Mode of Arrival Aware Models for Forecasting Flow of Patient and\rLength of Stay in Emergency Departments
Loading...
Date
2022
Authors
Mustafa Gökalp Ataman
görkem sariyer
Journal Title
Journal ISSN
Volume Title
Publisher
Open Access Color
GOLD
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Average
Influence
Average
Popularity
Average
Abstract
Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this\rstudy developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning\rof ED operations.\rMaterials and Methods: In this study by categorizing the mode of arrival into two self-arrived in and by ambulance autoregressive\rintegrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an\rambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a\rlarge-scaled urban ED in İzmir Turkey.\rResults: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes non-seasonal models are proper for\rforecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5 432% 13 085% 9 955% and\r10.984% respectively. Thus daily arrivals to the EDs show seasonality patterns.\rConclusion: By emphasizing the impact of mode of arrival in ED context this study can be used to aid the strategic decision making in the\rEDs for capacity planning to enable efficient use of the ED resources.
Description
Keywords
Acil Tıp, emergency department, length of stay, RC86-88.9, patient flow, R, Medicine, forecasting, arima, Medical emergencies. Critical care. Intensive care. First aid
Fields of Science
Citation
1. Xu Q Tsui KL Jiang W Guo H. A hybrid approach for forecasting patient visits in emergency department. Qual Reliab Eng Int. 2016,32:2751-9.2. Sariyer G Taşar CÖ Cepe GE. Use of data mining techniques to classify length of stay of emergency department patients. Bio-Algorithms and MedSystems. 2019. doi: 10.1515/bams-2018-00443. Sariyer G Ataman MG Kızıloğlu İ. Factors affecting length of stay in the emergency department: a research from an operational viewpoint. Int J Healthc Manag. 2018,1-10.4. Wiler JL Griffey RT Olsen T. Review of modeling approaches for emergency department patient flow and crowding research. Acad Emerg Med. 2011,18:1371-9.5. Chase VJ Cohn AE Peterson TA Lavieri MS. Predicting emergency department volume using forecasting methods to create a “surge response” for noncrisis events. Acad Emerg Med. 2012,19:569-76.6. Hertzum M. Forecasting hourly patient visits in the emergency department to counteract crowding. Ergonomics Open J. 2017,10:1-13.7. Jones SS Evans RS Allen TL Thomas A Haug PJ Welch SJ et al. multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform. 2009,42:123-139.8. McCarthy ML Zeger SL Ding R Aronsky D Hoot NR Kelen GD. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008,15:337-46.9. Peck JS Benneyan JC Nightingale DJ Gaehde SA. Predicting emergency department inpatient admissions to improve same‐day patient flow. Acad Emerg Med. 2012,19: E1045-54.10. Sariyer G. Time series modelling for forecasting demand in the emergency department. IJERAD. 2018,10:66-77.11. Butler MB Gu H Kenney T Campbell SG. Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes. CJEM. 2016,18(S1):S83-4.12. Bergs J Heerinckx P Verelst S. Knowing what to expect forecasting monthly emergency department visits: A time-series analysis. Int Emerg Nurs. 2014,22:112-5.13. Mai Q Aboagye‐Sarfo P Sanfilippo FM Preen DB Fatovich DM. Predicting the number of emergency department presentations in W estern A ustralia: A population‐based time series analysis. Emerg Med Australas. 2015,27:16-21.14. Petrou P. An interrupted time-series analysis to assess impact of introduction of co-payment on emergency room visits in Cyprus. Appl Health Econ Hea. 2015,13:515-23.15. Rosychuk RJ Klassen TP Voaklander DC Senthilselvan A Rowe BH. Seasonality patterns in croup presentations to emergency departments in Alberta Canada: a time series analysis. Pediatr Emerg Care. 2011,27:256-60.16. Rosychuk RJ Youngson E Rowe BH. Presentations to emergency departments for COPD: a time series analysis. Can Respir J. 2016,2016:138243417. Batal H Tench J McMillan S Adams J Mehler PS. Predicting patient visits to an urgent care clinic using calendar variables. Acad Emerg Med. 2001,8:48-53.18. Jones SS Thomas A Evans RS Welch SJ Haug PJ Snow GL. Forecasting daily patient volumes in the emergency department. Acad Emerg Med. 2008,15:159-70.19. Sun Y Heng BH Seow YT Seow E. Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009,9:1.20. Kam HJ Sung JO Park RW. Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Health Inform Res. 2010,16:158-65.21. Higginson I Whyatt J Silvester K. Demand and capacity planning in the emergency department: how to do it. Emerg Med J. 2011,28:128-35.22. Boyle J Jessup M Crilly J Green D Lind J Wallis M et al. Predicting emergency department admissions. Emerg Med J. 2012,29:358-65.23. Cote MJ Smith MA Eitel DR Akçali E. Forecasting emergency department arrivals: a tutorial for emergency department directors. Hosp Top. 2013,91:9- 19.24. 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. 2013,54:1488- 98.25. Marcilio I Hajat S Gouveia N. Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Acad Emerg Med. 2013,20:769-77.26. Kadri F Harrou F Chaabane S Tahon C. Time series modelling and forecasting of emergency department overcrowding. J Med Syst. 2014,38:107.27. Luo L Luo L Zhang X He X. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017,17:469.28. Carvalho-Silva M Monteiro MTT de Sá-Soares F Dória-Nóbrega S. Assessment of forecasting models for patients arrival at Emergency Department. Oper Res Health Care. 2018,18:112-8.29. Yucesan M Gul M Celik EA. Multi-method patient arrival forecasting outline for hospital emergency departments. Int J Healthc Manag. 2018,1-13. doi: 10.1080/20479700.2018.153160830. Whitt W Zhang X. Forecasting arrivals and occupancy levels in an emergency department. Oper Res Health Care. 2019,21:1-18.31. Jilani T Housley G Figueredo G Tang PS Hatton J Shaw D. Short and long term predictions of hospital emergency department attendances. Int J Med Inform. 2019,129:167-174.32. Gul M Celik E. An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments. Health Syst. 2018,1-22.33. Combes C Kadri F Chaabane S. Predicting hospital length of stay using regression models: application to emergency department. Available at: https://hal.archives-ouvertes.fr/hal-01081557/document34. Gul M Guneri AF. Forecasting patient length of stay in an emergency department by artificial neural networks. Int J Aeronaut Space Sci. 2015,8:43- 8.35. Kolker A. Process modeling of emergency department patient flow: Effect of patient length of stay on ED diversion. J Med Syst. 2008,32:389-401.36. Derose SF Gabayan GZ Chiu VY Yiu SC Sun BC. Emergency department crowding predicts admission length-of-stay but not mortality in a large health system. Med Care. 2014,52:602.37. Tandberg D Qualls C. Time series forecasts of emergency department patient volume length of stay and acuity. Ann Emerg Med. 1994,23:299-306.38. Menke NB Caputo N Fraser R Haber J Shields C Menke MN. A retrospective analysis of the utility of an artificial neural network to predict ED volume. Am J Emerg Med. 2014,32:614-7.39. Box GE Jenkins GM. Time series analysis control and forecasting. San Francisco CA: Holden Day 1976,3226(3228) 10.40. Aladeemy M Chou CA Shan X Khasawneh M Srihari K Poranki S. Forecasting daily patient arrivals at emergency department: a comparative study. In Proceedings of the 2016 Industrial and Systems Engineering Research Conference 201641. Lewis CD. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann 1982.42. Sariyer G. Sizing capacity levels in emergency medical services dispatch centers: Using the newsvendor approach. Am J Emerg Med. 2018,36:804-15.
WoS Q
Scopus Q
OpenCitations Citation Count
1
Source
Eurasian Journal of Emergency Medicine
Volume
21
Issue
Start Page
34
End Page
44
Collections
PlumX Metrics
Captures
Mendeley Readers : 4
Google Scholar™
