Simulation Applications in a Healthcare Setting

Introduction

The purpose of this chapter is to broaden the reader’s knowledge in regards to the gamut of simulation implements. This chapter is guided towards engineers, neophyte simulation practitioners, analysts and technical staff who, in their daily undertakings, encounter uncertain situations. During their quest for answers to these events or undertakings, the analyst ends with incomplete or inconclusive results. These inconclusive results may be related to the extent of the initial hypothesis. The following real case demonstrates a situation where an inconclusive result showed the need for a simulation:

On a major southeast teaching hospital, an analyst was tasked with determining the optimal space needed to store specialty beds, such as burn, bariatric, orthopedic or other specialty beds, at a storage area of the hospital. These specialty beds are delivered from a rental company when a nursing floor requests it. The standard beds needs to be removed and stored until the specialty bed is no longer needed. A straight forward method, using static simulation or analyzing this problem at a finite time, is to account and verify the total number of specialty beds requested at the end of each day. With the total number of beds requested on a daily basis, the analyst can determine the space needed for storage. But, did the analyst perform the most suitable analysis for this situation? In a short answer, no, the analyst failed to take into consideration the dynamic aspect of this specific situation, such as a dynamic simulation or analyzing the problem and how it behaves over a known period of time. The analyst may under or overestimated the space needed.

Based on the initial assumption in which beds are requested on a daily basis and are accounted for does not provide the real picture. Specialty beds rental, seen as an independent entity, fails to illustrate the rental process complexity and the relationship to other entities, namely patients. Each specialty bed is allocated to a patient and to the patient’s length of stay in the hospital. Using a Gantt chart and plotting each specialty bed’s length of stay, gathered from patient’s information, will show that some days there are more stored beds on the hospital than beds ordered on the same day.

Following the dynamic simulation analysis performed by the analyst, Figure 1 shows a Gantt chart where two beds were ordered on day 1, an additional two beds were ordered on day 2, one bed on day 3 and one bed on day 4. So, the maximum number of beds ordered on any day is two beds. If the analyst utilizes this process, he will be underestimating the real need. Analyzing this problem from a different perspective by taking into consideration patient’s length of stay, the analyst will realize that the number of beds is higher. For instance, there are two beds on day 1, four beds in day 2 (an additional 2 beds compared to the prior method), and three beds on days 3 and 4 (an additional 2 beds each day). Now, the maximum number of beds ordered on any day is four beds instead of two.

Figure 1.

The real case analysis, in which the above figure is based on, showed that there was a need for 19 beds at any time instead of the expected 30 beds needed. Savings related to the reduction of space utilization of 11 beds, 32 square feet each, at a cost of $300 per square feet of construction, resulted in a reduction of $105,600 in initial costs. This initial cost does not take into consideration the expected long-term maintenance expenses related to this additional space.

As we have seen, there are other methods to solve uncertainty besides typical mathematical methods. An alternative method is simulation applications and these ranges from paper-and-pencil and board-game reproductions of situations, static simulations to complex computer-aided interactive systems.

This chapter will discuss dynamic simulation methods and techniques. Also, this chapter will answer the following questions: