Infusion centers are caught between climbing oncology volume, a workforce that hasn’t rebounded post-pandemic, and margin discipline that rules out the old answer of more chairs and more nurses. The operational lever has shifted to better demand pacing and more balanced workload distribution.
During an April 14 featured session at Becker’s Oncology Executive Summit, hosted by LeanTaaS, Marisa Quinn, DNP, RN, director of nursing for infusion services and cancer acute care at UCSF Health in San Francisco, and Pamela Tobias, senior director of customer success at LeanTaaS, walked through a decade of operational change at UCSF’s Helen Diller Family Comprehensive Cancer Center — an NCI-designated center running more than 100,000 infusion visits a year across 12 centers and 200-plus chairs.
Note: Quotes have been edited for length and clarity.
1. Scheduling AI flattened the midday demand curve
UCSF began piloting LeanTaaS’s iQueue for Infusion Centers in 2016 after a patient told Dr. Quinn the center was “booking us like an airline, overbooked seats — and we have cancer and we don’t have time to wait.”
“We were scheduling using a rear view mirror,” Dr. Quinn said.
One year after implementation, the pilot unit saw a 31% reduction in waiting time at peak hours and 42% reduction in average hours over capacity. As UCSF expanded the tool across four additional sites, median wait times from check-in to seating held steady at 23 median minutes through the entire growth period.
2. AI-driven staffing rebalanced workload without removing clinical judgment
Now that patient scheduling had been solved, the team turned to another common bottleneck: workload distribution among nurses. In 2024, UCSF launched iQueue’s Patient Assignment tool, which recommends which nurse should take the next patient based on workload, skill mix and acuity, with the charge nurse retaining final say.
“This is AI as a copilot, augmenting decision making, not replacing it, and keeping clinical judgment at the center,” Dr. Quinn said.
Pilot post-survey results showed a 9% improvement in workload balance, a 9% productivity gain measured in patient hours per nurse hour, and 75% of nurses reporting improved assignment pacing.
3. Change management and integration drove adoption
After positive pilot results, UCSF deliberately slowed the rollout until the tool could integrate with its new workforce management platform via two-way API, on the view that adoption at scale needed deeper groundwork. Dr. Quinn’s three lessons from the journey: AI governance approval requires long lead times; change management starts with people and process, not technology; and real-time visibility for front-line staff — not just leaders — turns transparency into trust.
The financial case
UCSF is projecting $2.5 million in incremental revenue for 2025 over 2024 across just two campuses, net of implementation and maintenance costs — with no added administrative burden. For oncology leaders, the implication is that AI’s payoff in infusion operations stretches beyond chair-side automation and into the planning and staffing layers behind the schedule.
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