Demand and capacity modelling for acute services using discrete event simulation
- 187 Downloads
Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages), a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5-year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in accident and emergency, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, for example, future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for thousands of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions that are supported by statistically validated data.
Keywordssimulation decision support system hospital capacity hospital resources
- Cote MJ (2000) Understanding patient flow. Decision Line 31(2000), 8–10.Google Scholar
- Department of Health (2007) [WWW document] Patient Pathways: Department of Health - Health care, http://webarchive.nationalarchives.gov.uk/+ www.dh.gov.uk/en/Healthcare/Primarycare/Treatmentcentres/DH_4097263 (accessed September 2015).
- Robinson S (2004) Simulation: The Practice of Model Development and Use. John Wiley & Sons, Chichester, West Sussex, UK; Hoboken, NJ.Google Scholar
- Sterman JD (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. Mc-Graw Hill, Singapore.Google Scholar
- Vasilakis C, El-Darzi E and Chountas P (2008) A decision support system for measuring and modelling the multi-phase nature of patient flow in hospitals. In Intelligent Techniques and Tools for Novel System Architectures (Chountas P, Petrounias I and Kacprzyk J, Eds), pp 201–217, Springer, Berlin.CrossRefGoogle Scholar
- Vissers J and Beech R (2005) Health Operations Management: Patient Flow Logistics in Health Care. Routledge Publishing, New York, NY, USA.Google Scholar
- Zonderland ME and Boucherie RJ (2012) Queuing networks in healthcare systems. In Handbook if Healthcare System Scheduling, International Series in Operations Research and Management Science, (Randolph WH, Ed), pp 201–243, Springer-Verlag, Los Angeles, California, USA.Google Scholar