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Journal of Medical Systems
Published in: Journal of Medical Systems 9/2014

01-09-2014 | Systems-Level Quality Improvement

Time Series Modelling and Forecasting of Emergency Department Overcrowding

Authors: Farid Kadri, Fouzi Harrou, Sondès Chaabane, Christian Tahon

Published in: Journal of Medical Systems | Issue 9/2014

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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.
Literature
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
13.
Kellermann, A. L., Crisis in the emergency department. N. Engl. J. Med. 355(13):1300–1303, 2006.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
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.
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.CrossRef
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.CrossRef
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.MATHCrossRef
27.
Hisnanick, J. J., Forecasting the demand for inpatient services for specific chronic conditions. J. Med. Syst. 18(1):9–21, 1994.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
35.
Jones, S. A., Joy, M. P., and Pearson, J., Forecasting demand of emergency care. Health Care Manag. Sci. 5(4):297–305, 2002.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
41.
Barişçi, N., The adaptive ARMA analysis of EMG signals. J. Med. Syst. 32(1):43–50, 2008.CrossRef
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.CrossRef
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.CrossRef
44.
Nouira, K., and Trabelsi, A., Intelligent monitoring system for intensive care units. J. Med. Syst. 36(4):2309–2318, 2012.CrossRef
45.
Lim, C., McAleer, M., and Min, J. C. H., ARMAX modelling of international tourism demand. Math. Comput. Simul. 79(9):2879–2888, 2009.MATHMathSciNetCrossRef
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.
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.CrossRef
52.
Windhorst, U., and Johansson, H., Modern techniques in neuroscience research, 1st edition. Springer, New York, 1999.CrossRef
53.
Mayer, D. G., and Butler, D. G., Statistical validation. Ecol. Model. 68(1–2):21–32, 1993.CrossRef
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.
55.
Banerjee, A., Co-integration, error correction, and the econometric analysis of non-stationary data. Oxford University Press, 1993.
Metadata
Title
Time Series Modelling and Forecasting of Emergency Department Overcrowding
Authors
Farid Kadri
Fouzi Harrou
Sondès Chaabane
Christian Tahon
Publication date
01-09-2014
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 9/2014
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-014-0107-0

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