Araştırma Makalesi
BibTex RIS Kaynak Göster

Acil Servislerde Talebin Zaman Serileri Modelleri ile Tahmin Edilmesi

Yıl 2018, Cilt: 10 Sayı: 1, 66 - 77, 29.01.2017
https://doi.org/10.29137/umagd.419661

Öz

Acil
servislerde talebin etkin olarak yönetilmesi hastane yöneticilerinin (karar
vericilerin) önemli bir görevi haline gelmektedir. Günümüzde, hastane
yöneticileri, hasta akışının ve aşırı kalabalıklaşmanın en iyi şekilde
yönetilebilmesi için strateji geliştirmeye odaklanmaktadırlar. Acil durumlarda
zaman çok kritiktir ve yaşam ve ölüm arasındaki farkı ifade eder. Bu nedenle
acil servislerde önemli oranda kaynak bulundurulması gerekmektedir, fakat
kaynaklar sınırlıdır. Bu bağlamda, acil servislere talebin en az hata ile
tahmin edilmesi, operasyonların planlanması ve yönetilmesinde büyük önem arz
etmektedir. Bu çalışmanın amacı; İzmir, Türkiye’deki büyük ölçekli bir eğitim
hastanesi acil servisinde talebi zaman serileri modelleri ile tahmin etmektir.
Kış aylarında acil servis talebinde ciddi bir artş beklendiği için, talep
tahminlemede kış aylarına odaklanılmıştır. Hastane veri tabanı kullanılarak, 1
Aralık 2016-28 Şubat 2017 arasında acil servise yapılan başvurular elde
edilmiştir. 1 Aralık-14 Şubat arasındaki 76 günlük veri farklı otoregresif
entegre(bütünlenen) hareketli ortalama (ARIMA) ve mevsimsel otoregresif entegre
hareketli ortalama (SARIMA) modellerinin uygunluk ve test edilmesinde
kullanılırken, kalan 14 günlük veri de uygun modellerin performanslarının
karşılaştırılmasında kullanılmıştır. Günlük ve periyodik (8-saat aralıkları)
tahminler elde edilmiş ve karşılaştırılmıştır. Bu çalışma acil servis hasta
sayısının tahminlemesinde zaman serileri modellerinin uygun olduğunu
göstermektedir.

