A decision support system for demand and capacity modelling of an accident and emergency department

References

• Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F. M., Preen, D. B., Stewart, L. M., & Fatovich, D. M. (2015). A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics, 57, 62–73.
   PubMed Web of Science ®Google Scholar
• Ahmad, N., Ghani, N. A., Kamil, A. A., Tahar, R. M., & Teo, A. H. (2012). Evaluating emergency department resource capacity using simulation. Modern Applied Science, 6, 9–19.
   Google Scholar
• Al-Refaie, A., Fouad, R. H., Li, M., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59–72.
   Web of Science ®Google Scholar
• Banks, J., Carson II, J. S., Nelson, B. L., & Nicol, D. M. (2005). Discrete-event system simulation. New Jersey, NJ: Pearson.
   Google Scholar
• Batal, H., Tench, J., McMillan, S., Adams, J., & Mehler, P. S. (2001). Predicting patient visits to an urgent care clinic using calendar variables. Academic Emergency Medicine, 8, 48–53.
   PubMed Web of Science ®Google Scholar
• Bergs, J, Heerinckx, P, & Verelst, S. (2013). Knowing what to expect, forecasting monthly emergency department visits: a time-series analysis. International Emergency Nursing, 22, 112–115. doi: http://dx.doi.org/10.1016/j.ienj.2013.08.001
   Google Scholar
• Blunt, I. (2014). Focus on: A&E attendances: Why are patients waiting longer? Retrieved June 21, 2017, from http://www.qualitywatch.org.uk/sites/files/qualitywatch/field/field_document/QW%20Focus%20on%20A%26E%20attendances%20%28for%20web%29.pdf
   Google Scholar
• Boutsioli, Z. (2010). Forecasting the stochastic demand for inpatient care: The case of Greek national health system. Health Services Management Research, 23, 116–120.
   Google Scholar
• Boutsioli, Z. (2013). Estimation of unpredictable hospital demand variations in two Piraeus public hospitals, Greece. Journal of Hospital Administration, 2, 126–137.
   Google Scholar
• Champion, R., Kinsman, L. D., Lee, G. A., Masman, K. A., May, E. A., Mills, T. M., … Williams, R. J. (2007). Forecasting emergency department presentations. Australian Health Review, 31, 83–90.
   PubMed Web of Science ®Google Scholar
• Cochran, J. K., & Bharti, A. (2006). Stochastic bed balancing of an obstetrics hospital. Health Care Management Science, 9, 31–45.
   PubMedGoogle Scholar
• Connelly, L. G., & Bair, A. E. (2004). Discrete event simulation of emergency department activity: A platform for system-level operations. Academic Emergency Medicine, 11, 1177–1185.
   PubMed Web of Science ®Google Scholar
• Cracknell, R. (2010). The ageing population. Value for money in public services: key issues for the 2010 Parliament. (pp. 44). England: House of Commons Library Research.
   Google Scholar
• Davis, S., Stevenson, M., Tappenden, P., & Wailoo, A. J. (2014). NICE DSU technical support document 15: Cost-effectiveness modelling using patient-level simulation. Retrieved July 4, 2017, from http://www.nicedsu.org.uk
   Google Scholar
• DeLurgio, S. A. (1998). Forecasting principles and applications. New York: McGraw – Hill.
   Google Scholar
• Demir, E., Gunal, M., & Southern, D. (2017). Demand and capacity modelling for acute services using discrete event simulation. Health Systems, 6, 33–40.
   Web of Science ®Google Scholar
• Department of Health. (2013). Reference costs 2012–13. Retrieved May 26, 2017, from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/261154/nhs_reference_costs_2012-13_acc.pdf
   Google Scholar
• Department of Health. (2014). Reference costs 2013–14. Retrieved May 26, 2017, from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/380322/01_Final_2013-14_Reference_Costs_publication_v2.pdf
   Google Scholar
• Duguay, C., & Chetouane, F. (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation, 83, 311–320.
   Google Scholar
• Gneiting, T. (2011). Making and evaluating point forecasts. Journal of the American Statistical Association, 494, 746–762.
