third stage of the study, optimization models, were developed by performing statistical analysis of the results obtained from the design of experiment and simulation models. Considering the maximum and minimum values of the decision variables in simulation and design of experiment optimum results for the aims of the research and the decision variables were obtained. 

Proje Numarası

6602A-İİBF/20-430

Kaynakça

  • Altiok, T., & Melamed, B. (2007). Simulation Modeling and Analysis with Arena (1st ed.). Academic Press.
  • Antony, J. (2003). Design of Experiments for Engineers and Scientists. Elsevier.
  • Atalan, A. (2014). Central Composite Design Optimization Using Computer Simulation Approach. Flexsim Quarterly Publication, 5–19. https://www.flexsim.com/wp-content/uploads/2014/07/July2014.pdf
  • Atalan, A. (2018). Türkiye Sağlık Ekonomisi için İstatistiksel Çok Amaçlı Optimizasyon Modelinin Uygulanması. İşletme Ekonomi ve Yönetim Araştırmaları Dergisi, 1(1), 34–51. http://dergipark.gov.tr/download/article-file/414076
  • Atalan, A., & Donmez, C. (2019). Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application. Processes, 7(1), 48. https://doi.org/10.3390/pr7010048
  • Atalan, A., & Donmez, C. C. (2020). Developing Optimization Models to Evluate Healthcare Systems. Sigma Journal of Engineering and Natural Sciences, 38(2), 853–873.
  • Austin, A., & Wetle, V. (2016). The United States Health Care System: Combining Business, Health, and Delivery (3rd ed.). PRENTICE HALL.
  • Clemente, J., Lázaro-Alquézar, A., & Montañés, A. (2019). US state health expenditure convergence: A revisited analysis. Economic Modelling, 83, 210–220. https://doi.org/10.1016/j.econmod.2019.02.011
  • 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. https://doi.org/10.1016/J.ORHC.2018.01.008
  • Dönmez, N. F. K., Atalan, A., & Dönmez, C. Ç. (2020). Desirability Optimization Models to Create the Global Healthcare Competitiveness Index. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-04718-w
  • Eriksen, S., & Wiese, R. (2019). Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries. European Journal of Political Economy. https://doi.org/https://doi.org/10.1016/j.ejpoleco.2019.02.001
  • Fisher, R. A. (1971). The Design of Experiment (Reprinted). Hafner Publishing Company.
  • Goienetxea Uriarte, A., Ruiz Zúñiga, E., Urenda Moris, M., & Ng, A. H. C. (2017). How can decision makers be supported in the improvement of an emergency department? A simulation, optimization and data mining approach. Operations Research for Health Care, 15, 102–122. https://doi.org/10.1016/J.ORHC.2017.10.003
  • Joffres, M. R., Campbell, N. R. C., Manns, B., & Tu, K. (2007). Estimate of the benefits of a population-based reduction in dietary sodium additives on hypertension and its related health care costs in Canada. Canadian Journal of Cardiology, 23(6), 437–443. https://doi.org/10.1016/S0828-282X(07)70780-8
  • Kelton, W. D. (2004). Simulation with Arena (4th ed.). Mass: WCB/McGraw-Hill.
  • McGuire, M., & Iuga. (2014). Adherence and health care costs. Risk Management and Healthcare Policy, 35. https://doi.org/10.2147/RMHP.S19801
  • Mikolajczak, M., & Bellegem, S. Van. (2017). Increasing emotional intelligence to decrease healthcare expenditures: How profitable would it be? Personality and Individual Differences, 116(Supplement C), 343–347. https://doi.org/https://doi.org/10.1016/j.paid.2017.05.014
  • Montgomery, D. C. (2012). Design and Analysis of Experiments (8th ed.). Wiley.
  • Pellegrini, L. C., Rodriguez-Monguio, R., & Qian, J. (2014). The US healthcare workforce and the labor market effect on healthcare spending and health outcomes. International Journal of Health Care Finance and Economics. https://doi.org/10.1007/s10754-014-9142-0
  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research, 219(3), 611–621. https://doi.org/10.1016/j.ejor.2011.10.043
  • Siciliani, L., Stanciole, A., & Jacobs, R. (2009). Do waiting times reduce hospital costs? Journal of Health Economics, 28, 771–780.
  • Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52(Supplement C), 53–64. https://doi.org/https://doi.org/10.1016/j.omega.2014.10.005
  • Tsai, J. C.-H., Liang, Y.-W., & Pearson, W. S. (2010). Utilization of Emergency Department in Patients With Non-urgent Medical Problems: Patient Preference and Emergency Department Convenience. Journal of the Formosan Medical Association, 109(7), 533–542. https://doi.org/10.1016/S0929-6646(10)60088-5
  • Türk Dil Kurumu. (2021). Türk Dil Kurumu Sözlükleri. TDK. https://sozluk.gov.tr/
  • Van Barneveld, T., Jagtenberg, C., Bhulai, S., & Van der Mei, R. (2018). Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation. Socio-Economic Planning Sciences, 62, 129–142. https://doi.org/10.1016/j.seps.2017.11.001
  • Wang, T., Guinet, A., Belaidi, A., & Besombes, B. (2009). Modelling and simulation of emergency services with ARIS and Arena. case study: The emergency department of Saint Joseph and Saint Luc hospital. Production Planning and Control. https://doi.org/10.1080/09537280902938605
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Kütüphane ve Bilgi Çalışmaları
Bölüm Makaleler
Yazarlar

Yasemin Ayaz Atalan 0000-0001-7767-0342

Ümit Çıraklı 0000-0002-3134-8830

Battal Burak Temel 0000-0002-7701-7658

Abdulkadir Atalan 0000-0003-0924-3685

Proje Numarası 6602A-İİBF/20-430
Yayımlanma Tarihi 30 Haziran 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Ayaz Atalan, Y., Çıraklı, Ü., Temel, B. B., Atalan, A. (2021). SAĞLIK KAYNAKLARI İÇİN KESİKLİ-OLAY SİMÜLASYONUN VE DENEYSEL OPTİMİZASYON TASARIMININ UYGULANMASI-YOZGAT BOZOK ÜNİVERSİTESİ ARAŞTIRMA VE UYGULAMA HASTANESİ. International Journal of Management Information Systems and Computer Science, 5(1), 63-85. https://doi.org/10.33461/uybisbbd.929432