Kaynakça

  • Abdel-Aal, R. E., & Mangoud, A. M. (1998). Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis. Computer Methods and Programs in Biomedicine, 56(3), 235-247.
  • Albayrak, A. S. (2010). ARIMA forecasting of primary energy production and consumption in Turkey: 1923-2006. Enerji, piyasa ve düzenleme, 1(1), 24-50.
  • Aydemir, E., Karaatlı, M., Yılmaz, G., & Aksoy, S. (2014). 112 acil çağrı merkezine gelen çağrı sayılarını belirleyebilmek için bir yapay sinir ağları tahminleme modeli geliştirilmesi. Pamukkale Üniversitesi Mühendislik Fakültesi Dergisi 20(5):145-149
  • Bair, A. E., Song, W. T., Chen, Y. C., & Morris, B. A. (2010). The impact of inpatient boarding on ED efficiency: a discrete-event simulation study. Journal of medical systems, 34(5), 919-929.
  • Baker, D. W., Stevens, C. D., & Brook, R. H. (1991). Patients who leave a public hospital emergency department without being seen by a physician: causes and consequences. Jama, 266(8), 1085-1090.
  • Balaguer, E., Palomares, A., Soria, E., & Martín-Guerrero, J. D. (2008). Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks. Expert Systems with Applications, 34(1), 665-672.
  • Barişçi, N. (2008). The adaptive ARMA analysis of EMG signals. Journal of medical systems, 32(1), 43-50.
  • Bergs, J., Heerinckx, P., & Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. International emergency nursing, 22(2), 112-115.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Capan, M., Hoover, S., Jackson, E. V., Paul, D., & Locke, R. (2016). Time series analysis for forecasting hospital census: Application to the Neonatal Intensive Care Unit. Applied clinical informatics, 7(2), 275.
  • Cooke, M. W., Wilson, S., Halsall, J., & Roalfe, A. (2004). Total time in English accident and emergency departments is related to bed occupancy. Emergency Medicine Journal, 21(5), 575-576.
  • Çuhadar, M., Güngör, İ., & Göksu, A. (2009). Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1).
  • Derose, S. F., Gabayan, G. Z., Chiu, V. Y., Yiu, S. C., & Sun, B. C. (2014). Emergency department crowding predicts admission length-of-stay but not mortality in a large health system. Medical care, 52(7), 602.
  • Dilaver, Z., & Hunt, L. C. (2011a). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
  • Dilaver, Z., & Hunt, L. C. (2011b). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39(6), 3117-3127.
  • Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708.
  • Erdogdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy policy, 35(2), 1129-1146.
  • Hamzaçebi, C., & Kutay, F. (2004). Yapay sinir ağlari ile Türkiye elektrik enerjisi tüketiminin 2010 yilina kadar tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 19(3).
  • Hertzum, M. (2017). Forecasting Hourly Patient Visits in the Emergency Department to Counteract Crowding. The Ergonomics Open Journal, 10(1).
  • Howard, M. S., Davis, B. A., Anderson, C., Cherry, D., Koller, P., & Shelton, D. (2005). Patients' perspective on choosing the emergency department for nonurgent medical care: a qualitative study exploring one reason for overcrowding. Journal of Emergency Nursing, 31(5), 429-435.
  • De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting, 22(3), 443-473. Güngör, İ., & Çuhadar, M. (2005). Antalya İline Yönelik Alman Turist Talebinin Yapay Sinir Ağları Yöntemiyle Tahmini. Gazi Üniversitesi Ticaret ve Turizm Eğitim Fakültesi Dergisi, 1, 84-99.
  • Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2), 159-170.
  • Jones, S. A., Joy, M. P., & Pearson, J. (2002). Forecasting demand of emergency care. Health care management science, 5(4), 297-305.
  • Jones, S. S., Evans, R. S., Allen, T. L., Thomas, A., Haug, P. J., Welch, S. J., & Snow, G. L. (2009). A multivariate time series approach to modeling and forecasting demand in the emergency department. Journal of biomedical informatics, 42(1), 123-139.
  • Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of medical systems, 38(9), 107.
  • Karahan, M. (2015). Yapay Sinir Ağları Metodu İle İhracat Miktarlarının Tahmini: ARIMA ve YSA Metodunun Karşılaştırmalı Analizi. Ege Academic Review, 15(2).
  • Kennedy, K., Salzillo, M., Olinsky, A., & Quinn, J. (2013). Forecasting patient volume for a large hospital system: A comparison of the periodicity of time series data and forecasting approaches. In Advances in Business and Management Forecasting (pp. 33-44). Emerald Group Publishing Limited.
  • Kim, K., Lee, C., O’Leary, K., Rosenauer, S., & Mehrotra, S. (2014). Predicting patient volumes in hospital medicine: A comparative study of different time series forecasting methods. Tech. rep., Northwestern University.
  • Kolker, A. (2008). Process modeling of emergency department patient flow: Effect of patient length of stay on ED diversion. Journal of Medical Systems, 32(5), 389-401.
  • Lewis, C. D. (1982). Industrial and business forecasting methods: A Radical guide to exponential smoothing and curve fitting. Colin David.
  • Li, G., Lau, J. T., McCarthy, M. L., Schull, M. J., Vermeulen, M., & Kelen, G. D. (2007). Emergency department utilization in the United States and Ontario, Canada. Academic Emergency Medicine, 14(6), 582-584.
  • Lin, W. T. (1989). Modeling and forecasting hospital patient movements: Univariate and multiple time series approaches. International Journal of Forecasting, 5(2), 195-208.
  • Özüdoğru, A. G., & Görener, A. (2015). Sağlık sektöründe talep tahmini üzerine bir uygulama. http://hdl.handle.net/11467/940.
  • Özüdoğru, A. G., & Gorener, A. (2016). Method Selection for Demand Forecasting: Application in a Private Hospital. International Journal of Decision Sciences & Applications-IJDSA, 1(1), 22-34.
  • Rowe, B. H., Channan, P., Bullard, M., Blitz, S., Saunders, L. D., Rosychuk, R. J., ... & Holroyd, B. R. (2006). Characteristics of patients who leave emergency departments without being seen. Academic Emergency Medicine, 13(8), 848-852.
  • Sariyer, G., Ataman, M. G., Sofuoğlu, T., & Sofuoğlu, Z. (2017a). Does ambulance utilization differ between urban and rural regions: a study of 112 services in a populated city, Izmir. Journal of Public Health, 1-7.
  • Sariyer, G., Ataman, M. G., Akay, S., Sofuoglu, T., & Sofuoglu, Z. (2017b). An analysis of Emergency Medical Services demand: Time of day, day of the week, and location in the city. Turkish Journal of Emergency Medicine, 17(2), 42-47.
  • Schull, M. J., Mamdani, M. M., & Fang, J. (2005). Influenza and emergency department utilization by elders. Academic emergency medicine, 12(4), 338-344.
  • Solak, A. O. (2013). Türkiye'nin Toplam Petrol Talebi ve Ulaştırma Sektörü Petrol Talebinin Arıma Modeli İle Tahmin Edilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3).
  • Sun, B. C., Mohanty, S. A., Weiss, R., Tadeo, R., Hasbrouck, M., Koenig, W., ... & Asch, S. (2006). Effects of hospital closures and hospital characteristics on emergency department ambulance diversion, Los Angeles County, 1998 to 2004. Annals of emergency medicine, 47(4), 309-316.
  • Tandberg, D., & Qualls, C. (1994). Time series forecasts of emergency department patient volume, length of stay, and acuity. Annals of emergency medicine, 23(2), 299-306.
  • Temuçin, T., & Temiz, İ. (2016). Türkiye dış ticaret ihracat hacminin projeksiyonu: Holt-Winters ve Box-Jenkins modellerinin bir kıyaslaması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(3).
  • Yasemin, B. E. N. L. (2002). Finansal Başarısızlığın Tahmininde Yapay Sinir Ağı Kullanımı ve MKB’de Bir Uygulama. Muhasebe Bilim Dünyası Dergisi, 17-30.
  • Yıldız, B. (2001). Finansal başarısızlığın öngörülmesinde yapay sinir ağı kullanımı ve halka açık şirketlerde ampirik bir uygulama. İMKB Dergisi, 17, 51-67.

