One of the most unpredictable environments in the Healthcare Industry is the Emergency Department, ED. We have all been there, spending countless hours in a waiting room only to finally receive medical attention in a hallway, or if you were lucky enough to make it to an examination room, you sat hopeless, wondering if a doctor even knew that you existed. Excessive patient wait times can be tolling on the patient both physically and emotionally; affecting the morale of the patient, doctors, nurses and medical staff. Let’s take a look at some of the most common contributing factors (and their implications) which lead to one of America’s biggest challenges in the healthcare community today; optimizing the ED to reduce patient wait times and increase overall efficiency.
Several elements can be attributed to increased patient wait times including, variability in patient arrival, poor patient flow due to check in procedures/architectural planning, and resource contentions caused by a lack of medical staff or poorly allocated resources. These circumstances (and others) can force severe implications on an ED leading to poor patient and staff morale, inadequate patient tracking, medical error and confusion, decreased patient well being, reduced throughput, and increased operating costs.
So how can these excessive wait times be addressed when dealing with such variability?
For years, many healthcare professionals have turned to traditional simulation tools in an effort to gain operational foresight and have struggled to rectify the key issues that contribute to excessive patient wait times and poor operational efficiency. In theory, these simulation tools seem to be an adequate option to explore when looking to achieve a solution to such operational setbacks, but why is it that so many of these projects have failed?
In the case of traditional tools, the graphical aspect of the model was built in a drag and drop style interface. Creating the graphical model actually generated code that is then sent to the SIMAN engine, a technology from decades past. As our model expanded and the detailed reality of the environment was created, we were required to continue modeling in either an intermediate programming or scripting environment or the SIMAN engine itself. It was found that accurately modeling the environment beyond simple pretty pictures not only took a substantial amount of time (and therefore money), but also demanded an incredible amount of knowledge, and unnecessary workload just to get a glimpse of the operation as a whole; but what about validation, optimization, scenario analysis and using the tool as an integral part of the organization’s continuous improvement program? After all, isn’t process improvement the main focus?
Patented technology of today, to achieve an improved tomorrow.
Unlike the previously mentioned approach, Simcad Pro Health is the only patented dynamic technology that eliminates the fundamental draw back encountered using traditional simulation tools; the coding requirements. This next generation functionality brings users back to the core of process improvement, allowing for immeasurable breakthroughs in simulation modeling and optimization alike...
The Simcad Pro Health ROI: Summary of results using dynamic simulation to improve the ED
keep the appropriate scale/distances of the triage area, processing stations, beds, and waiting rooms easily and accurately ensuring that the travel distances for service providers as well as patients would be tracked for further layout analysis. Creating the model was as simple as placing registration, triage, waiting room, etc… “processes” onto the Simcad Pro Health canvas and the “skeleton” (including both 2D and 3D animation) of the virtual ED was readily created, using the convenient drag and drop interface. Although we had the option to enter our model parameters (timings, capacities, buffers, resource requirements …) manually, we opted to dynamically import these items using the built-in Import/Export wizard which proved useful in saving valuable time while various scenarios where analyzed. Using actual historical data ensured that the natural affect of statistical distribution wouldn’t skew the data and add to the ever present variability that we were looking to mitigate. Once the process flow was mapped, historical data (including arrival rates) were imported, and we ran the model to ensure a valid flow and accurate results compared to historical data. Identifying the poorly performing areas of the ED was quickly determined by observing the visual back up of patients, and through several built in reports that could be accessed at anytime throughout (or after) the simulation run. (We found that the “Simulation Analysis Report “ was specifically useful as it provided a clear and concise view of the poorly performing areas for resources, processes and top level model activity of the ED).