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Time Series Modelling and Forecasting of Emergency Department Overcrowding

  • Systems-Level Quality Improvement
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Abstract

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

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References

  1. Gordon, J. A., Billings, J., Asplin, B. R., and Rhodes, K. V., Safety net research in emergency medicine: proceedings of the Academic Emergency Medicine Consensus Conference on “The Unraveling Safety Net”. Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med. 8(11):1024–1029, 2001.

    Article  Google Scholar 

  2. Boyle, A., Beniuk, K., Higginson, I., and Atkinson, P., Emergency department crowding: Time for interventions and policy evaluations. Emerg. Med. Int. 2012:2012.

  3. Cooke, M. W., Wilson, S., Halsall, J., and Roalfe, A., Total time in English accident and emergency departments is related to bed occupancy. Emerg. Med. J. EMJ 21(5):575–576, 2004.

    Article  Google Scholar 

  4. Sun, B. C., Mohanty, S. A., Weiss, R., Tadeo, R., Hasbrouck, M., Koenig, W., Meyer, C., and Asch, S., Effects of hospital closures and hospital characteristics on emergency department ambulance diversion, Los Angeles County, 1998 to 2004. Ann. Emerg. Med. 47(4):309–316, 2006.

    Article  Google Scholar 

  5. Howard, M. S., Davis, B. A., Anderson, C., Cherry, D., Koller, P., and Shelton, D., Patients’ perspective on choosing the emergency department for nonurgent medical care: a qualitative study exploring one reason for overcrowding. J. Emerg. Nurs. JEN Off. Publ. Emerg. Dep. Nurses Assoc. 31(5):429–435, 2005.

    Article  Google Scholar 

  6. Kadri, F., Chaabane, S., Harrou, F., et Tahon, C., Modélisation et prévision des flux quotidiens des patients aux urgences hospitalières en utilisant l’analyse de séries chronologiques. In: 7ème conférence de Gestion et Ingénierie des Systèmes Hospitaliers (GISEH), Liège, Belgique, 2014, pp. 8.

  7. Schull, M. J., Mamdani, M. M., and Fang, J., Influenza and emergency department utilization by elders. Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med. 12(4):338–344, 2005.

    Article  Google Scholar 

  8. Espinosa, G., Miró, O., Sánchez, M., Coll-Vinent, B., and Millá, J., Effects of external and internal factors on emergency department overcrowding. Ann. Emerg. Med. 39(6):693–695, 2002.

    Article  Google Scholar 

  9. Li, G., Lau, J. T., McCarthy, M. L., Schull, M. J., Vermeulen, M., and Kelen, G. D., Emergency department utilization in the United States and Ontario, Canada. Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med. 14(6):582–584, 2007.

    Article  Google Scholar 

  10. Bair, A. E., Song, W. T., Chen, Y.-C., and Morris, B. A., The impact of inpatient boarding on ED efficiency: a discrete-event simulation study. J. Med. Syst. 34(5):919–929, 2010.

    Article  Google Scholar 

  11. Kolker, A., Process modeling of emergency department patient flow: effect of patient length of stay on ED diversion. J. Med. Syst. 32(5):389–401, 2008.

    Article  Google Scholar 

  12. IMNA, Institute of Medicine Committee on the Future of Emergency Care in the U.S. Health System. Hospital- based emergency care: at the breaking point. The National Academies Press, Washington, DC, 2006.

    Google Scholar 

  13. Kellermann, A. L., Crisis in the emergency department. N. Engl. J. Med. 355(13):1300–1303, 2006.

    Article  Google Scholar 

  14. Baubeau, D., Deville, A., et M. Joubert, Les passages aux urgences de 1990 à 1998: une demande croissante de soins non programmés. 72, 2000.

  15. Cours des comptes, Les urgences médicales, constats et évolution récente, rapport public annuel—08 février 2007. 2007.

  16. Roh, C.-Y., Lee, K.-H., and Fottler, M. D., Determinants of hospital choice of rural hospital patients: the impact of networks, service scopes, and market competition. J. Med. Syst. 32(4):343–353, 2008.

    Article  Google Scholar 

  17. Kadri, F., Pach, C., Chaabane, S., Berger, T., Trentesaux, D., Tahon, C., and Sallez, Y., Modelling and management of the strain situations in hospital systems using un ORCA approach, IEEE IESM, 28–30 October », RABAT - MOROCCO, 2013, p. 10.