Time Series Modelling for Forecasting Demand in the Emergency

Yıl 2018, Cilt: 10 Sayı: 1, 66 - 77, 29.01.2017
https://doi.org/10.29137/umagd.419661

Öz

Managing
demand efficiently in emergency departments (ED) has become an important task
for decision makers of hospitals. Currently, decision makers focus on improving
strategies for optimally managing flow of patients and overcrowding in EDs.
Since time is very critical for emergency situations, and can generally mean
the difference between life and death, EDs need substantial amount of resources
which are indeed limited. In this context, forecasting demand in ED with a
minimum error, has noticeable significance for hospitals in planning and
managing operations. The objective of this paper was to develop time series
models for forecasting demand at the ED of a large scaled training hospital in
Izmir, Turkey. Since in winter periods, a significant increase is expected in
demand, forecasting demand during winter period is focused. By using Electronic
Health Record (EHR) of this hospital, demand in ED during 1st of
December, 2016 to 28th of February, 2017 were obtained. First 76
days data (1st December to 14th February) were used to test
appropriateness and accuracy of different autoregressive integrated moving
average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA)
models, where remaining 14 days were used to test the performance of them.
Daily and periodical (8-hour lengths) forecasts were evaluated and compared.
This study shows how time series models are proper in forecasting patient
volumes in EDs
.

Kaynakça

  • Abdel-Aal, R. E., & Mangoud, A. M. (1998). Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis. Computer Methods and Programs in Biomedicine, 56(3), 235-247.
  • Albayrak, A. S. (2010). ARIMA forecasting of primary energy production and consumption in Turkey: 1923-2006. Enerji, piyasa ve düzenleme, 1(1), 24-50.
  • Aydemir, E., Karaatlı, M., Yılmaz, G., & Aksoy, S. (2014). 112 acil çağrı merkezine gelen çağrı sayılarını belirleyebilmek için bir yapay sinir ağları tahminleme modeli geliştirilmesi. Pamukkale Üniversitesi Mühendislik Fakültesi Dergisi 20(5):145-149
  • Bair, A. E., Song, W. T., Chen, Y. C., & Morris, B. A. (2010). The impact of inpatient boarding on ED efficiency: a discrete-event simulation study. Journal of medical systems, 34(5), 919-929.
  • Baker, D. W., Stevens, C. D., & Brook, R. H. (1991). Patients who leave a public hospital emergency department without being seen by a physician: causes and consequences. Jama, 266(8), 1085-1090.
  • Balaguer, E., Palomares, A., Soria, E., & Martín-Guerrero, J. D. (2008). Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks. Expert Systems with Applications, 34(1), 665-672.
  • Barişçi, N. (2008). The adaptive ARMA analysis of EMG signals. Journal of medical systems, 32(1), 43-50.
  • Bergs, J., Heerinckx, P., & Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. International emergency nursing, 22(2), 112-115.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Capan, M., Hoover, S., Jackson, E. V., Paul, D., & Locke, R. (2016). Time series analysis for forecasting hospital census: Application to the Neonatal Intensive Care Unit. Applied clinical informatics, 7(2), 275.
  • Cooke, M. W., Wilson, S., Halsall, J., & Roalfe, A. (2004). Total time in English accident and emergency departments is related to bed occupancy. Emergency Medicine Journal, 21(5), 575-576.
  • Çuhadar, M., Güngör, İ., & Göksu, A. (2009). Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1).
  • Derose, S. F., Gabayan, G. Z., Chiu, V. Y., Yiu, S. C., & Sun, B. C. (2014). Emergency department crowding predicts admission length-of-stay but not mortality in a large health system. Medical care, 52(7), 602.
  • Dilaver, Z., & Hunt, L. C. (2011a). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
  • Dilaver, Z., & Hunt, L. C. (2011b). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39(6), 3117-3127.
  • Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708.
  • Erdogdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy policy, 35(2), 1129-1146.
  • Hamzaçebi, C., & Kutay, F. (2004). Yapay sinir ağlari ile Türkiye elektrik enerjisi tüketiminin 2010 yilina kadar tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 19(3).
  • Hertzum, M. (2017). Forecasting Hourly Patient Visits in the Emergency Department to Counteract Crowding. The Ergonomics Open Journal, 10(1).
  • Howard, M. S., Davis, B. A., Anderson, C., Cherry, D., Koller, P., & Shelton, D. (2005). Patients' perspective on choosing the emergency department for nonurgent medical care: a qualitative study exploring one reason for overcrowding. Journal of Emergency Nursing, 31(5), 429-435.
  • De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International journal of forecasting, 22(3), 443-473. Güngör, İ., & Çuhadar, M. (2005). Antalya İline Yönelik Alman Turist Talebinin Yapay Sinir Ağları Yöntemiyle Tahmini. Gazi Üniversitesi Ticaret ve Turizm Eğitim Fakültesi Dergisi, 1, 84-99.
  • Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine, 15(2), 159-170.
  • Jones, S. A., Joy, M. P., & Pearson, J. (2002). Forecasting demand of emergency care. Health care management science, 5(4), 297-305.
  • Jones, S. S., Evans, R. S., Allen, T. L., Thomas, A., Haug, P. J., Welch, S. J., & Snow, G. L. (2009). A multivariate time series approach to modeling and forecasting demand in the emergency department. Journal of biomedical informatics, 42(1), 123-139.
  • Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of medical systems, 38(9), 107.
  • Karahan, M. (2015). Yapay Sinir Ağları Metodu İle İhracat Miktarlarının Tahmini: ARIMA ve YSA Metodunun Karşılaştırmalı Analizi. Ege Academic Review, 15(2).
