Araştırma Makalesi
BibTex RIS Kaynak Göster

NÜFUS BÜYÜME PROJEKSİYONU İLE BÜTÜNLEŞİK SİMÜLASYON TABANLI POLİKLİNİK KAPASİTE YÖNETİMİ

Yıl 2020, , 420 - 438, 31.12.2020
https://doi.org/10.46465/endustrimuhendisligi.767201

Öz

Hastanelerdeki insan kaynakları ve bütçe gibi kısıtlı kaynaklar, artan hastane taleplerini karşılamak için yetersiz kalabilmekte ve bu durum hastanelerdeki sağlık hizmeti sağlayıcıları için yoğun iş yüküne neden olabilmektedir. Travma ve ortopedi poliklinikleri İngiltere’deki hastanelerde en yüksek hasta aktivitesine ve takipli tedavi sayısına sahiptir. Bu çalışma, tam teşekküllü bir İngiliz hastanesinde travma ve ortopedi polikliniğinin projeksiyonu için klinik kullanım oranlarının hesaplanmasında Ulusal İstatistik Ofisi ile entegre simülasyon tabanlı bir karar destek sisteminin geliştirilmesi amaçlanmıştır. Hastanenin hizmet verdiği yerleşim bölgesinin yıllar itibari ile büyüme projeksiyonları göz önünde bulundurularak, hastanenin gelecekteki üç yıllık talebi ele alınmıştır. Senaryo analizinde, klinik kullanım oranını etkileyen üç parametre (Talep, klinik zaman dilimi ve hasta takip sayısı) içeren deneysel bir analiz dikkate alınmıştır. En düşük, ortalama ve en yüksek olmak üzere üç farklı klinik kullanım oranları, öngörülen her bir yıl için toplam 8 deneyden oluşan senaryo analizi yoluyla travma ve ortopedi polikliniği için hesaplanmıştır. Bu çalışma da ayrıca tedavi süreleri ve doktorların yıllık tam zamanlı çalışma süreleri dikkate alınarak öngörülen her bir yıl için ihtiyaç duyulan doktor sayıları belirlenmiştir. Geliştirilen bu karar destek sistemi, klinik kullanım oranlarının polikliniklerde daha iyi anlaşılması ve gelecekte ihtiyaç duyulacak personel, yeterli bütçe ve ekipman gibi kaynak ihtiyaçlarının önceden tespit edilmesi ve daha iyi kaynak planlamalarının yapılabilmesi için hastane yönetimine bir öngörü sunmaktadır.

