Analyzing the Efficiency of Health Resources of Health Institutions in Computer Environment and Calculating Optimum Values

Öz

As health systems have a dynamic and complex structure, obtaining tangible results usually requires a long time and high cost. In this study, a three-dimensional discrete-event simulation model has been developed in order to analyze the efficiency of health resources of health institutions in a computer environment and to calculate optimum values. The simulation model has been developed by taking into account the most intense and active emergency service unit of health institutions. The patient flow chart in the simulation model was created according to the Turkish health system. In this study, the number of patients treated, patient waiting and length of stay, staff productivity, and location-based resources were calculated by taking into account the available health resources. With the statistical experimental design, 16 scenarios were created considering the number of beds, doctors, and nurses, and optimum results were obtained by running each scenario with 3 repetitions. At least 2 doctors, 1 nurse, and 4 beds are required to minimize the patient waiting time and patient stay, in the emergency room, while a patient waits for an average of 9.34 minutes, the average stay of a patient in the emergency room is calculated as 36.92 minutes. By employing 1 doctor, 1 nurse, and 2 beds in order to maximize the number of patients treated with human and location-based resource efficiencies, the optimum values for staff and location efficiencies and the number of patients treated were calculated as 51%, 61%, and 275, respectively. As a result, the simulation model developed in this study to calculate the efficiency and optimum health resource number values of a dynamic and complex health institution has provided important results in terms of health management.

Anahtar Kelimeler

• health resources
• efficiency
• discrete-event simulation
• optimum
• statistical experiment design
• emergency room

Sağlık Kuruluşlarına ait Sağlık Kaynaklarının Bilgisayar Ortamında Verimliliklerinin Analiz Edilmesi ve Optimum Değerlerin Hesaplanması

Öz

Sağlık sistemleri dinamik ve karmaşık yapıya sahip olması ile somut sonuçların elde edilmesi genellikle uzun zaman ve yüksek maliyet gerektirmektedir. Bu çalışmada sağlık kuruluşlarına ait sağlık kaynaklarının bilgisayar ortamında verimliliklerinin analiz edilmesi ve optimum değerlerin hesaplanması adına üç boyutlu simülasyon modeli geliştirilmiştir. Simülasyon modeli sağlık kuruluşlarının en yoğun ve hareketli olan acil servis birimi dikkate alarak geliştirilmiştir. Simülasyon modelinde yer alan hasta akış çizelgesi Türk sağlık sistemine göre oluşturulmuştur. Bu çalışmada mevcut sağlık kaynakları dikkate alınarak tedavi edilen hasta sayısı, hasta bekleme ve kalış süreleri, personel verimlilikleri, lokasyon bazlı kaynakların verimlilikleri hesaplanmıştır. İstatistiksel deney tasarımı ile yatak, doktor ve hemşire sayıları dikkate alınarak 16 senaryonun oluşturulması ve her bir senaryonun 3 tekrar ile çalıştırılması ile optimum sonuçların elde edilmesi sağlanmıştır. Hasta bekleme süresinin ve hasta kalış süresinin miminize edilmesi için acil serviste en az 2 doktor, 1 hemşire ve 4 yatak çalışması ile bir hasta ortalama 9,34 dakika beklerken bir hastanın acil serviste kalma süresi ortalama olarak 36,92 dakika olarak hesaplanmıştır. İnsan ve lokasyon bazlı kaynak verimlikleri ile tedavi edilen hasta sayısının maksimize edilmesi adına 1 doktor, 1 hemşire ve 2 yatağın çalıştırılmasıyla personel ve lokasyon verimliliklerine ve tedavi edilen hasta sayısına ait optimum değerler sırasıyla %51, %61 ve 275 olarak hesaplanmıştır. Sonuç olarak, bu çalışma ile dinamik ve karmaşık yapılı bir sağlık kuruluşuna ait verimlilik ve optimum sağlık kaynak sayısı değerlerinin hesaplanması için geliştirilen simülasyon modeli sağlık yönetimi bakımından önemli sonuçlar sağlamıştır.