   Google Scholar
• Gul, M., Celik, E., Guneri, A. F., & Taskin Gumus, A. (2012). Simulation with integrated multi criteria decision making: An application of scenario selection for a hospital emergency department. Istanbul Commerce University Journal of Science, 22, 1–18. (in Turkish).
   Google Scholar
• Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system. International Journal of Industrial Engineering, 19(5), 221–231.
   Web of Science ®Google Scholar
• Gul, M., & Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers and Industrial Engineering, 83, 327–344.
   Web of Science ®Google Scholar
• Gunal, M., & Pidd, M. (2006, December). Understanding accident and emergency department performance using simulation. In L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, & R. M. Fujimoto, Eds., 39th Winter simulation conference (446–452). Monterey: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17–25. doi: doi.10.1057/hs.2012.8
   Google Scholar
• Hamm, C. (2010). The coalition government’s plans for the NHS in England. British Medical Journal, 341, 3790.
   Google Scholar
• Hong, N. C., & Ghani, N. A. (2006, June). A model for predicting average ambulance service travel times in Penang Island. Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. Penang Island.
   Google Scholar
• Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting principles and practice. Australia: Otexts.
   Google Scholar
• Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3).
   Web of Science ®Google Scholar
• Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679–688.
   Web of Science ®Google Scholar
• Hyndman, R. J., O’Hara-Wild, M., Bergmeir, C., Razbash, S., & Wang, E. (2016). Package ‘forecast’. Retrieved May 26, 2017, from https://cran.r-project.org/web/packages/forecast/forecast.pdf
   Google Scholar
• 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, 159–170.
   PubMed Web of Science ®Google Scholar
• Kam, H. J., Sung, J. O., & Park, R. W. (2010). Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthcare Informatics Research, 16, 158–165.
   PubMedGoogle Scholar
• Komashie, A., & Mousavi, A. (2005, December). Modelling emergency departments using discrete event simulation techniques. In M. E. Kuhl, N. M. Steiger, F. B. Armstrong, & J. A. Joines, Eds., 38th Winter simulation conference (2681–2685). Orlando: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Law, A. M., & Kelton, W. D. (2000). Simulation modeling and analysis. New York: McGraw – Hill.
   Google Scholar
• Levin, S. R., Dittus, R., Aronsky, D., Weinger, M. B., Han, J., Boord, J., & France, D. (2008). Optimizing cardiology capacity to reduce emergency department boarding: A system engineering approach. American Heart Journal, 156, 1202–1209.
   Web of Science ®Google Scholar
• Makridakis, S., Wheelwright, S. C., & Hydnman, R. J. (1998). Forecasting methods and applications. New York, NY: John Wiley & Sons.
   Google Scholar
• Marcilio, I., Hajat, S., & Gouveia, N. (2013). Forecasting daily emergency department visits using calendar variables and ambient temperature readings. Academic Emergency Medicine, 20, 769–777.
   PubMed Web of Science ®Google Scholar
• Mathwave Technologies. (n.d.). How to select the best fitting distribution using the goodness of fit tests. Retrieved May 26, 2017, from http://www.mathwave.com/articles/distribution-fitting-goodness-of-fit.html
   Google Scholar
• Medeiros, D. J., Swenson, E., & DeFlitch, C. (2008, December). Improving patient flow in a hospital emergency department. In J. S. Mason, R. R. Hill, L. Monch, O. Rose, T. Jefferson, & J. W. Fowler, Eds., 41st Winter simulation conference (1526–1531). Miami: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Meng, L. Y., & Spedding, T. (2008). Modelling patient arrivals when simulating an accident and emergency unit. In J. S. Mason, R. R. Hill, L. Monch, O. Rose, T. Jefferson, & J. W. Fowler, Eds., 41st Winter Simulation Conference (1509-1515). Miami, USA. New York: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Monitor. (2015). A&E delays: Why did patients wait longer? Our econometric analysis. Retrieved November 17, 2018, from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/457824/AE_delay_econometric_model_final_document.pdf
   Google Scholar