Kaynakça

  • Ahmad, N., Ghani, N.A., Kamil, A.A., Tahar, R.M. & Teo, A.H. (2012). Evaluating emergency department resource capacity using simulation. Modern Applied Science, 6, 9-19. Retrieved from http://doi.org/10.5539/mas.v6n11p9
  • Arefeh, M., Barghash, M.A., Haddad, N., Musharbash, N., Nashawati, D., Al-Bashir, A. & Assaf F. (2018). Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center. Journal of Healthcare Engineering, 2018. Retrieved from https://doi.org/10.1155/2018/3832151
  • Babashov, V., Aivas, L., Begen, M.A., Cao, J.Q., Rodriques, G., D’Souza, D., Lock, M. & Zaric G.S. (2017). Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: A discrete-event Simulation Model (Radiation Treatment Planning). Clinical Oncology, (29), 385-391. Retrieved from https://doi.org/10.1016/j.clon.2017.01.039
  • Banks J., Carson II J.S., Nelson B.L. & Nicol D.M. (2005). Discrete-Event System Simulation. New Jersey, ABD: Pearson.
  • Bhattacharjee, P. & Ray P. (2014). Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, (78), 299-312. Retrieved from https://doi.org/10.1016/j.cie.2014.04.016
  • Bowers, J. & Mould, G. (2004). Managing uncertainty in orthopaedic trauma theatres. European Journal of Operation Research, 154 (3), 599–608. Retrieved from https://doi.org/10.1016/S0377-2217(02)00816-0
  • Bowers, J. & Mould, G. (2005). Ambulatory and orthopaedic capacity planning. Health Care Management Science, 8 (1), 41–47. Retrieved from https://doi.org/10.1007/s10729-005-5215-4
  • Cappanera, P., Visintin, F. & Banditori, C. (2014). Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach. International Journal of Production Economics, (158), 179–196. Retrieved from https://doi.org/10.1016/j.ijpe.2014.08.002
  • Cracknell R. (2010). The ageing population, Key issues for the new parliament 2010, 44.
  • Demir, E., Gunal, M. & Southern, D. (2017). Demand and capacity modelling for acute services using discrete event simulation. Health Systems, 6, 33-40. Retrieved from https://doi.org/10.1057/hs.2016.1
  • Gunal, M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17-25. Retrieved from https://doi.org/10.1057/hs.2012.8
  • Hamm C. (2010). The coalition government’s plans for the NHS in England. British Medical Journal, (341), 3790.
  • Harper, A., Navonil, M. & Feeney M. (2017). A hybrid approach using forecasting and discrete-event simulation for endoscopy services. Proceedings of the 2017 Winter Simulation Conference, 1583–1594, Las Vegas-USA.
  • Hong, N.C. & Ghani, N.A. (2006). A Model for Predicting Average Ambulance Service Travel Times in Penang Island. Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Penang Island-Malaysia.
  • Kaushal, A., Zhao, Y., Peng, Q., Strome, T., Weldon, E., Zhnag, M. & Chochinov A. (2015). Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department. Socio-Economic Planning Sciences, (50), 18-31. Retrieved from https://doi.org/10.1016/j.seps.2015.02.002
  • Kelton D., Sadowski R.P. & Sadowski D.A. (2001). Simulation with Arena. New York, ABD: McGraw Hill.
  • Law, A.M. & Kelton, W.D. (2000). Simulation Modeling and Analysis. McGraw – Hill.
  • Mallor, F. & Azcarate, C. (2014). Combining optimization with simulation to obtain credible models for intensive care units. Annals of Operations Research, (221), 255–271. Retrieved from https://doi.org/10.1007/s10479-011-1035-8
  • Mathwave Technologies, (2018). How to select the best fitting distribution using the goodness of fit tests. Erişim adresi: http://www.mathwave.com/articles/distribution-fitting-goodness-of-fit.html. Erişim tarihi: Mart 26, 2018.
  • Monks, T., Worthington, D., Allen, M., Pitt, M., Stein, K. & James, M.A. (2016). A modelling tool for capacity planning in acute and community stroke services. BMC Health Service Research, 16 (530). Retrieved from https://doi.org/10.1186/s12913-016-1789-4
  • National Health Services England, (2018). Quarterly hospital activity. Erişim adresi: https://www.england.nhs.uk/statistics/statistical-work-areas/hospital-activity/quarterly-hospital-activity/. Yayın tarihi Mayıs 25, 2018. Erişim tarihi: Temmuz 2, 2018.
  • NHS Improvement, (2017). Equality for all: Delivering safe care – seven days a week. Erişim adresi: https://www.england.nhs.uk/improvement-hub/wp-content/uploads/sites/44/2017/11/Equality-for-all-Delivering-safe-care-seven-days-a-week.pdf. Yayın tarihi Kasım 30, 2017. Erişim tarihi: Haziran 6, 2018.
  • Office for National Statistics, (2017). Statistical bulletin: Population estimates for the UK, England and Wales, Scotland and Northern Ireland: mid-2016. Erişim adresi: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2016#toc. Yayın tarihi Haziran 22, 2017. Erişim tarihi: Haziran 6, 2018.
  • Ordu, M., Demir, E. & Tofallis, C. (2017). A Discrete Event Simulation Modelling to Capture Demand and Capacity in an Accident and Emergency Department. 19th International Conference on Industrial Engineering and Operations Research, 1434, Zurich-Switzerland.
  • Ordu, M., Demir, E. & Tofallis, C. (2018). A Discrete Event Simulation Model to Manage Bed Usage for Non-Elective Admissions in a Geriatric Medicine Speciality. International Journal of Industrial and Systems Engineering, 12 (3), 239–244. Retrieved from https://panel.waset.org/Publication/10008800
  • Pidd, M. (2004). Computer Simulation in Management Science. Chichester, England: John Wiley & Sons.
  • 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), 276–293. Retrieved from https://doi.org/10.1016/j.ejor.2015.05.043
  • Rohleder, T.R., Lewkonia, P., Bischak, D.P., Duffy P. & Hendijani R. (2011). Using simulation modelling to improve patient flow at an outpatient orthopaedic clinic. Health Care Management Science, (14), 135-145. Retrieved from https://doi.org/10.1007/s10729-010-9145-4
  • Royal College of Physicians, (2015). Work and wellbeing in the NHS: why staff health matters to patient care. Erişim adresi: https://www.rcpsych.ac.uk/pdf/RCP-%20WorkWellbeingNHS.pdf. Yayın tarihi Ekim 12, 2015. Erişim tarihi: Temmuz 23, 2018.
  • Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L. & Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers & Industrial Engineering, (80), 72–79. Retrieved from https://doi.org/10.1016/j.cie.2014.11.021
  • Simul8, (2018). Erişim adresi: https://www.simul8.com/. Erişim tarihi: Ağustos 6, 2018.
  • Zhu, Z., Hen, B.H. & Teow, K.L. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25, 134-144. Retrieved from https://doi.org/10.1108/09526861211198290

