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Improve Patient Wait Time and Increase Operational Efficiency in the ED / ER

One of the most unpredictable environments in the Healthcare Industry is the Emergency Department: the ED. We’ve all been there, spending 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 wondering if a doctor even knew you existed. Excessive patient wait times can be tolling on the patient both physically and emotionally, while also affecting the morale of the 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
  • Resource contentions caused by a lack of medical staff or poorly allocated resources.

These circumstances (and others) can lead to poor patient and staff morale, inadequate patient tracking, medical error and confusion, decreased patient wellbeing, reduced throughput, and increased operating costs.

How can excessive wait times be addressed when dealing with such variability?

Many healthcare professionals have turned to traditional simulation tools in an effort to gain operational foresight, but have struggled to rectify the 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, so why have so many of these projects failed?

In the case of traditional tools, the graphical aspect of the model was built in a drag-and-drop style interface, generating code that was 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 labor 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 drawback encountered using traditional simulation tools: coding requirements. This next-generation functionality brings users back to the core of process improvement, allowing for breakthroughs in simulation modeling and optimization alike.

The Simcad Pro Health ROI: Summary of results using dynamic simulation to improve the ED

Creating the model was as simple as placing registration, triage, waiting rooms, and other processes onto the Simcad Pro Health canvas using the convenient drag-and-drop interface. The skeleton of the virtual ED was created in both 2D and 3D, keeping the appropriate scale to ensure that travel distances for service providers and patients could be tracked for further layout analysis.

Although we had the option to manually enter our model parameters--timings, capacities, buffers, and resource requirements--we opted to dynamically import these items using the built-in Import/Export Wizard which saved time while various scenarios were analyzed. Using actual historical data ensured that the natural effect 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. Poorly performing areas of the ED were quickly identified by observing patient backup and through several built-in reports that could be accessed at any time during or after the simulation run. We found that the Simulation Analysis Report was particularly 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.

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