Emory Healthcare stands as the only academic medical center in the state of Georgia, with an impressive network of ten hospitals, over 580 locations, and 230 primary care facilities across the state. Ranked as the number one healthcare system in Georgia, Emory has built a reputation for excellence in patient care, medical education, and innovative healthcare solutions.
Among Emory’s extensive network of facilities is the Dunwoody Family Medicine clinic, a comprehensive teaching facility that opened in October 2024. This newly established clinic represents a significant upgrade from its previous location, expanding from 25 to 33 exam rooms to accommodate growing patient demand. The facility provides a wide range of services including primary care, family medicine, orthopedics, spine and cardiology, as well as imaging, laboratory services, ambulatory surgery, and physical therapy.
What makes the Dunwoody clinic particularly unique is its role as a teaching facility. As Victoria Jordan, Vice President of Process Optimization and Innovation for Emory Healthcare explains, “The Dunwoody Family Medicine clinic is a resident-driven clinic. In fact, over 70% of the providers within the clinic are residents themselves.” This teaching environment creates specific operational challenges that impact both patient experience and educational requirements.
With a capacity to see over 350,000 patients annually and a projection to serve more than 20,000 patients in 2025 alone, optimizing operations at this facility became a critical priority. To address this challenge, Emory Healthcare partnered with Georgia Tech Industrial and Systems Engineering senior design students and Simio to develop a simulation model that would help identify opportunities for improvement.
“We specifically wanted to demonstrate how we could use simulation at Emory Healthcare,” explains Dr. Jordan. “We don’t have a lot of people that have used it. And as we were working with our primary care group, they were anxious to see. So this was really more of a demonstration to start with.”
The Dunwoody Family Medicine clinic faced a complex operational challenge stemming from its dual mission of providing excellent patient care while serving as a teaching facility for medical residents. This created unique workflow requirements that significantly impacted patient wait times and overall clinic efficiency.
As a resident-driven clinic, the facility operates under specific educational protocols that affect patient flow. Residents at different stages of their training have varying levels of autonomy and supervision requirements:
These supervision requirements created significant bottlenecks, particularly at the preceptor’s office. With only 2-3 preceptors available each day supervising 10 providers, 3-4 nurses, and 5-7 medical students, wait times accumulated throughout the day.
Data analysis revealed concerning patterns in patient wait times:
“We noticed system backlogs,” explained one of the Georgia Tech team members. “At the beginning of the day, there are very little wait times. As the session progresses, towards the middle of the day, the end of the morning session, and at the end of the day, appointments start ending later and later.”
The complexity of the clinic’s operations, with multiple interdependent processes and the unique preceptor-resident interaction model, made it difficult for staff to identify the root causes of delays and develop effective solutions. This environment presented an ideal opportunity for the application of simulation in healthcare to visualize, analyze, and optimize patient flow.
To address these complex challenges, Emory Healthcare partnered with Georgia Tech Industrial and Systems Engineering students to develop a comprehensive digital twin in healthcare using Simio simulation software. This approach allowed them to model the intricate operations of the Dunwoody Family Medicine clinic and test potential improvements without disrupting actual patient care.
The project began with extensive data collection from multiple sources:
With this data in hand, the team performed statistical analysis to identify the most significant factors affecting each step in the patient journey. They discovered that:
To avoid overfitting and simplify the model, the team conducted correlation analysis to cluster similar attributes. For example, they found that patient arrival patterns could be grouped into just three categories (8 AM, 1 PM, and all other hours) rather than modeling each hour separately.
Using Simio simulation software, the team created a detailed digital twin of the Dunwoody clinic that visually represented the physical layout, patient flow, and resource allocation. The simulation included:
“We tried to model the clinic visually as best as possible just to make this as useful as possible,” explained one of the Georgia Tech team members. The simulation allowed clinic staff to visualize patient movement, identify bottlenecks, and understand how delays propagated throughout the system.
A key innovation was the development of a data preprocessor tool that allowed the clinic to import actual patient schedules into the simulation. This enabled them to test specific days or scenarios by simply selecting a date, running the script, and importing the resulting CSV files into Simio.
To ensure the model’s accuracy and reliability, the team followed healthcare simulation standards for validation:
This validation process revealed that while many aspects of the model accurately reflected reality, some refinements were needed. For example, the simulation strictly enforced a 1-to-1 assignment between patients and nurses, whereas in reality, nurses would sometimes help each other when backlogs developed.