SIMULATION-BASED OUTPATIENT CLINIC CAPACITY MANAGEMENT INTEGRATED WITH POPULATION GROWTH PROJECTION

Yıl 2020, , 420 - 438, 31.12.2020
https://doi.org/10.46465/endustrimuhendisligi.767201

Öz

Limited resources (i.e. human resources and budget) in hospitals may be inadequate to meet increasing demand and this causes intense workload for healthcare providers in hospitals. Trauma and orthopedics outpatient clinics involve the highest patient activity and number of follow-up treatments in hospitals in England. This study aims to develop a simulation-based decision support system integrated with the Office for National Statistics in order to calculate clinic utilization rates of a trauma and orthopedics outpatient clinic for projection in England. The future 3-year demand of the hospital was tackled by considering growth projections of the region where the hospital serves. An experimental analysis including 3 parameters (i.e. demand, clinic slot and number of follow-up) affecting the clinic utilization rate, was taken into account in the scenario analysis. Three different clinic utilization rates (i.e. lowest, average and highest) were calculated for the trauma and orthopedics outpatient clinic through the scenario analysis which involves a total of 8 experiments for each projected year. In addition, number of consultants required for each projected year in this study were specified by considering treatment time and consultants' full time equivalent working time. Developed this study provides an insight into hospital management for better understanding of clinic utilization rates in outpatient clinics, for proactively determining required resources (i.e. staff, adequate budget and equipment) in future, and for better resource planning.

