Dynamic resource allocation for efficient patient scheduling: A data-driven approach
- 128 Downloads
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.
KeywordsPatient scheduling dynamic rostering patient care path discrete-event simulation
Unable to display preview. Download preview PDF.
- 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
- Kelton, D.W., Sadowski, R.P. & Zupick, N.B. (2015). Simulation with Arena (6th ed.). McGraw-Hill Education, New York.Google Scholar
- 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
- Robinson, S. (2004). Simulation: The Practice of Model Development and Use. John Wiley & Sons, Ltd., Hoboken, NJ.Google Scholar
- 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
- Vissers, J. & Beech, R. (2005). Health Operations Management. Patient Flow Logistics in Health Care. Routledge, London and New York.Google Scholar
- Yin, R.K., (2013). Case Study Research: Design and Methods (5th ed.). Sage Publications, Inc.Google Scholar