Success Stories

UCSF Reduced Infusion Wait Times at Peak Hours by 31%

Summary

Part of the top-ranked University of California San Francisco (UCSF) health system, the UCSF Helen Diller Family Comprehensive Cancer Center is one of 71 elite NCI-Designated Cancer Centers in the United States. Maintaining a dual mission in clinical care and innovative research, it is also one of only two cancer centers in the Bay Area to receive the prestigious designation of “comprehensive” from the National Cancer Institute. UCSF diagnoses about 6,500 cancer patients annually at its center, and sees 100,000 patient visits.

In one year, UCSF experienced a 21% growth rate in infusion treatment volumes, and experienced consistent operational challenges. UCSF infusion centers were continually operating overcapacity, and a “peaky” utilization schedule led to extended wait times for the many patients who had appointments in the middle of the day. Overall resources at UCSF infusion centers were strained, unlocking further capacity was difficult, and staff and patient satisfaction were poor.

To address these issues and begin optimizing schedules for better capacity use, leadership at the UCSF Helen Diller Family Comprehensive Cancer Center deployed the predictive analytics solution iQueue for Infusion Centers at a site with 12 infusion chairs and three beds. After realizing significant results at that UCSF infusion center site, leadership continued deploying iQueue at four additional UCSF infusion centers, collectively adding 82 more chairs. iQueue’s scheduling templates helped UCSF “flatten” the over-capacity midday peaks, evenly distributing appointment load to unlock capacity for add-ons and delays and promote a more predictable day for staff.

Key Takeaways

Connect the quality of infusion staff and patient experience with the level-loading of infusion schedules

Identify the need for a technology solution to redistribute capacity evenly in infusion schedules

Understand how optimizing infusion schedules can accommodate overall growth in patient volumes

Results

31% lower waiting times at peak hours

26% lower average waiting time

42% lower average hours over capacity

8% lower overall average daily peak

Our utilization curve shows we were level loading after [implementing iQueue]. We were able to unlock the capacity to help deal with unexpected delays and add-ons. Our nursing team felt there was the ability to rest and plan out their days and take a lunch break. It was a huge satisfier for employees and patients.

Aubrey Wong

Administrative Director of the University of California, San Francisco