Anahtar Kelimeler

• sağlık kaynakları
• verimlilik
• kesikli-olay simülasyon
• optimum
• istatistiksel deney tasarımı
• acil servis

Kaynakça

1. [1] A. Atalan and C. C. Dönmez, “Optimizing experimental simulation design for the emergency departments,” Brazilian J. Oper. Prod. Manag., vol. 17, no. 4, pp. 1–13, 2020, doi: 10.14488/BJOPM.2020.026.
2. [2] S. Novak and N. Djordjevic, “Information system for evaluation of healthcare expenditure and health monitoring,” Phys. A Stat. Mech. its Appl., vol. 520, pp. 72–80, Apr. 2019, doi: 10.1016/j.physa.2019.01.007.
3. [3] A. Atalan, C. Ç. Dönmez, and Y. Ayaz Atalan, “Yüksek-Eğitimli Uzman Hemşire İstihdamı ile Acil Servis Kalitesinin Yükseltilmesi için Simülasyon Uygulaması: Türkiye Sağlık Sistemi,” Marmara Fen Bilim. Derg., vol. 30,
4. [4] M. Raunak, L. Osterweil, A. Wise, L. Clarke, and P. Henneman, “Simulating patient flow through an emergency department using process-driven discrete event simulation,” in Proceedings of the 2009 ICSE Workshop on Software Engineering in Health Care, SEHC 2009, 2009. doi: 10.1109/SEHC.2009.5069608.
5. [5] F. Sabbadini, M. De Oliveira, A. Gonçalves, J. A. Araujo, J. Glenio Barros, and R. B. Ribeiro, “A Method to Improve the Accessibility and Quality of a Brazilian Public Emergency Hospital Service,” in International Conference on Industrial Engineering and Operations Management, 2014, pp. 1427–1436. doi: 978-0-9855497-1-8.
6. [6] S. Mostafa and N. Chileshe, “Application of discrete-event simulation to investigate effects of client order behaviour on off-site manufacturing performance in Australia,” Archit. Eng. Des. Manag., vol. 14, no. 1–2, pp. 139–157, Mar. 2018, doi: 10.1080/17452007.2017.1301367.
7. [7] S. Çelen, “Sanayi 4.0 ve simülasyon,” Int. J. 3D Print. Technol. Digit. Ind., vol. 1, no. 1, pp. 9–26, 2017.
8. [8] T. Altiok and B. Melamed, Simulation Modeling and Analysis with Arena, 1st ed. Academic Press, 2007.