  18. Kadri, F., Chaabane, S., and Tahon, C., A simulation-based decision support system to prevent and predict strain situations in emergency department systems. Simul. Model. Pract. Theory 42:32–52, 2014.

    Article  Google Scholar 

  19. El-Masri, S., and Saddik, B., An emergency system to improve ambulance dispatching, ambulance diversion and clinical handover communication—a proposed model. J. Med. Syst. 36(6):3917–3923, 2012.

    Article  Google Scholar 

  20. Sprivulis, P. C., Da Silva, J.-A., Jacobs, I. G., Frazer, A. R. L., and Jelinek, G. A., The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med. J. Aust. 184(5):208–212, 2006.

    Google Scholar 

  21. Alexandrescu, R., Bottle, A., Jarman, B., and Aylin, P., Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models. J. Med. Syst. 38(5):1–7, 2014.

    Article  Google Scholar 

  22. Rondeau, K. V., and Francescutti, L. H., Emergency department overcrowding: the impact of resource scarcity on physician job satisfaction. J. Healthc. Manag. Am. Coll. Healthc. Exec. 50(5):327–340, 2005. discussion 341–342.

    Google Scholar 

  23. Lin, B. Y.-J., Hsu, C.-P. C., Chao, M.-C., Luh, S.-P., Hung, S.-W., and Breen, G.-M., Physician and nurse job climates in hospital-based emergency departments in Taiwan: management and implications. J. Med. Syst. 32(4):269–281, 2008.

    Article  Google Scholar 

  24. Pope, C., van Royen, P., and Baker, R., Qualitative methods in research on healthcare quality. Qual. Saf. Health Care 11(2):148–152, 2002.

    Article  Google Scholar 

  25. Ozcan, Y. A., Quantitative methods in health care management: techniques and applications. John Wiley & Sons, 2005.

  26. Shumway, R. H., and Stoffer, D., Time series analysis and its applications with R examples. Springer Texts in Statistics, New York, 2011.

    Book  MATH  Google Scholar 

  27. Hisnanick, J. J., Forecasting the demand for inpatient services for specific chronic conditions. J. Med. Syst. 18(1):9–21, 1994.

    Article  Google Scholar 

  28. Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., and Snow, G. L., Forecasting daily patient volumes in the emergency department. Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med. 15(2):159–170, 2008.

    Article  Google Scholar 

  29. McGee, V. E., Jenkins, E., and Rawnsley, H. M., Statistical forecasting in a hospital clinical laboratory. J. Med. Syst. 3(3–4):161–174, 1979.

    Article  Google Scholar 

  30. Xu, M., Wong, T. C., and Chin, K. S., Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decis. Support Syst. 2013.

  31. Lin, W. T., Modeling and forecasting hospital patient movements: univariate and multiple time series approaches. Int. J. Forecast. 5(2):195–208, 1989.

    Article  Google Scholar 

  32. Tandberg, D., and Qualls, C., Time series forecasts of emergency department patient volume, length of stay, and acuity. Ann. Emerg. Med. 23(2):299–306, 1994.

    Article  Google Scholar 

  33. Rotstein, Z., Wilf-Miron, R., Lavi, B., Shahar, A., Gabbay, U., and Noy, S., The dynamics of patient visits to a public hospital ED: a statistical model. Am. J. Emerg. Med. 15(6):596–599, 1997.

    Article  Google Scholar 

  34. Abdel-Aal, R. E., and Mangoud, A. M., Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis. Comput. Methods Programs Biomed. 56(3):235–247, 1998.

    Article  Google Scholar 

  35. Jones, S. A., Joy, M. P., and Pearson, J., Forecasting demand of emergency care. Health Care Manag. Sci. 5(4):297–305, 2002.

    Article  Google Scholar 

  36. Martín Rodríguez, G., and Cáceres Hernández, J. J., A method for ascertaining the seasonal pattern of hospital emergency department visits. Rev. Esp. Salud Pública 79(1):5–15, 2005.

    Article  Google Scholar 

  37. Alkan, A., and Kiymik, M. K., Comparison of AR and Welch methods in epileptic seizure detection. J. Med. Syst. 30(6):413–419, 2006.