Kaynakça

  • Ahmad, N., Ghani, N.A., Kamil, A.A., Tahar, R.M. & Teo, A.H. (2012). Evaluating emergency department resource capacity using simulation. Modern Applied Science, 6, 9-19. Retrieved from http://doi.org/10.5539/mas.v6n11p9
  • Arefeh, M., Barghash, M.A., Haddad, N., Musharbash, N., Nashawati, D., Al-Bashir, A. & Assaf F. (2018). Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center. Journal of Healthcare Engineering, 2018. Retrieved from https://doi.org/10.1155/2018/3832151
  • Babashov, V., Aivas, L., Begen, M.A., Cao, J.Q., Rodriques, G., D’Souza, D., Lock, M. & Zaric G.S. (2017). Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: A discrete-event Simulation Model (Radiation Treatment Planning). Clinical Oncology, (29), 385-391. Retrieved from https://doi.org/10.1016/j.clon.2017.01.039
  • Banks J., Carson II J.S., Nelson B.L. & Nicol D.M. (2005). Discrete-Event System Simulation. New Jersey, ABD: Pearson.
  • Bhattacharjee, P. & Ray P. (2014). Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, (78), 299-312. Retrieved from https://doi.org/10.1016/j.cie.2014.04.016
  • Bowers, J. & Mould, G. (2004). Managing uncertainty in orthopaedic trauma theatres. European Journal of Operation Research, 154 (3), 599–608. Retrieved from https://doi.org/10.1016/S0377-2217(02)00816-0
  • Bowers, J. & Mould, G. (2005). Ambulatory and orthopaedic capacity planning. Health Care Management Science, 8 (1), 41–47. Retrieved from https://doi.org/10.1007/s10729-005-5215-4
  • Cappanera, P., Visintin, F. & Banditori, C. (2014). Comparing resource balancing criteria in master surgical scheduling: A combined optimisation-simulation approach. International Journal of Production Economics, (158), 179–196. Retrieved from https://doi.org/10.1016/j.ijpe.2014.08.002
  • Cracknell R. (2010). The ageing population, Key issues for the new parliament 2010, 44.
  • Demir, E., Gunal, M. & Southern, D. (2017). Demand and capacity modelling for acute services using discrete event simulation. Health Systems, 6, 33-40. Retrieved from https://doi.org/10.1057/hs.2016.1
  • Gunal, M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17-25. Retrieved from https://doi.org/10.1057/hs.2012.8
  • Hamm C. (2010). The coalition government’s plans for the NHS in England. British Medical Journal, (341), 3790.
  • Harper, A., Navonil, M. & Feeney M. (2017). A hybrid approach using forecasting and discrete-event simulation for endoscopy services. Proceedings of the 2017 Winter Simulation Conference, 1583–1594, Las Vegas-USA.
  • Hong, N.C. & Ghani, N.A. (2006). A Model for Predicting Average Ambulance Service Travel Times in Penang Island. Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Penang Island-Malaysia.
  • Kaushal, A., Zhao, Y., Peng, Q., Strome, T., Weldon, E., Zhnag, M. & Chochinov A. (2015). Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department. Socio-Economic Planning Sciences, (50), 18-31. Retrieved from https://doi.org/10.1016/j.seps.2015.02.002
  • Kelton D., Sadowski R.P. & Sadowski D.A. (2001). Simulation with Arena. New York, ABD: McGraw Hill.
  • Law, A.M. & Kelton, W.D. (2000). Simulation Modeling and Analysis. McGraw – Hill.
  • Mallor, F. & Azcarate, C. (2014). Combining optimization with simulation to obtain credible models for intensive care units. Annals of Operations Research, (221), 255–271. Retrieved from https://doi.org/10.1007/s10479-011-1035-8
  • Mathwave Technologies, (2018). How to select the best fitting distribution using the goodness of fit tests. Erişim adresi: http://www.mathwave.com/articles/distribution-fitting-goodness-of-fit.html. Erişim tarihi: Mart 26, 2018.
  • Monks, T., Worthington, D., Allen, M., Pitt, M., Stein, K. & James, M.A. (2016). A modelling tool for capacity planning in acute and community stroke services. BMC Health Service Research, 16 (530). Retrieved from https://doi.org/10.1186/s12913-016-1789-4
  • National Health Services England, (2018). Quarterly hospital activity. Erişim adresi: https://www.england.nhs.uk/statistics/statistical-work-areas/hospital-activity/quarterly-hospital-activity/. Yayın tarihi Mayıs 25, 2018. Erişim tarihi: Temmuz 2, 2018.
  • NHS Improvement, (2017). Equality for all: Delivering safe care – seven days a week. Erişim adresi: https://www.england.nhs.uk/improvement-hub/wp-content/uploads/sites/44/2017/11/Equality-for-all-Delivering-safe-care-seven-days-a-week.pdf. Yayın tarihi Kasım 30, 2017. Erişim tarihi: Haziran 6, 2018.
  • Office for National Statistics, (2017). Statistical bulletin: Population estimates for the UK, England and Wales, Scotland and Northern Ireland: mid-2016. Erişim adresi: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2016#toc. Yayın tarihi Haziran 22, 2017. Erişim tarihi: Haziran 6, 2018.
  • Ordu, M., Demir, E. & Tofallis, C. (2017). A Discrete Event Simulation Modelling to Capture Demand and Capacity in an Accident and Emergency Department. 19th International Conference on Industrial Engineering and Operations Research, 1434, Zurich-Switzerland.
  • Ordu, M., Demir, E. & Tofallis, C. (2018). A Discrete Event Simulation Model to Manage Bed Usage for Non-Elective Admissions in a Geriatric Medicine Speciality. International Journal of Industrial and Systems Engineering, 12 (3), 239–244. Retrieved from https://panel.waset.org/Publication/10008800
  • Pidd, M. (2004). Computer Simulation in Management Science. Chichester, England: John Wiley & Sons.
  • 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), 276–293. Retrieved from https://doi.org/10.1016/j.ejor.2015.05.043
  • Rohleder, T.R., Lewkonia, P., Bischak, D.P., Duffy P. & Hendijani R. (2011). Using simulation modelling to improve patient flow at an outpatient orthopaedic clinic. Health Care Management Science, (14), 135-145. Retrieved from https://doi.org/10.1007/s10729-010-9145-4
  • Royal College of Physicians, (2015). Work and wellbeing in the NHS: why staff health matters to patient care. Erişim adresi: https://www.rcpsych.ac.uk/pdf/RCP-%20WorkWellbeingNHS.pdf. Yayın tarihi Ekim 12, 2015. Erişim tarihi: Temmuz 23, 2018.
  • Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L. & Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers & Industrial Engineering, (80), 72–79. Retrieved from https://doi.org/10.1016/j.cie.2014.11.021
  • Simul8, (2018). Erişim adresi: https://www.simul8.com/. Erişim tarihi: Ağustos 6, 2018.
  • Zhu, Z., Hen, B.H. & Teow, K.L. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25, 134-144. Retrieved from https://doi.org/10.1108/09526861211198290
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Endüstri Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Muhammed Ordu 0000-0003-4764-9379

Eren Demir Bu kişi benim 0000-0002-9087-7169

Chris Tofallis Bu kişi benim 0000-0001-6150-0218

Yayımlanma Tarihi 31 Aralık 2020
Kabul Tarihi 30 Kasım 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Ordu, M., Demir, E., & Tofallis, C. (2020). NÜFUS BÜYÜME PROJEKSİYONU İLE BÜTÜNLEŞİK SİMÜLASYON TABANLI POLİKLİNİK KAPASİTE YÖNETİMİ. Endüstri Mühendisliği, 31(3), 420-438. https://doi.org/10.46465/endustrimuhendisligi.767201

19736      14617      26287       15235           15236           15240      15242