Simulation-Based Design and Traffic Flow Improvements in the Operating Room

A simulation model was created to model the traffic flow in the operating room. A key research challenge in operating room design is to create the most efficient layout that supports staff and patient requirements on the day of surgery. The simulation allows comparison of base model designs to future designs using several performance measures. To develop the model, we videotaped multiple surgeries in a set of operating rooms and then coded all activities by location, agent and purpose. Our current analysis compares layouts based on total distance walked by agents, as well as the number of contacts, measured as the number of times agents must change their path to accommodate some other agent or physical constraint in the room. We demonstrate the value and capability of the model by improving traffic flow in the operating room as a result of rotating the bed orientation.

The operating room (OR) is a sensitive and high-risk environment. It is estimated that major complications after surgery occur in 3-22% of inpatient surgeries in industrialized countries, where around half of these adverse events were determined to be preventable (WHO 2009). It is no wonder that both surgeons and nurses have expressed frustration with the way the design of the OR affects work flow negatively.

Layout is an important factor when designing an OR since it can highly affect the flow of staff and materials during surgery (Palmer et al. 2013). However, little is known about the design of the OR and how it can improve performance outcomes (Joseph et al. 2017). More than half of the movements during robot-assisted surgeries can be eliminated with an improved OR layout (Ahmad et al. 2016). Although there is no clear study about the nature of surgical flow disruptions (SFD) in the OR (Wiegmann et al. 2007), layout-related features such as improper placement of equipment and poor utilization of space and furniture result in disruptions (Palmer et al. 2013). Another study found that ORs with an innovative layout experience shorter case times and higher throughput (Sandberg et al. 2005). It has been shown that poor layout of the OR have the potential to increases SFDs and may also increase surgical site infections (SSIs) in the OR (Wahr et al. 2013).

Each layout contains a number of functional zones, where each zone has been defined based on the key activities that take place in that particular zone. The literature in OR planning and design mainly considers three zones: the anesthesia zone, the circulating zone, and the sterile zone (Joseph et al. 2017). However, to better address activities in the OR, we consider 17-20 zones depending on surgery type. According to these zones and the placement of materials, an ideal layout might be attained by increasing the OR size to accommodate more equipment and larger teams (Schneider 2012) but an analytical approach is required to get a sense of the performance of the layout.

Among operations research techniques, simulation may be the only alternative to provide solutions to complex problems. For example, it is not possible to obtain closed form solutions for complex queuing systems in OR, but they might be readily obtained with simulation methods. The idea is to model the behaviour of individual elements in the system.

We develop a simulation model to study the flow of Patient, Equipment, Materials, Staff and Information (PEMSI) during surgery. The model not only tracks location and PEMSI activities for human and object agents, but also accounts for contact avoidance as the agents move within the OR. The research question is how to create the most efficient layout that supports staff and patient requirements on the day of surgery. The goal of this research is to develop a decision tool that can inform and validate future operating room designs. The model allows us to statistically compare base model OR designs to future designs using several performance measures.

The remainder of this paper is structured as follows: first, we present the method we used to obtain data as input for the simulation model. Then we will describe how the model is developed. In the next section we will have some preliminary results by comparing three different layouts using the simulation model. Finally, we will conclude by addressing limitations of the model and how the model can be further developed to give more reliable results.

Method

We observed and collected data on 35 surgeries at the Medical University of South Carolina (MUSC). Next, we coded videos of surgeries using Noldus Observer XT. Video coding is the process of observing and analyzing video files in an observational research. In the OR, video coding helps us to code subjects, log events, and create valuable qualitative and quantitative results. These videos were recorded in four ORs for the person’s role, location, and PEMSI activity using a map of designated zones for each OR. Each OR had its own map with zones designated based on which type of activity was performed in that zone or who visited it most often. This coding was exported to an Excel file and used as an input for the simulation model. The main objective of the coding was to develop measures to analyze the risks to both the patient and staff safety that exist within the PEMSI flows.

Subjects can be either human or object agents. Human agents are surgeons, doctors, circulating or scrub nurses, anesthesiologists, cleanup technicians, or other staff that occasionally enter the OR. Also object agents are carts, trash cans, or any other types of objects that are being used in the OR. Obviously, the list and number of subjects might change from surgery to surgery. In this study, subjects were coded by location and activity during surgery. If the agent is performing more than one activity at a time, we gave the priority to the most important activity occurring.

Locations (zones) were defined according to the type of function conducted in them. Also, areas within the OR that share the same function (i.e. left side and right side of the surgical table) have been differentiated in coding, since tracking movement among these locations (flow) was important. Each surgery is carried out in an OR with seven types of zones based on where staff are typically stationed and the location of supplies and equipment. These zones are:

  • head/foot of the surgical table and surgical table zones 1-2;
  • anesthesia work station zone;
  • circulating nurse work station zone;
  • support zones 1-6;
  • supply zones 1-2;
  • transitional zones 1-3;
  • door 1-2 to corridor and door to sterile core.

The ”surgical table zones 1-2” denote the sides of the operating table. The ”supply zones 1-2” consist of cabinets containing supplies. The ”support zones 1-6” contain movable items, usually furniture and equipment being stored or trash cans. The ”transitional zones 1-3” are the circulation areas and connect most of the zones together. The rest of the zones were clearly named by the type of function they have. The reason of differentiating more areas for each type of zone is to define locations more specific and meaningful in terms of the movement of subjects in the OR.

Simulation was used to compare layouts

Three layouts to be compared: top-left — base layout, top-right — rotating the bed 90 degrees, bottom — changing the position of surgery boom.

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