    Article  Google Scholar 

  38. Earnest, A., Chen, M. I., Ng, D., and Sin, L. Y., Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. BMC Health Serv. Res. 5(1):36, 2005.

    Article  Google Scholar 

  39. Gooijer, J. G. D., and Hyndman, R. J., Twenty five years of time series forecasting. Int. J. Forecast. p. 2006.

  40. Champion, R., Kinsman, L. D., Lee, G. A., Masman, K. A., May, E. A., Mills, T. M., Taylor, M. D., Thomas, P. R., and Williams, R. J., Forecasting emergency department presentations. Aust. Health Rev. Publ. Aust. Hosp. Assoc. 31(1):83–90, 2007.

    Google Scholar 

  41. Barişçi, N., The adaptive ARMA analysis of EMG signals. J. Med. Syst. 32(1):43–50, 2008.

    Article  Google Scholar 

  42. Jones, S. S., Evans, R. S., Allen, T. L., Thomas, A., Haug, P. J., Welch, S. J., and Snow, G. L., A multivariate time series approach to modeling and forecasting demand in the emergency department. J. Biomed. Inform. 42(1):123–139, 2009.

    Article  Google Scholar 

  43. Sun, Y., Heng, B., Seow, Y., and Seow, E., Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg. Med. 9(1):1, 2009.

    Article  Google Scholar 

  44. Nouira, K., and Trabelsi, A., Intelligent monitoring system for intensive care units. J. Med. Syst. 36(4):2309–2318, 2012.

    Article  Google Scholar 

  45. Lim, C., McAleer, M., and Min, J. C. H., ARMAX modelling of international tourism demand. Math. Comput. Simul. 79(9):2879–2888, 2009.

    Article  MATH  MathSciNet  Google Scholar 

  46. Lütkepohl, H., Forecasting cointegrated VARMA processes. In: Clements, M. P., and Hendry, D. F., (Ed.), A Companion to Economic Forecasting. Blackwell Publishing Ltd, 2007, p. 179–205.

  47. Reinsel, G. C., Elements of multivariate time series analysis. Springer, 2003.

  48. Box, G. E. P., and Jenkins, G. M., Time series analysis: forecasting and control. Holden-Day, 1976.

  49. Makridakis, S. G., Wheelwright, S. C., and Hyndman, R. J., Forecasting: methods and applications, 3rd Edition. 1998.

  50. Draper, N., and Smith, H., Applied regression analysis. Wiley, New York, 1966.

    Google Scholar 

  51. Balaguer, E., Palomares, A., Soria, E., and Martín-Guerrero, J. D., Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks. Expert Syst. Appl. 34(1):665–672, 2008.

    Article  Google Scholar 

  52. Windhorst, U., and Johansson, H., Modern techniques in neuroscience research, 1st edition. Springer, New York, 1999.

    Book  Google Scholar 

  53. Mayer, D. G., and Butler, D. G., Statistical validation. Ecol. Model. 68(1–2):21–32, 1993.

    Article  Google Scholar 

  54. Berthier, F., Andreü, M., Bourjac, M., Baron, D., Branger, B., and Turbide, A., Analysis of cost and of non-medical care load of patients seen in an accident and emergency department—the importance of clinical classification of emergency patients. Eur. J. Emerg. Med. Off. J. Eur. Soc. Emerg. Med. 5(2):235–240, 1998.

    Google Scholar 

  55. Banerjee, A., Co-integration, error correction, and the econometric analysis of non-stationary data. Oxford University Press, 1993.

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Authors and Affiliations

  1. Univ. Lille Nord de France, 59000, Lille, France

    Farid Kadri, Sondès Chaabane & Christian Tahon

  2. UVHC, TEMPO Lab., “Production, Services, Information” Team, 59313, Valenciennes, France

    Farid Kadri, Sondès Chaabane & Christian Tahon

  3. Chemical Engineering Program, Texas A&M University at Qatar, Doha, Qatar

    Fouzi Harrou

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  1. Farid Kadri
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  2. Fouzi Harrou
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Correspondence to Farid Kadri.

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Kadri, F., Harrou, F., Chaabane, S. et al. Time Series Modelling and Forecasting of Emergency Department Overcrowding. J Med Syst 38, 107 (2014). https://doi.org/10.1007/s10916-014-0107-0

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