  • Kennedy, K., Salzillo, M., Olinsky, A., & Quinn, J. (2013). Forecasting patient volume for a large hospital system: A comparison of the periodicity of time series data and forecasting approaches. In Advances in Business and Management Forecasting (pp. 33-44). Emerald Group Publishing Limited.
  • Kim, K., Lee, C., O’Leary, K., Rosenauer, S., & Mehrotra, S. (2014). Predicting patient volumes in hospital medicine: A comparative study of different time series forecasting methods. Tech. rep., Northwestern University.
  • Kolker, A. (2008). Process modeling of emergency department patient flow: Effect of patient length of stay on ED diversion. Journal of Medical Systems, 32(5), 389-401.
  • Lewis, C. D. (1982). Industrial and business forecasting methods: A Radical guide to exponential smoothing and curve fitting. Colin David.
  • Li, G., Lau, J. T., McCarthy, M. L., Schull, M. J., Vermeulen, M., & Kelen, G. D. (2007). Emergency department utilization in the United States and Ontario, Canada. Academic Emergency Medicine, 14(6), 582-584.
  • Lin, W. T. (1989). Modeling and forecasting hospital patient movements: Univariate and multiple time series approaches. International Journal of Forecasting, 5(2), 195-208.
  • Özüdoğru, A. G., & Görener, A. (2015). Sağlık sektöründe talep tahmini üzerine bir uygulama. http://hdl.handle.net/11467/940.
  • Özüdoğru, A. G., & Gorener, A. (2016). Method Selection for Demand Forecasting: Application in a Private Hospital. International Journal of Decision Sciences & Applications-IJDSA, 1(1), 22-34.
  • Rowe, B. H., Channan, P., Bullard, M., Blitz, S., Saunders, L. D., Rosychuk, R. J., ... & Holroyd, B. R. (2006). Characteristics of patients who leave emergency departments without being seen. Academic Emergency Medicine, 13(8), 848-852.
  • Sariyer, G., Ataman, M. G., Sofuoğlu, T., & Sofuoğlu, Z. (2017a). Does ambulance utilization differ between urban and rural regions: a study of 112 services in a populated city, Izmir. Journal of Public Health, 1-7.
  • Sariyer, G., Ataman, M. G., Akay, S., Sofuoglu, T., & Sofuoglu, Z. (2017b). An analysis of Emergency Medical Services demand: Time of day, day of the week, and location in the city. Turkish Journal of Emergency Medicine, 17(2), 42-47.
  • Schull, M. J., Mamdani, M. M., & Fang, J. (2005). Influenza and emergency department utilization by elders. Academic emergency medicine, 12(4), 338-344.
  • Solak, A. O. (2013). Türkiye'nin Toplam Petrol Talebi ve Ulaştırma Sektörü Petrol Talebinin Arıma Modeli İle Tahmin Edilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3).
  • Sun, B. C., Mohanty, S. A., Weiss, R., Tadeo, R., Hasbrouck, M., Koenig, W., ... & Asch, S. (2006). Effects of hospital closures and hospital characteristics on emergency department ambulance diversion, Los Angeles County, 1998 to 2004. Annals of emergency medicine, 47(4), 309-316.
  • Tandberg, D., & Qualls, C. (1994). Time series forecasts of emergency department patient volume, length of stay, and acuity. Annals of emergency medicine, 23(2), 299-306.
  • Temuçin, T., & Temiz, İ. (2016). Türkiye dış ticaret ihracat hacminin projeksiyonu: Holt-Winters ve Box-Jenkins modellerinin bir kıyaslaması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(3).
  • Yasemin, B. E. N. L. (2002). Finansal Başarısızlığın Tahmininde Yapay Sinir Ağı Kullanımı ve MKB’de Bir Uygulama. Muhasebe Bilim Dünyası Dergisi, 17-30.
  • Yıldız, B. (2001). Finansal başarısızlığın öngörülmesinde yapay sinir ağı kullanımı ve halka açık şirketlerde ampirik bir uygulama. İMKB Dergisi, 17, 51-67.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Görkem Sarıyer

Yayımlanma Tarihi 29 Ocak 2017
Gönderilme Tarihi 2 Eylül 2017
Yayımlandığı Sayı Yıl 2018 Cilt: 10 Sayı: 1

Kaynak Göster

APA Sarıyer, G. (2017). Acil Servislerde Talebin Zaman Serileri Modelleri ile Tahmin Edilmesi. International Journal of Engineering Research and Development, 10(1), 66-77. https://doi.org/10.29137/umagd.419661
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.