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Modelling activities at a neurological rehabilitation unit

Author

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  • Griffiths, J.D.
  • Williams, J.E.
  • Wood, R.M.

Abstract

A queuing model of a specialist neurological rehabilitation unit is studied. The application is to the Neurological Rehabilitation Centre at Rookwood Hospital (Cardiff, UK), the national rehabilitation unit for Wales. Due to high demand this 21-bed inpatient facility is nearly always at maximum occupancy, and with a significant bed-cost per day this makes it a prime candidate for mathematical modelling. Central to this study is the concept that treatment intensity has an effect on patient length of stay. The model is constructed in four stages. First, appropriate patient groups are determined based on a number of patient-related attributes. Second, a purpose-built scheduling program is used to deduce typical levels of treatment to patients of each group. These are then used to estimate the mean length of stay for each patient group. Finally, the queuing model is constructed. This consists of a number of disconnected homogeneous server queuing systems; one for each patient group. A Coxian phase-type distribution is fitted to the length of time from admission until discharge readiness and an exponential distribution models the remainder of time until discharge. Some hypothetical scenarios suggested by senior management are then considered and compared on the grounds of a number of performance measures and cost implications.

Suggested Citation

  • Griffiths, J.D. & Williams, J.E. & Wood, R.M., 2013. "Modelling activities at a neurological rehabilitation unit," European Journal of Operational Research, Elsevier, vol. 226(2), pages 301-312.
    • Handle: RePEc:eee:ejores:v:226:y:2013:i:2:p:301-312
    DOI: 10.1016/j.ejor.2012.10.037
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    References listed on IDEAS

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    1. S McClean & P Millard, 2007. "Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 255-261, February.
    2. Paul Harper & Evandro Leite Jr., 2008. "TreeWorks: Advances in Scalable Decision Trees," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 3(4), pages 53-68, October.
    3. G. J. Taylor & S. I. McClean & P. H. Millard, 1998. "Using a continuous‐time Markov model with Poisson arrivals to describe the movements of geriatric patients," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 14(2), pages 165-174, June.
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    Cited by:

    1. Kozlowski, Dawid & Worthington, Dave, 2015. "Use of queue modelling in the analysis of elective patient treatment governed by a maximum waiting time policy," European Journal of Operational Research, Elsevier, vol. 244(1), pages 331-338.
    2. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    3. Gogi, Anastasia & Tako, Antuela A. & Robinson, Stewart, 2016. "An experimental investigation into the role of simulation models in generating insights," European Journal of Operational Research, Elsevier, vol. 249(3), pages 931-944.
    4. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    5. Jennifer Gillespie & Sally McClean & Lalit Garg & Maria Barton & Bryan Scotney & Ken Fullerton, 2016. "A multi-phase DES modelling framework for patient-centred care," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(10), pages 1239-1249, October.
    6. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    7. Sally McClean, 2021. "Using Markov Models to Characterize and Predict Process Target Compliance," Mathematics, MDPI, vol. 9(11), pages 1-12, May.
    8. Yang, Xiaopeng & Zheng, Danheng & Sieminowski, Tammy & Paradi, Joseph C., 2015. "A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process," European Journal of Operational Research, Elsevier, vol. 240(2), pages 582-591.

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