Infusion Center Leverages Data Science To Overcome Scheduling Complexities

By Christine Kern, contributing writer

Case study demonstrates a 30 percent reduction in patient wait times with data analytics platform.

Healthcare Informatics reports data science is transforming operations at Stanford Health Care Infusion Centers, cutting patient wait times by 30 percent at peak times at its infusion centers with the deployment of a data analytics platform that uses data science and optimization algorithms.

According to Sridhar Seshadri, vice president of Cancer Services at Stanford Health Care, patients were facing long wait times in the middle of the day, between 11 a.m. and 2 p.m., and the infusion chairs were underutilized during other times of the day.

“There is a classic healthcare phenomenon where there is a valley in the early part of the week and then again in the later part of the week and patients build up in the middle of the week, and then you see the same pattern happening during the day, when it’s slow in the early morning and evening, and patient volume peaks in the afternoon. You will see this in most production flows in healthcare, so basically areas such as chemotherapy labs, radiation therapy and the operating room. And, we were seeing this same problem in our infusion centers,” Seshadri says.

The solution to this problem lies in understanding and employing the game of Tetris. If an institution can slot all treatments in a day perfectly without any gaps, underutilization would be avoided in the mornings and evenings, and overutilization would be avoided in the afternoons. That means more unlocked capacity (and hence, more patients seen), higher revenue, less operational cost, and happy patients and providers.

“Optimized slotting or level loading of patients, if done right, can be the easiest way to improve patient access and your bottom line — driving more revenue by avoiding underutilization and reducing operational cost by avoiding overutilization,” concluded Giridharadas.