The next posts will focus on the effective use of simulation to improve Emergency Department (ED) performance and use an optimization procedure with respect to controllable variables and constants. Clark (2016) describes the approach and a case study illustrating its application. Long waiting times and length of stay at hospital emergency departments is an important public health problem. This post describes the use of simulation to improve ED performance. The approach described was applied at the Saint Camille hospital in Paris. The hospital has about 300 beds and its ED operates 24 hours per day and serves more than 60,000 patients per year.
Long wait times is an increasing problem in the United States, and visits to Hospital EDs has been increasing. From 1999 to 2009, it had increased by 32% to 136 million annual visits (Hing, Bhuiya 2012). That is, it increased at annual rate of 2.8%. For some hospitals, this increase has resulted in crowding and longer wait times to see a provider. Between 2003 and 2009, the mean wait time to be examined by a provider increased by 25% to 58.1 minutes. However, the distributions of wait times are highly skewed since more serious conditions are treated more quickly. The median wait time increased by 22% to 33 minutes.
The National Academy of Engineering and the Institute of Medicine prepared a report presenting the importance of systems engineering tools in improving health care processes (Reid, Compton, Grossman et all 2005). They emphasized the use of simulation. A discrete-event simulation of patient flow through an ED represents the ED as it evolves over time. The simulation’s state is stochastic since the processes such as patient arrival times, patient severity, and treatment times are stochastic and represented by random variables. Thus one must replicate the simulation model to estimate performance measures such as the average waiting time, the histogram of waiting times for a specified set of ED resources such as number of beds, doctor availability and nurse availability.
- Clark, Gordon (2016). “Statistics for Quality Improvement” ASQ Statistics Division Digest, 35(2): 22-26.
- E. Hing, F. Bhuiya (2012). “Wait Time for Treatment in Hospital Emergency Departments: 2009” National Center for Health Statistics Data Brief, No. 102, August 2012.
- P. P. Reid, W. D. Compton, J. H. Grossman et al (2005). Building a Better Delivery System: A New Engineering/Health Care Partnership, National Academies Press, Washington, DC.