A multi-method patient arrival forecasting outline for hospital emergency departments

References

• Hertzum M. Forecasting hourly patient visits in the emergency department to counteract crowding. Ergonom Open J. 2017;10(1):1–13.
   Google Scholar
• Carvalho-Silva M, Monteiro MTT, de Sá-Soares F, et al. Assessment of forecasting models for patients arrival at emergency department. Oper Res Health Care. 2017;18:112–118.
   Web of Science ®Google Scholar
• Xu Q, Tsui KL, Jiang W, et al. A hybrid approach for forecasting patient visits in emergency department. Qual Reliab Eng Int. 2016;32(8):2751–2759.
   Web of Science ®Google Scholar
• Gul M, Guneri AF. Planning the future of emergency departments: forecasting ED patient arrivals by using regression and neural network models. Int J Indust Eng. 2016a;23(2):137–154.
   Google Scholar
• Aladeemy M, Chou C-A, Shan X, et al. (2016). Forecasting daily patient arrivals at emergency department: a comparative study. Proceedings of the 2016 Industrial and Systems Engineering Research Conference, May 2016.
   Google Scholar
• Bal A, Ceylan C, Taçoğlu C. Using value stream mapping and discrete event simulation to improve efficiency of emergency departments. Int J Healthc Manag. 2017;10(3):196–206.
   Web of Science ®Google Scholar
• Sarıyer G, Ataman MG, Kızıloğlu İ. Factors affecting length of stay in the emergency department: A research from an operational viewpoınt. Int J Healthc Manag. 2018. DOI: 10.1080/20479700.2018.1489992.
   PubMed Web of Science ®Google Scholar
• Qureshi RS, Qureshi I, Abbasy M, et al. Unexpected return visits to emergency department: A healthcare quality management challenge. Int J Healthc Manag. 2018. DOI: 10.1080/20479700.2018.1500223.
   PubMed Web of Science ®Google Scholar
• George T, Elgharbawy MA, Fathi AA, et al. Inaccuracy in electronic medical record-reported wait times to initial emergency physician evaluation. Int J Healthc Manag. 2018. DOI: 10.1080/20479700.2017.1418277.
   PubMed Web of Science ®Google Scholar
• Nayebi BA, Mohebbifar R, Azimian J, et al. Estimating nursing staff requirement in an emergency department of a general training hospital: application of workload indicators of staffing need (WISN). Int J Healthc Manag. 2018. DOI: 10.1080/20479700.2017.1390182.
   PubMed Web of Science ®Google Scholar
• Rosychuk RJ, Youngson E, Rowe BH. Presentations to emergency departments for COPD: A time series analysis. Can Respir J. 2016. http://doi.org/10.1155/2016/1382434.
   PubMed Web of Science ®Google Scholar
• Butler MB, Gu H, Kenney T, et al. P017: does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes. CJEM. 2016;18(S1):S83–S84.
   Google Scholar
• Afilal M, Yalaoui F, Dugardin F, et al. Emergency department flow: A new practical patients classification and forecasting daily attendance. IFAC-PapersOnLine. 2016a;49(12):721–726.
   Google Scholar
• Afilal M, Yalaoui F, Dugardin F, et al. Forecasting the emergency department patients flow. J Med Syst. 2016b;40(7):1–18.
   PubMed Web of Science ®Google Scholar
• Rosychuk RJ, Youngson E, Rowe BH. Presentations to Alberta emergency departments for asthma: a time series analysis. Acad Emerg Med. 2015;22(8):942–949.
   PubMed Web of Science ®Google Scholar
• Mai Q, Aboagye-Sarfo P, Sanfilippo FM, et al. Predicting the number of emergency department presentations in Western Australia: A population-based time series analysis. Emerg Med Australas. 2015;27(1):16–21.
   PubMed Web of Science ®Google Scholar
• Aboagye-Sarfo P, Mai Q, Sanfilippo FM, et al. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. J Biomed Inform. 2015;57:62–73.
   PubMed Web of Science ®Google Scholar
• Petrou P. An interrupted time-series analysis to assess impact of introduction of co-payment on emergency room visits in Cyprus. Appl Health Econ Health Policy. 2015;13(5):515–523.
   PubMed Web of Science ®Google Scholar
• Harrou F, Sun Y, Kadri F, et al. Early detection of abnormal patient arrivals at hospital emergency department. In Industrial Engineering and Systems Management (IESM), 2015 International Conference on (pp. 221–227). IEEE, 2015, October.
