Dynamic resource allocation for efficient patient scheduling: A data-driven approach

Article
  • 126 Downloads

Abstract

Efficient staff rostering and patient scheduling to meet outpatient demand is a very complex and dynamic task. Due to fluctuations in demand and specialist availability, specialist allocation must be very flexible and non-myopic. Medical specialists are typically restricted in sub-specialization, serve several patient groups and are the key resource in a chain of patient visits to the clinic and operating room (OR). To overcome a myopic view of once-off appointment scheduling, we address the patient flow through a chain of patient appointments when allocating key resources to different patient groups. We present a new, data-driven algorithmic approach to automatic allocation of specialists to roster activities and patient groups. By their very nature, simplified mathematical models cannot capture the complexity that is characteristic to the system being modeled. In our approach, the allocation of specialists to their day-to-day activities is flexible and responsive to past and present key resource availability, as well as to past resource allocation. Variability in roster activities is actively minimized, in order to enhance the supply chain flow. With discrete-event simulation of the application case using empirical data, we illustrate how our approach improves patient Service Level (SL, percentage of patients served on-time) as well as Wait Time (days), without change in resource capacity.

Keywords

Patient scheduling dynamic rostering patient care path discrete-event simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Agnetis, A., Coppi, A., Corsini, M., Dellino, G., Meloni, C. & Pranzo, M. (2014). A decomposition approach for the combined master surgical schedule and surgical case assignment problems. Health Care Management Science, 17 (1): 49–59.CrossRefGoogle Scholar
  2. [2]
    Beaulieu, H., Ferland, J.A., Gendron, B. & Michelon, P. (2000). A mathematical programming approach for scheduling physicians in the emergency room. Health Care Management Science, 3 (3): 193–200.CrossRefGoogle Scholar
  3. [3]
    Bratley, P., Fox, B.L. & Schrage, L.E. (1987). A Guide To Simulation (2nd ed.). New York: Springer Science & Business Media.CrossRefMATHGoogle Scholar
  4. [4]
    Brucker, P., Qu, R. & Burke, E. (2011). Personnel scheduling: models and complexity. European Journal of Operational Research, 210 (3): 467–473.MathSciNetCrossRefMATHGoogle Scholar
  5. [5]
    Brunner, J.O. Bard, J.F. & Kolisch, R. (2009). Flexible shift scheduling of physicians. Health Care Management Science, 12 (3): 285–305.CrossRefGoogle Scholar
  6. [6]
    Cardoen, B. Demeulemeester, E. & Beliën, J. (2010). Operating room planning and scheduling: a literature review. European Journal of Operational Research, 201 (3): 921–932.CrossRefMATHGoogle Scholar
  7. [7]
    Cayirli, T. & Veral, E. (2003). Outpatient scheduling in health care: a review of literature. Production and Operations Management, 12 (4): 519–549.CrossRefGoogle Scholar
  8. [8]
    Day, R., Garfinkel, R. & Thompson, S. (2012). Integrated block sharing: a win-win strategy for hospitals and surgeons. Manufacturing & Service Operations Management, 14 (4): 567–583.CrossRefGoogle Scholar
  9. [9]
    Fugener, A., Brunner, J.O. & Podtschaske, A. (2015). Duty and workstation rostering considering preferences and fairness: a case study at a department of anaesthesiology. International Journal of Production Research, 53 (24): 7465–7487.CrossRefGoogle Scholar
  10. [10]
    Gunal, M.M. (2012). A guide for building hospital simulation models. Health Systems, 1 (1): 17–25.CrossRefGoogle Scholar
  11. [11]
    Guo, M., Wagner, M. & West, C. (2004). Outpatient clinic scheduling - a simulation approach. In: Ingalls, R.G., Rossetti, M.D., Smith, J. & Peters, B.A. (eds.), Winter Simulation Conference, 1981-1987, Washington DC, December 2004.Google Scholar
  12. [12]
    Kelton, D.W., Sadowski, R.P. & Zupick, N.B. (2015). Simulation with Arena (6th ed.). McGraw-Hill Education, New York.Google Scholar
  13. [13]
    Ma, G. & Demeulemeester, E. (2013). A multilevel integrative approach to hospital case mix and capacity planning. Computers and Operations Research, 40 (9): 2198–2207.CrossRefMATHGoogle Scholar
  14. [14]
    Mǎruşter, L., Weijters, T., De Vries, G., Van Den Bosch, A. & Daelemans, W. (2002). Logistic-based patient grouping for multi-disciplinary treatment. Artificial Intelligence in Medicine, 26 (1–2): 87–107.Google Scholar
  15. [15]
    Molina-Pariente, J.M., Fernandez-Viagas, V. & Framinan, J.M. (2015). Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations. Computers & Industrial Engineering, 82: 8–20.CrossRefGoogle Scholar
  16. [16]
    Robinson, S. (2004). Simulation: The Practice of Model Development and Use. John Wiley & Sons, Ltd., Hoboken, NJ.Google Scholar
  17. [17]
    Schmenner, R.W. & Swink, M.L. (1998). On theory in operations management. Journal of Operations Management, 17 (1): 97–113.CrossRefGoogle Scholar
  18. [18]
    Vermeulen, I.B., Bohte, S.M., Elkhuizen, S.G., Lameris, H., Bakker, P.J.M. & Poutré, H.L. (2009). Adaptive resource allocation for efficient patient scheduling. Artificial Intelligence in Medicine, 46 (1): 67–80.CrossRefGoogle Scholar
  19. [19]
    Viccellio, P. & Litvak, E. (2015) Seven-day week approach can transform hospitals: Can we reduce hospital overcrowding without hurting the economy? The Irish Times, 08 Apr 2015, Dublin.Google Scholar
  20. [20]
    Vissers, J. & Beech, R. (2005). Health Operations Management. Patient Flow Logistics in Health Care. Routledge, London and New York.Google Scholar
  21. [21]
    White, D.L., Froehle, C.M. & Klassen, K.J. (2011). The effect of integrated scheduling and capacity policies on clinical efficiency. Production and Operations Management, 20 (3): 442–455.CrossRefGoogle Scholar
  22. [22]
    Yin, R.K., (2013). Case Study Research: Design and Methods (5th ed.). Sage Publications, Inc.Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementCity University of Hong KongHong KongChina

Personalised recommendations