The healthcare simulation project delivered valuable insights that led to several practical recommendations for improving clinic operations. Through what-if analysis, the team identified four key opportunities for patient flow optimization:
The simulation revealed that changing the preceptor assignment system from a 1-to-1 mapping (where each resident is assigned to a specific preceptor) to a first-come, first-served model could reduce wait times at the preceptor office by 31%. This simple operational change required no additional resources but could significantly improve patient flow.
The team discovered that allowing residents to visit available preceptors opportunistically, rather than strictly adhering to their maximum stacking limits, could further reduce preceptor waiting time. For example, if a second-year resident had seen two patients (their maximum stack) but a preceptor was available, allowing them to consult immediately rather than waiting for their assigned preceptor would improve efficiency.
One of the most straightforward yet impactful findings involved the physical layout of the clinic. The time study revealed that first-year residents (who need to consult preceptors most frequently) could reduce their travel time by 60% if they were assigned to Pod 4, which was closest to the preceptor room.
As Dr. Jordan noted, “It was interesting that one of the recommendations the team made was to move the first-year residents who have to check in with a preceptor after each visit to the pod that was closest to the preceptor office, which sounds completely obvious in hindsight, but it’s something that the leaders of the clinic were really happy to see because they’re like, ‘We see it every day and we just never thought about that.’”
The analysis showed that the clinic’s standard 20-minute and 40-minute appointment slots didn’t always align with actual service times. By better matching appointment lengths to typical service durations for different visit types, the clinic could reduce both provider idle time and patient waiting time.
The impact of simulation training on patient care at Emory Healthcare extended beyond specific operational recommendations. The project delivered several broader benefits:
“The primary care team was really excited about the possibility of implementing some of the recommendations from the team,” Dr. Jordan explained. “They gave us very positive feedback. They said it really helped to have some fresh eyes look at the process and identify things that in hindsight seemed very obvious.”
The success of this initial healthcare simulation project has opened the door for expanded applications across Emory Healthcare. The organization is already planning next steps to build on this foundation:
The next phase will focus on optimizing scheduling for residents and patients to ensure all residents complete the procedures required for their training. “Phase two will be to optimize scheduling for the residents and patients so that we can make sure all our residents complete all the procedures on their checklist,” explained Dr. Jordan.
Emory has already requested another Georgia Tech team to work on this optimization model for the fall semester, demonstrating their commitment to continuing this data-driven approach to healthcare process improvement.
Beyond the Dunwoody clinic, Emory sees potential for applying similar simulation models across their extensive network. “We have over 300 clinics across the Atlanta area,” noted Dr. Jordan. “We’re looking for how we can use similar modeling to optimize patient flow and resource usage in those as well.”
This expansion represents a significant opportunity to standardize best practices and improve operations throughout the Emory Healthcare system.
Several valuable lessons emerged from this healthcare simulation initiative:
The healthcare simulation project at Emory’s Dunwoody Family Medicine clinic demonstrates the powerful impact that digital modeling can have on healthcare operations, particularly in complex teaching environments. By creating a detailed digital twin of the clinic, the team was able to identify specific, actionable improvements that could significantly reduce patient wait times and enhance both the educational experience for residents and the care experience for patients.
As Dr. Jordan summarized, “Through this project, the Georgia Tech team, along with Simio’s facilitation support, did a great job of getting an initial digital twin in Simio. It gave us a solid preliminary model upon which we will build.”
The success of this project highlights the growing importance of simulation in healthcare as organizations seek to optimize resources, improve patient experiences, and maintain educational excellence in teaching facilities. By following healthcare simulation standards and leveraging advanced modeling capabilities, Emory Healthcare has established a foundation for continuous improvement that can be expanded across their entire system.
“We would like to specifically thank Simio for the work that they did to help us get this going,” Dr. Jordan concluded. “Greer and her team helped us make sure that we had a consistent system between the software that we were using internally and the software that the students were using. They also helped us with education and consulting support that was invaluable in our efforts.”
This case study illustrates how healthcare simulation can transform operations in ways that benefit all stakeholders—patients, providers, residents, and the healthcare system as a whole—while providing a roadmap for other organizations facing similar challenges.