• National Health Services England. (2014). A&E attendances and emergency admissions and bed availability and occupancy. Retrieved June 2, 2014, from https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/
   Google Scholar
• National Health Services England. (2017a). A&E attendances and emergency admissions and bed availability and occupancy. Retrieved July 9, 2017, from https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/
   Google Scholar
• National Health Services England. (2017b). Bed availability and occupancy. Retrieved May 26, 2017, from https://www.england.nhs.uk/statistics/statistical-work-areas/bed-availability-and-occupancy/
   Google Scholar
• NHS Digital. (2013). NHS staff earnings estimates to June 2013 – Provisional, experimental statistics. Retrieved May 26, 2017, from https://content.digital.nhs.uk/catalogue/PUB116121
   Google Scholar
• NHS Digital. (2014). NHS staff earnings estimates to June 2014 – Provisional statistics. Retrieved May 26, 2017, from https://content.digital.nhs.uk/catalogue/PUB14955
   Google Scholar
• NHS Digital. (n.d.). Healthcare Research Groups (HRG4). Retrieved May 26, 2017, from http://content.digital.nhs.uk/hrg4
   Google Scholar
• NHS England. (2017). NHS national tariff payment system 2016/17. Retrieved May 26, 2017, from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/509697/2016-17_National_Tariff_Payment_System.pdf
   Google Scholar
• Oh, C., Novotny, A. M., Carter, P. L., Ready, R. K., Campbell, D. D., & Leckie, M. C. (2016). Use of a simulation – Based decision support tool to improve emergency department throughput. Operations Research for Health Care, 9, 29–39.
   Web of Science ®Google Scholar
• Ozdagoglu, A., Yalcinkaya, O., & Ozdagoglu, G. (2009). A simulation based analysis of a research and application hospital emergency patient data in Aegean region. Istanbul Commerce University Journal of Science, 16, 61–73. (in Turkish).
   Google Scholar
• Pidd, M. (2004). Computer simulation in management science. Chichester: John Wiley and Sons.
   Google Scholar
• Ripley, B., Venables, B., Bates, D. M., Hornik, K., Gebhardt, A., & Firth, D. (2016). Package ‘MASS’. Retrieved May 26, 2017, from https://cran.r-project.org/web/packages/MASS/MASS.pdf
   Google Scholar
• Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modelling to improve patient flow at an outpatient orthopaedic clinic. Health Care Management Science, 14, 135–145.
   PubMed Web of Science ®Google Scholar
• Royal College of Physicians. (n.d.). Work and wellbeing in the NHS: Why staff health matters to patient care. Retrieved October 23, 2017, from https://www.rcpsych.ac.uk/pdf/RCP-%20WorkWellbeingNHS.pdf
   Google Scholar
• Ruohonen, T., & Teittinen, J. (2006, December). Simulation model for improving the operational of the emergency department of special health care. In L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, & R. M. Fujimoto, Eds., 39th Winter simulation conference (453–458). Monterey: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Sun, Y., Heng, B. H., Seow, Y. T., & Seow, E. (2009). Forecasting daily attendances at an emergency department to aid resource planning. BMC Emergency Medicine, 9.
   Google Scholar
• Tofallis, C. (2015). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, 66, 1352–1362.
   Web of Science ®Google Scholar
• VanBerkel, P. T., & Blake, J. T. (2007). A comprehensive simulation for wait time reduction and capacity planning applied in general surgery. Health Care Management Science, 10, 373–385.
   PubMedGoogle Scholar
• Vasilakis, C., & El-Darzi, E. (2001). A simulation study of the winter bed crisis. Health Care Management Science, 4, 31–36.
   PubMedGoogle Scholar
• Virtue, A., Kelly, J., & Chaussalet, T. (2011, December). Using simplified discrete-event simulation models for health care applications. In S. Jain, R. R. Creasey, J. Himmelspach, & M. Fu, Eds., 44th Winter simulation conference (1154–1165). Phoenix: Institute of Electrical and Electronics Engineers.
   Google Scholar
• Wang, J., Li, J., Tussey, K., & Ross, K. (2012). Reducing length of stay in emergency department: A simulation study at a community hospital. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transaction, 42, 1314–1322.
   Web of Science ®Google Scholar
• Zhu, Z., Hen, B. H., & Teow, K. L. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25, 134–144.
   PubMedGoogle Scholar