Oncology providers are increasingly turning to data-driven strategies to manage rising patient volumes without expanding physical infrastructure. Johns Hopkins Medicine recently reported a 16% increase in weekday ambulatory oncology volume without adding infusion chairs, operating hours, or clinic days. This result was achieved through artificial intelligence-enabled scheduling, workflow redesign, and advanced clinical verification.
Donna Berizzi, the associate chief nursing officer of the Cancer Service Line, explains that while high volume is often uncontrollable, the variability and unpredictability of that volume can be managed. The institution focuses on moving complex care into the outpatient setting to alleviate pressure on inpatient units.
We are moving autologous and allogeneic transplantation, chimeric antigen receptor (CAR) T-cell therapy, and bispecific T-cell engager (BiTE) therapy into the ambulatory setting. The hospital has created an umbrella of support and resources around these patient populations. If a patient requires urgent admission, a bed is available.
Addressing Nursing Burnout
Survey data indicates that 29% of respondents identified high patient volume as a leading contributor to burnout. However, 40% identified last-minute shifts or adjustments in assignments and patient templates as major stressors. Berizzi notes that when patient demand concentrates during specific hours, the pressure extends across the entire infusion area.
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The system relies heavily on historical data provided by LeanTaaS iQueue to build scheduling templates. The goal is consistent daily operations that eliminate days extending beyond scheduled hours. The data is what helps them to see where the bottlenecks are at.
For the clinical teams on the ground, this shift toward data-driven scheduling offers more than just administrative relief. It restores a sense of agency to the staff, allowing them to manage their workload with foresight rather than constantly reacting to the next crisis. When nurses can anticipate the flow of the day, the environment becomes less chaotic and more focused on patient needs.
Berizzi emphasizes that AI does not make decisions or replace nursing judgment. It is not a rigid scheduling system, nor is it a staffing reduction tool. Instead, it helps teams prepare by forecasting demand, identifying bottlenecks, and creating more predictable workflows.
Expansion Without New Construction
The report highlights that 65% of cancer centers are planning to expand services, while 61% identified patient flow and scheduling as their greatest operational challenges. Berizzi argues that growth cannot rely solely on adding more space, chairs, or staff. Leaders must examine how care is delivered and how resources are shared across the service line.
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One strategy involved separating laboratory appointments from clinician visits. If a patient completes lab testing the day before treatment, the team can review results and verify readiness in advance. When the patient arrives the next day, the wait time is negligible.
The institution also evaluates patient locations to determine where to offer specific programs. A local hospital in Washington, DC, is approximately 40 miles away, but traffic can make the trip difficult. The center began increasing the availability of certain regimens in that area.
For the past 18 months, the hospital has treated patients undergoing autologous transplantation in the ambulatory setting at Sibley Memorial Hospital. This change eliminated the need for patients to spend 1.5 hours traveling on the Beltway for appointments.
This approach reinforces the importance of treating patients where they live. At the end of the day, they can return home to sleep in their own beds. Efficiency cannot come at the expense of the workforce, so treatment complexity and workload must be evaluated collectively.