   Google Scholar
• Zlotnik A, Gallardo-Antolin A, Alfaro MC, et al. Emergency department visit forecasting and dynamic nursing staff allocation using machine learning techniques with readily available open-source software. CIN. 2015;33(8):368–377.
   Google Scholar
• Araz OM, Bentley D, Muelleman RL. Using Google Flu Trends data in forecasting influenza-like–illness related ED visits in Omaha, Nebraska. Am J Emerg Med. 2014;32(9):1016–1023.
   PubMed Web of Science ®Google Scholar
• Kadri F, Harrou F, Chaabane S, et al. Time series modelling and forecasting of emergency department overcrowding. J Med Syst. 2014;38(9):107–127.
   PubMed Web of Science ®Google Scholar
• Bergs J, Heerinckx P, Verelst S. Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. Int Emerg Nurs. 2014;22(2):112–115.
   PubMed Web of Science ®Google Scholar
• Menke NB, Caputo N, Fraser R, et al. A retrospective analysis of the utility of an artificial neural network to predict ED volume. Am J Emerg Med. 2014;32(6):614–617.
   PubMed Web of Science ®Google Scholar
• Xu M, Wong TC, Chin KS. Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decis Support Syst. 2013;54(3):1488–1498.
   Web of Science ®Google Scholar
• Marcilio I, Hajat S, Gouveia N. Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad Emer Med. 2013;20(8):769–777.
   PubMed Web of Science ®Google Scholar
• Cote MJ, Smith MA, Eitel DR, et al. Forecasting emergency department arrivals: a tutorial for emergency department directors. Hosp Top. 2013;91(1):9–19.
   PubMedGoogle Scholar
• Chase VJ, Cohn AE, Peterson TA, et al. Predicting emergency department volume using forecasting methods to create a “surge response” for noncrisis events. Acad Emerg Med. 2012;19(5):569–576.
   PubMed Web of Science ®Google Scholar
• Rosychuk RJ, Klassen TP, Voaklander DC, et al. Seasonality patterns in croup presentations to emergency departments in Alberta, Canada: a time series analysis. Pediatr Emerg Care. 2011;27(4):256–260.
   PubMed Web of Science ®Google Scholar
• Kam HJ, Sung JO, Park RW. Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthc Inform Res. 2010;16(3):158–165.
   PubMedGoogle Scholar
• Wargon M, Casalino E, Guidet B. From model to forecasting: a multicenter study in emergency departments. Acad Emerg Med. 2010;17(9):970–978.
   PubMed Web of Science ®Google Scholar
• Au-Yeung SWM, Harder U, McCoy EJ, et al. Predicting patient arrivals to an accident and emergency department. Emer Med J. 2009;26(4):241–244.
   PubMed Web of Science ®Google Scholar
• Jones SS, Evans RS, Allen TL, et al. A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform. 2009;42(1):123–139.
   PubMed Web of Science ®Google Scholar
• Sun Y, Heng BH, Seow YT, et al. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009;9(1):1.
   PubMedGoogle Scholar
• McCarthy ML, Zeger SL, Ding R, et al. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337–346.
   PubMed Web of Science ®Google Scholar
• Jones SS, Thomas A, Evans RS, et al. Forecasting daily patient volumes in the emergency department. Acad Emerg Med. 2008;15(2):159–170.
   PubMed Web of Science ®Google Scholar
• Park J, Park E, Desai Y. Forecasting patient load of ED at Moses Cone Hospital. In IIE Annual Conference. Proceedings (p. 1). Institute of Industrial and Systems Engineers (IISE), 2008, January.
   Google Scholar
• Champion R, Kinsman LD, Lee GA, et al. Forecasting emergency department presentations. Aust Health Rev. 2007;31(1):83–90.
   PubMed Web of Science ®Google Scholar
• Morzuch BJ, Allen PG. Forecasting hospital emergency department arrivals. In 26th Annual Symposium on Forecasting, Santander, Spain, 2006, June.
   Google Scholar
• Batal H, Tench J, McMillan S, et al. Predicting patient visits to an urgent care clinic using calendar variables. Acad Emerg Med. 2001;8(1):48–53.
   PubMed Web of Science ®Google Scholar
• Wargon M, Guidet B, Hoang TD, et al. A systematic review of models for forecasting the number of emergency department visits. Emerg Med J. 2009;26(6):395–399.
   PubMed Web of Science ®Google Scholar
• Schonwetter RS, Clark LD, Leedy SA, et al. Predicting emergency room visits and hospitalizations among hospice patients with cardiac disease. J Palliat Med. 2008;11(8):1142–1150.
