Cancer-patient wait times could be reduced by 44%, new research shows
An unexpected marriage of industrial engineering and patient processing at cancer treatment clinics has yielded some exciting results.
We created a scheduling algorithm and software that reduced patient wait times. And we reduced interruptions at the pharmacy, resulting in a theoretical 44 per cent total reduction in average waits.
These results were achieved by improving the coordination between the different groups of professionals — oncologists, pharmacists, nurses and administration — to facilitate a more complete hand-off of information about the patient’s condition and treatment requirements.
Bhuiyan’s findings were published this summer in the Journal of Oncology Practice. The article was co-authored with postdoctoral fellow Samuel Suss, engineering professor Kudret Demirli and Jewish General Hospital (JGH) oncologist Gerald Batist.
“Our goal was to reduce or eliminate the current need for frequent clarifications from the pharmacist before he or she can prepare the medication,” explains Bhuiyan, who joined Concordia’s Office of the Provost and VP, Academic Affairs as vice-provost of Partnerships and Experiential Learning in January 2017.
Motivation meets innovation
When Bhuiyan’s mother was diagnosed with cancer in 2012, the professor was initially surprised that the clinic’s treatment processes were so streamlined.
“The wait times were quite short,” she added. “But as the year went on, and as demand increased, things changed — wait times increased.”
That’s when Bhuiyan had the idea to apply her engineering background to the problem at hand: patient flow in treatment centres.
“Much of industrial engineering is about how to optimize processes and systems. The project is very much in line with what I do,” she says, adding that her mother is alive and doing well.
Working closely with the Segal Cancer Centre at the JGH in Montreal, Bhuiyan’s team used value stream mapping of the end-to-end process in the treatment centre, along with time studies and statistical analysis of each of the process steps in the patients’ trajectories.
Stochastic models and clinic simulations
“With that data, we constructed a stochastic model of the operations of the clinic to account for the variability of patient trajectories and service times at each process step,” Bhuiyan adds.
“We used this model in a discrete event simulation of the clinic, running ‘what-if’ scenarios to observe the effects of improving coordination between groups of professionals in the clinic and improving patient appointment scheduling. Moreover, we used constraint programming to find optimal patient schedules for treatment.”
It’s a win-win
The improvements to patient flow outlined in Bhuiyan’s ongoing study required no additional staffing resources and caused no disruption to existing facilities. Her research found that staff work could be reduced by eliminating the need to manage separate appointment schedules for patient visits to oncology and chemotherapy. Reduced wait times also means that waiting areas can be smaller.
“There is substantial scope for improving the quality of health-care services, and reducing operating costs, if we can further coordinate delivery processes — and not just in this one clinic,” Bhuiyan concludes.
“Our work is transferable to other health-care centres that offer similar services in a similar environment
The need is crucial, she adds, noting that one in four Canadians will die of cancer, according to the Government of Canada’s cancer statistics report for 2017.
Gerald Batist, director of the Segal Cancer Centre, expressed, "our tremendous enthusiasm and appreciation for this work by the Concordia team, which makes a significant contribution to improve the experience of our patients as they navigate through the complex and sometimes distressing maze of cancer care."
Project funding was provided by Mitacs, a federal granting agency that aims to build partnerships between academia, industry and the world “to create a more innovative Canada.”