NEW JERSEY – Researchers at Rutgers University say a computer simulation model helped significantly reduce wait times for blood cancer patients while increasing the number of patients treated each day.
The study, published in the Annals of Operations Research, examined how researchers from Rutgers Cancer Institute and Rutgers Business School created a digital simulation of the institute’s blood cancer clinic to identify operational bottlenecks and test potential improvements.
According to the researchers, the changes reduced laboratory blood-work turnaround times from about 90 minutes to less than 30 minutes and increased the clinic’s daily infusion capacity from roughly 50 patients to about 80.
The team said the approach could potentially be applied to cancer centers and other health care settings across the country.
“The multistep process for oncology patients exists at every cancer center in the country, if not the world,” said Andrew Evens, deputy director for clinical services at Rutgers Cancer Institute, the state’s only National Cancer Institute-designated Comprehensive Cancer Center, together with RWJBarnabas Health.
When the project began, the blood cancer clinic treated approximately 50 infusion patients daily while also serving more than 200 patients who required blood checks, consultations or transfusions.
“Cancer is difficult, emotionally and physically,” said Evens, a senior author of the study and Chief Physician Officer of the Jack & Sheryl Morris Cancer Center and Rutgers Cancer Institute. “So, we wanted to make sure patients were able to go through these different junctures of their visit in a very efficient and patient-centered way.”
Evens, who earned an Executive MBA from Rutgers in 2022, partnered with Rutgers Business School faculty to explore whether business operations strategies could improve patient flow.
A team led by Xin Ding, of Rutgers Business School’s Department of Supply Chain Management, analyzed patient movement through the clinic using direct observations and electronic health records.
“From there, we can generate distributions and find patterns,” Ding said. “What is the pattern for them to come? What is the pattern for them to leave? And we can do that for each point of the process.”
The resulting digital model allowed researchers to test operational changes before implementing them in the clinic.
Researchers found that staffing was not the primary source of delays. Instead, significant bottlenecks were linked to blood samples being processed at an off-site laboratory and a scheduling system that placed short visits and lengthy infusion treatments into the same queue.
The simulation indicated that moving laboratory processing on-site and creating a separate fast-track system for supportive care services could reduce average visit times by 75 to 90 minutes, even with increased patient volumes.
The clinic subsequently moved laboratory processing on-site and implemented a fast-track system separating cancer treatments from services such as transfusions. Those changes continue to be used at the Jack & Sheryl Morris Cancer Center in New Brunswick.
Evens said other health care providers could learn from the findings but would need to conduct their own analyses because each facility operates differently.
“You could probably take some learned lessons from this article and apply them, but at the end of the day, you’re going to need to do your own analysis,” he said.
Researchers noted that the same simulation approach could potentially be used to improve patient flow in outpatient clinics, emergency departments and surgical units.