   PubMed Web of Science ®Google Scholar
• Pekel E, Gul M, Celik E. Forecasting daily patient visits in an emergency department by ga-ann hybrid approach. In Proceedings of the 14th International Symposium on Operational Research, SOR 2017, Bled; Slovenia, 27-29 September (pp. 473–478), 2017.
   Google Scholar
• Elias RJ, Montgomery DC, Kulahci M. An overview of short-term statistical forecasting methods. Int J Manage Sci Eng Manage. 2006;1(1):17–36.
   Google Scholar
• Yucesan M. YSA, ARIMA ve ARIMAX Yöntemleriyle Satış Tahmini: Beyaz Eşya Sektöründe bir Uygulama. J Bus Res Turk. 2018;10(1):689–706.
   Google Scholar
• Bozkurt ÖÖ, Biricik G, Tayşi ZC. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PloS ONE. 2017;12(4):e0175915.
   PubMed Web of Science ®Google Scholar
• Guneri AF, Gumus AT. Artificial neural networks for finite capacity scheduling: A comparative study. Int J Indust Eng. 2009;15(4):349–359.
   Google Scholar
• Guneri AF, Gumus AT. The usage of artificial neural networks for finite capacity planning. Int J Indust Eng. 2008;15(1):16–25.
   Google Scholar
• Bagnasco A, Siri A, Aleo G, et al. Applying artificial neural networks to predict communication risks in the emergency department. J Adv Nurs. 2015;71(10):2293–2304.
   PubMed Web of Science ®Google Scholar
• Onat O, Gul M. Application of artificial neural networks to the prediction of out-of-plane response of infill walls subjected to shake table. Smart Struct Syst. 2018;21(4):521–535.
   Web of Science ®Google Scholar
• Yucesan M, Gul M, Celik E. (2018). Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry. Wood Industry/Drvna Industrija.
   Google Scholar
• Yucesan M, Gul M, Celik E. Application of artificial neural networks using Bayesian training rule in sales forecasting for furniture industry. Wood Industry/Drvna Industrija. 2017;68(3):219–228.
   Web of Science ®Google Scholar
• Gul M, Guneri AF. An artificial neural network-based earthquake casualty estimation model for Istanbul city. Nat Hazards. 2016b;84(3):2163–2178.
   Web of Science ®Google Scholar
• Gul M, Guneri AF. Forecasting patient length of stay in an emergency department by artificial neural networks. J Aeronaut Space Technol. 2015;8(2):43–48.
   Google Scholar
• Kröse B, Krose B, van der Smagt P, et al. (1993). An introduction to neural networks.
   Google Scholar
• Gass SI, Harris CM, editors. Encyclopedia of operations research and management science (Centennial edition). Dordrecht: Kluwer; 2000.
   Google Scholar
• Moeeni H, Bonakdari H, Ebtehaj I. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. J Earth Syst Sci. 2017;126(2):1–13.
   Web of Science ®Google Scholar
• Barak S, Sadegh SS. Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. Int J Electr Power Energy Syst. 2016;82:92–104.
   Web of Science ®Google Scholar
• Pannakkong W, Pham VH, Huynh VN. A Hybrid Model of ARIMA, ANNs and k-Means Clustering for Time Series Forecasting. In Integrated Uncertainty in Knowledge Modelling and Decision Making: 5th International Symposium, IUKM 2016, Da Nang, Vietnam, November 30–December 2, 2016, Proceedings 5 (pp. 195–206). Springer International Publishing, 2016.
   Google Scholar
• Shukur OB, Lee MH. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA. Renew Energy. 2015;76:637–647.
   Web of Science ®Google Scholar
• Fard AK, Akbari-Zadeh MR. A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting. J Exp Theor Artif Intell. 2014;26(2):167–182.
   Web of Science ®Google Scholar
• Kumar M, Thenmozhi M. Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. Int J Bank Account Finan. 2014;5(3):284–308.
   Google Scholar
• Wang L, Zou H, Su J, et al. An ARIMA-ANN hybrid model for time series forecasting. Syst Res Behav Sci. 2013;30(3):244–259.
   Web of Science ®Google Scholar
• Tseng FM, Yu HC, Tzeng GH. Combining neural network model with seasonal time series ARIMA model. Technol Forecast Soc Change. 2002;69(1):71–87.
   Web of Science ®Google Scholar
• Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159–175.
   Web of Science ®Google Scholar