9. [9] W. D. Kelton, Simulation with Arena, 4th ed. Boston: Mass: WCB/McGraw-Hill, 2004.
10. [10] M. A. Ahmed and T. M. Alkhamis, “Simulation optimization for an emergency department healthcare unit in Kuwait,” Eur. J. Oper. Res., vol. 198, no. 3, pp. 936–942, 2009.
11. [11] A. Atalan, “A cost analysis with the discrete‐event simulation application in nurse and doctor employment management,” J. Nurs. Manag., vol. 30, no. 3, pp. 733–741, Apr. 2022, doi: 10.1111/jonm.13547.
12. [12] A. Atalan and A. Keskin, “Estimation of the utilization rates of the resources of a dental clinic by simulation,” Sigma J. Eng. Nat. Sci. – Sigma Mühendislik ve Fen Bilim. Derg., vol. 41, no. 2, pp. 423–432, 2023, doi: 10.14744/sigma.2023.00045.
13. [13] S. Panicacci, M. Donati, F. Profili, P. Francesconi, and L. Fanucci, “Trading-Off Machine Learning Algorithms towards Data-Driven Administrative-Socio-Economic Population Health Management,” Computers, vol. 10, no. 1, p. 4, Dec. 2020, doi: 10.3390/computers10010004.
14. [14] M. Farahani et al., “Impact of health system inputs on health outcome: A multilevel longitudinal analysis of Botswana national antiretroviral program (2002-2013),” PLoS One, 2016, doi: 10.1371/journal.pone.0160206.
15. [15] A. Atalan, “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050,” Gümüşhane Üniversitesi Sağlık Bilim. Derg., vol. 9, no. 1, pp. 8–16, Apr. 2020, doi: 10.37989/gumussagbil.538111.
16. [16] Z. Ceylan and A. Atalan, “Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection,” J. Forecast., vol. 40, no. 2, pp. 279–290, Mar. 2021, doi: 10.1002/for.2747.
17. [17] A. Atalan, “Türkiye Sağlık Ekonomisi için İstatistiksel Çok Amaçlı Optimizasyon Modelinin Uygulanması,” İşletme Ekon. ve Yönetim Araştırmaları Derg., vol. 1, no. 1, pp. 34–51, 2018, [Online]. Available: http://dergipark.gov.tr/download/article-file/414076
18. [18] N. R. Hoot et al., “Forecasting Emergency Department Crowding: A Discrete Event Simulation,” Ann. Emerg. Med., 2008, doi: 10.1016/j.annemergmed.2007.12.011.
19. [19] N. Khurma, G. M. Bacioiu, and Z. J. Pasek, “Simulation-based verification of lean improvement for emergency room process,” in 2008 Winter Simulation Conference, 2008, pp. 1490–1499.
20. [20] A. Azadeh, M. Baghersad, M. H. Farahani, and M. Zarrin, “Semi-online patient scheduling in pathology laboratories,” Artif. Intell. Med., vol. 64, no. 3, pp. 217–226, 2015.
21. [21] C. Baril, V. Gascon, and S. Cartier, “Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments,” Comput. Ind. Eng., vol. 78, pp. 285–298, 2014.
22. [22] P. Devapriya et al., “StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions,” J. Med. Syst., vol. 39, no. 10, p. 130, Oct. 2015, doi: 10.1007/s10916-015-0325-0.
23. [23] S. Enayati, M. E. Mayorga, H. K. Rajagopalan, and C. Saydam, “Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers,” Omega, vol. 79, pp. 67–80, Sep. 2018, doi: 10.1016/J.OMEGA.2017.08.001.
24. [24] A. H. Briggs and A. M. Gray, “Handling uncertainty when performing economic evaluation of healthcare interventions,” Health Technology Assessment. 1999.
25. [25] J. R. Swisher and S. H. Jacobson, “Evaluating the design of a family practice healthcare clinic using discrete-event simulation.,” Health Care Manag. Sci., vol. 5, no. 2, pp. 75–88, Apr. 2002, doi: 10.1023/a:1014464529565.
26. [26] C. Standridge and M. Wynne, “Validation of production system throughput potential and simulation experiment design,” Int. J. Prod. Manag. Eng., vol. 9, no. 1, p. 15, Jan. 2021, doi: 10.4995/ijpme.2021.14483.
27. [27] J. A. Montevechi, R. Costa, F. Leal, and A. Pinho, “Economic evaluation of scenarios for manufacturing systems using discrete event simulation based experiments,” Brazilian J. Oper. Prod. Manag., vol. 7, no. 1 SE-Articles, pp. 77–103, Jul. 2010, [Online]. Available: https://bjopm.emnuvens.com.br/bjopm/article/view/V7N1A4
28. [28] W. D. Kelton and R. R. Barton, “Experimental Design for Simulation,” in Proceedings of the 2003 Winter Simulation Conference, 2003.
29. [29] Y. Ayaz Atalan, M. Tayanç, K. Erkan, and A. Atalan, “Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey,” Sustainability, vol. 12, no. 15, p. 6017, Jul. 2020, doi: 10.3390/su12156017.
30. [30] K. Hinkelmann and D. C. Montgomery, Design and Analysis of Experiments, 8th ed. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. doi: 10.1002/9781118147634.
31. [31] C. Baril, V. Gascon, and D. Vadeboncoeur, “Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department,” J. Oper. Res. Soc., vol. 70, no. 12, pp. 2019–2038, Dec. 2019, doi: 10.1080/01605682.2018.1510805.
32. [32] A. Atalan and H. Şahin, “Design of experiments optimization application in physics: a case study of the damped driven pendulum experiment,” Sigma J. Eng. Nat. Sci. – Sigma Mühendislik ve Fen Bilim. Derg., vol. 39, no. 3, pp. 322–330, 2021, doi: 10.14744/sigma.2021.00020.
33. [33] A. Atalan, Z. Çınar, and M. Çınar, “A trendline analysis for healthcare expenditure per capita of OECD members,” Sigma J. Eng. Nat. Sci., vol. 10, no. 3, pp. 23–35, 2020.