Infusion centers face the ongoing challenge of balancing patient demand, staff workload, and operational efficiency. At the recent Transform Infusion Center Operations Virtual Summit, Marisa Quinn, DNP, MBA, RN, NEA-BC, Director of Nursing, Infusion Services & Cancer Acute Care at UCSF Health, shared her team’s journey implementing LeanTaaS’ iQueue for Infusion Centers to enhance patient assignments and workload equity. We sat down with Dr. Quinn to learn more about how UCSF piloted AI-supported patient assignment, tailored training for charge nurses, and worked through integration and adoption challenges to improve equity and efficiency.
Question: What challenges led UCSF Health to explore AI-supported patient assignment in infusion centers?
Dr. Marisa Quinn: At UCSF, we see over 100,000 infusion visits annually. In some of our busiest sites, charge nurses used to spend significant time manually creating patient assignments—only to have those plans disrupted by delays in labs or patient arrivals. To ease this burden, we shifted to a self-signup model where nurses chose patients as they became ready. While this gave nurses autonomy, it created downstream issues such as inequitable workloads and patients occasionally waiting too long to be seated. We needed a system that preserved autonomy but added guardrails for fairness, visibility, and efficiency. That’s what led us to implement iQueue for Infusion Centers’ Patient Assignment feature.
Q: How did you approach the implementation?
MQ: We started small with a pilot in one unit. Early success required strong IT support and frequent check-ins with LeanTaaS to address frontline challenges quickly. We tailored training for charge nurses, identified super users, and adapted workflows to local nuances. Equally important was setting up clear escalation processes for technical issues so charge nurses could focus on patients while leaders worked on solutions behind the scenes. A critical milestone was achieving two-way integration between our electronic health record, scheduling system, and iQueue using the Workforce Integration feature. This eliminated redundant work and made adoption far smoother.
Q: What impact did the solution have on staff satisfaction and operations?
MQ: After three months, we resurveyed staff and saw a 9% improvement in workload balance and an 8% improvement in productivity. Seventy-five percent of nurses reported better pacing of assignments. Charge nurses especially valued the new visibility – no more running around to track workloads manually. Importantly, the system also supported nurse well-being by protecting break times and ensuring equitable distribution of patients. That balance has been key to trust and adoption.
Q: What changes did you see in daily decision-making and team collaboration?
MQ: The real-time visibility has been transformative. Our infusion centers are spread across locations, and now teams can see each other’s volumes and collaborate more effectively. We use the tool to balance resources when one site is busier than another, including after-hours shifts. This flexibility has improved productivity while supporting staff and patient experience.
Q: How did you gain trust and overcome resistance to AI?
MQ: Trust came from transparency and emphasizing that AI is a support tool, not a replacement for clinical judgment. Nurses retained ultimate decision-making authority, with the system providing recommendations that could be accepted or overridden. We also focused on workload equity as the driver, highlighting fairness, patient experience, and staff well-being. Being open about both wins and early defects helped us build credibility and adoption across units.
Q: What lessons would you share with other leaders considering AI-enabled workforce tools?
MQ: Invest in frontline digital literacy before implementation. Early on, some frustration came from not fully understanding how AI-driven tools work. If I could go back, I’d spend more time upfront educating staff about what the system does, how it makes recommendations, and how their feedback shapes development. Positioning it as an opportunity for staff to co-create a solution is critical. Strong IT partnerships, rapid troubleshooting, and respecting the clinical voice at the bedside also make all the difference.
Q: Looking ahead, what excites you about AI in workforce planning?
MQ: I’m excited by the potential to deepen collaboration across sites and to bring frontline nurses into the design of these tools. AI should amplify, not replace, clinical judgment. And when implemented thoughtfully, it helps nurses focus more on patient care and less on administrative burden. For me, that’s the real value.
You can watch Dr. Quinn’s full session and other insights from healthcare leaders who presented at Transform Infusion Center Operations Virtual Summit (September 17, 2025) on demand! Click here to watch “Staffing Synergy: UCSF Health’s Journey in AI-Driven Infusion Workforce Management,” and click here to access all of the sessions from Transform.
