Note: Simul8 was recently acquired by Minitab. Read more about this exciting development here.
Several years ago, late at night, my son developed a high fever. The parents out there know that fevers never seem to develop during business hours.
We ended up taking him to the emergency room. Upon arrival, it was clear that there would be a long wait. After waiting for four hours, we were finally seen, and thankfully he ended up being fine after a few days and some medication.
During those four hours of waiting with a cranky and sick two-year-old, the thought crossed my mind—can this wait until tomorrow at an urgent care center or his pediatrician’s office? Should we just leave and monitor him at home overnight? It turns out, doing so would have been unsafe, and he ultimately had a much more comfortable night due to the care he received.
Emergency Department (ED) walkouts, where patients leave before receiving treatment, present a critical challenge to hospitals. With walkout rates often ranging from 1% to 8% of visits depending on the facility, the consequences are far-reaching. Patients who leave without care face serious risks to their health as unattended medical needs can escalate into severe complications. Emergency Departments are often a key focus of this conversation because there are often not enough available beds in other departments, and beds in other departments are blocked because there are not enough discharge facilities. They’re all connected, and all a part of “systems thinking” or “systems modeling.”
Hospitals also suffer financially since unseen patients generate no revenue. Beyond these immediate impacts, walkouts can harm a hospital's reputation, leading to negative word-of-mouth and discouraging future visits.
The Role of Simulation
Reducing walkouts requires more than incremental fixes; it demands an innovative, data-driven approach. By combining Simul8 and Minitab Statistical Software, we can create a simulation of the ED. In simple terms, a simulation is a virtual model to test and analyze processes or systems in action. In this instance, the simulation allows hospitals to test interventions in a controlled environment, paving the way for optimized patient flow without disrupting daily operations.
Step 1: Analyzing Historical Data with Minitab
The journey begins with a detailed analysis of historical ED data. Using tools in Minitab Statistical Software like control charts, time series analysis, and regression, hospitals can uncover patterns in patient arrivals, track average wait times, and evaluate resource utilization.
Examining patient demographics and reasons for leaving without being seen can shed light on underlying issues contributing to walkouts. For example, analyzing patient demographics might reveal that younger patients are more likely to leave during late-night hours due to longer wait times, highlighting a need for better staff coverage or streamlined triage during those periods. This deep understanding of the current state forms the foundation for targeted hypothesis solutions.
Step 2: Using a Simulation
Armed with insights from the data, the next step is to construct a comprehensive Simul8 model of the ED. This simulation accounts for the real-world dynamics of the department, factoring in key elements such as:
- Patterns of patient arrivals and varying levels of acuity
- Resource allocation, including the availability of doctors, nurses, and beds
- Treatment pathways and the logic behind decision-making processes
This simulation captures the complexity of ED operations, which allows hospitals to experiment with potential changes.
See how Simul8 has used simulation to reduce patient wait times.
Step 3: Testing and Refining Interventions
Once the simulation has been used, it becomes crucial for exploring and testing "what-if" scenarios. For instance, adding extra nurses or support staff during peak hours can be tested to determine if it reduces wait times and improves patient flow. Adjusting triage protocols to prioritize critical cases might show how quickly high-risk patients can receive care. Even seemingly simple interventions, like providing regular updates to waiting patients or offering alternative care options, can be evaluated to see their impact on overall satisfaction and retention.
Another major benefit is that even the most radical ideas can be tested with zero risk to patient care.
Managing improvement projects? See how Minitab Engage can solve your most pressing CI challenges at your hospital or network. 
Step 4: Data-Driven Insights with Minitab
After running these simulations, Minitab’s statistical tools come into play to analyze the results. Through regression analysis, variance and sensitivity analysis, control charts, and box plots and other visualizations, hospitals can identify the most impactful interventions and quantify their potential benefits. This ensures that decisions are grounded in robust data rather than guesswork.
Data can be recollected, and tests can be used to see whether interventions led to a statistically significant difference in ED walkouts.
A Path to Safer, More Efficient Emergency Care
By leveraging the predictive power of Minitab Statistical Software and the simulation capabilities of Simul8, hospitals can tackle the root causes of ED walkouts. This innovative approach allows for thorough testing and refinement of interventions, ensuring that changes lead to meaningful improvements in patient safety, operational efficiency, and overall satisfaction. With the power of data analysis and simulation, hospitals can confidently chart a path toward better emergency care.
The best part? All of this can be done with the world-class power of Minitab and Minitab trainers.
