The Fast Track to Efficiency: How Simulation Modeling Improves Oncology Clinic Flow

Long clinic wait times remain a persistent challenge in oncology, where adequate care isn’t a single appointment but rather a tightly coordinated sequence of steps. In this interview, Andrew Evens, DO, MBA, MSc, Deputy Director for Clinical Services at Rutgers Cancer Institute and Chief Physician Officer at the Jack and Sheryl Morris Cancer Center, describes how his team used simulation modeling to identify operational bottlenecks in a blood cancer and bone marrow transplant unit. By identifying laboratory turnaround time and an inefficient intake process as the primary barriers, his team redesigned operations and nearly doubled the number of patients treated each day.

Mapping the Complexity of Oncology Care

Cancer treatment often requires patients to complete several steps during one visit. “It’s really a common issue, and the commonality relates to any business that involves a multi-step process,” Dr. Evens explained. “In this particular case, it’s a bone marrow transplant and blood cancer unit, so it’s a very busy center.” In a single visit, patients may navigate check-in, written consent, laboratory testing, vital sign assessment, physician evaluation, and infusion. 

“We all know that cancer is complex,” Dr. Evens said. “When patients arrive for their visits, it’s a multi-step process before they ultimately receive their treatments.” Because each step can affect the next, delays early in the process can propagate throughout the clinic. First-time treatments may require additional documentation and consent before therapy can proceed, creating further opportunities for backups.

Using Simulation to Test Solutions 

Rather than focusing only on conventional operational measures, the team partnered with Rutgers Business School’s healthcare analytics group. “We decided to look outside the box,” Dr. Evens said. The collaboration included PhD faculty members, trainees, and students who helped analyze the clinic’s workflow. 

The team developed a simulation model that allowed them to test potential changes before making them in the clinical environment. “Using techniques such as a simulation model, which we use with the Rutgers Business School, allowed us to do that,” Dr. Evens said. “Once you create that model, you’re able to put in certain levers or buttons to pull and push respectively to say, ‘Well, what if I increase staffing by 20%? Or what if I reduced the laboratory result wait time by 40%?’”

This approach enabled the team to evaluate multiple hypotheses and focus resources on the interventions most likely to improve throughput and patient experience.

Identifying the Major Bottlenecks

The model produced an unexpected finding. “We thought it could have been related to staffing ratios, but it turns out that there were two major barriers that we identified,” Dr. Evens said.

The first was laboratory turnaround time. Patients needed rapid laboratory results to confirm that they could safely receive treatment that day. The second was the clinic’s single intake pathway for all infusion patients. Patients receiving anticancer therapy and those who had appointments for supportive care—such as blood transfusions or intravenous fluids—were entering the same process. 

“It was creating this funnel, and was everyone getting caught up in that funnel and backed up,” Dr. Evens said.

Redesigning the Workflow

The cancer center addressed the two barriers directly. It invested in faster laboratory testing and created a “fast track” pathway for supportive care patients, separating them from those receiving anticancer treatment. “By doing that, amazingly, we're able to almost double our number of patients coming through and being treated on a daily basis,” Evens said.

The results demonstrate how targeted operational changes can expand access without relying solely on additional staffing or physical capacity.

Scalability and the Future of Operational AI

While the study focused on a highly specialized unit, Dr. Evens believes the underlying methodology is "disease-agnostic" and can be adapted by other oncology programs regardless of their specific patient population. Successful implementation requires centers to commit to rigorous data collection and validation to ensure the model reflects their unique clinical environment.

Dr. Evens views this approach as a strategic application of technology that can be deployed across the oncology landscape. "In some ways, I view this as a version of AI," he noted. "I think this is a way—and I'm using the AI term as a big umbrella—where AI, when done in smart and strategic ways, can really drive down healthcare costs and improve patient experience." By leveraging internal talent and cross-disciplinary partnerships, other centers can replicate this success to improve access and efficiency.

The Power of a Multidisciplinary Approach 

Dr. Evens concluded by emphasizing that the success of the project was not the result of a single perspective, but rather the integration of clinical, nursing, and administrative expertise. He advocates for oncology leaders to seek internal collaborations before turning to outside solutions, noting that the talent required to solve complex operational challenges often already exists within their own institutions.

"When you bring a lot of smart, like-minded people around a room, you're able to look at things a little differently, solve problems, and create opportunities together," he observed. Ultimately, he believes that clinicians and administrators can find the answers they need by looking "under your own hood" and bringing diverse teams together to focus on the common goal of enhancing patient care.

Key Takeaways

• Oncology bottlenecks may stem from laboratory processes and patient queues, not just staffing.

• Simulation modeling can test operational changes before implementation.

• Separating supportive care and anticancer-treatment pathways can reduce congestion.

• Accurate workflow analysis may require direct observation in addition to electronic medical record data.

• Multidisciplinary collaboration is central to sustainable improvement.

About Dr. Evens

Andrew Evens, DO, MBA, MSc, is the Deputy Director for Clinical Services at Rutgers Cancer Institute and the Chief Physician Officer at the Jack and Sheryl Morris Cancer Center. He also serves as the System Director of Medical Oncology at RWJBarnabas Health Medical Group and the Associate Vice Chancellor for Clinical Innovation and Data Analytics at Rutgers Biomedical and Health Sciences. Dr. Evens specializes in the treatment of lymphoid malignancies, and he has been the principal investigator of more than 100 phase 1, 2, and 3 clinical trials investigating novel therapeutics for lymphomas.