How Predictive Scheduling Improves Oncology Infusion Center Workflows and Patient Wait Times

In this interview with Pharmacy Times, Donna Berizzi, DNP, RN, OCN, NEA-BC, associate chief nursing officer of the Cancer Service Line at Johns Hopkins Medicine, discusses how greater predictability can improve oncology infusion center operations. Berizzi explains that distributing patient volume appropriately allows nursing, pharmacy, provider, and scheduling teams to align staffing, treatment readiness, drug preparation, and chair capacity. Advance patient outreach and clinical verification can also identify medications that may be prepared safely before arrival, reducing delays while limiting unnecessary drug waste. At Johns Hopkins, implementation of LeanTaaS iQueue helped reduce average drug wait time by 46%, from 57 minutes to 31 minutes in 2026.

Pharmacy Times: How does greater predictability in daily patient volumes improve coordination among nursing, pharmacy, and clinic teams?

Donna Berizzi, DNP, RN, OCN, NEA-BC: This is particularly pertinent given the current health care environment. Being able to predict what a day, week, or holiday schedule will look like affects the entire team. When patient volumes are appropriately distributed throughout the day, nursing, pharmacy, clinical, and scheduling teams can work together to ensure that patients are well cared for and do not experience unreasonable wait times.

For nursing, greater predictability allows us to better align our staffing models and skill mix. For example, you do not need all your nurses arriving at 7 am if the day begins with a slower infusion schedule. Based on your LeanTaaS iQueue data, you may have a more complicated regimen scheduled for midday or new nurses whom you want to expose to certain treatment protocols. Scheduling patients appropriately within specific templates allows you to review those templates and determine the best times for nurses to arrive.

Two patients occupying infusion chairs simultaneously may require very different levels of nursing oversight, so it is important not to view scheduling solely as a numbers game. We may place all our short infusion visits and shorter appointments into a separate template. The iQueue data and templates help us identify patients who can arrive, be seen quickly, and leave promptly without creating bottlenecks within the infusion space.

For pharmacy, knowing the expected demand is incredibly important. Identifying patient readiness earlier—which is why verification programs and advanced clinical planning are so important—allows us to determine which patients’ medications can be prepared safely in advance so that they are not waiting for treatment. This can improve workflow while reducing unnecessary preparation and potential waste.

We prepare a large volume of medications in advance. We contact patients 1 week before they are scheduled and again 72 to 48 hours before they arrive. In some cases, if a question arises during an earlier patient contact, we may call the patient the day before treatment. If everything checks out, the patient’s medications can be prepared before arrival. Even with these practices, less than 1% of all medications are wasted. However, Danielle, there are certain medications that we never prepare in advance because they are too expensive to risk preparing before the patient arrives. We are very careful.

Predictability also supports coordination within the clinic. When appointments, laboratory results, clinician visits, treatment readiness, pharmacy preparation, and infusion capacity are better synchronized, we can reduce delays at the beginning of the day that might otherwise create a cascading effect throughout the rest of the day.

There are certain circumstances that we will never be able to predict. If the laboratory goes down or a patient-clinician appointment is delayed because of something beyond our control, delays can still occur. However, we are at least approaching the situation from a coordinated, big-picture perspective involving the multidisciplinary team.

The fourth important consideration is the workforce component. Last-minute changes, staffing adjustments, and unpredictable surges during the day contribute to nursing stress. A more predictable workday means that nurses worry less about what their day will look like and can be better prepared.

We are accustomed to last-minute patient additions for urgent and emergent visits. A patient may arrive unexpectedly, see a clinician, and be able to receive treatment that day. We will always accommodate those patients, but maintaining a more predictable workflow is especially important during peak hours. Every patient wants to be treated between 10 am and 2 pm; that is simply the way it goes.

When you consider the people scheduling these visits, they are schedulers who want to accommodate the patient in front of them and may not feel comfortable pushing back on a requested time. By anticipating demand and explaining to patients why we schedule the way we do, we can help everyone—especially patients—understand why we cannot always schedule an 11 am visit every time they come in.

At Johns Hopkins, implementing LeanTaaS iQueue helped reduce the average medication wait time by 46%, from 57 minutes before implementation to 31 minutes in 2026. That may not sound like a significant difference, but it can be very meaningful for patients who want to arrive and be seen promptly and who already spend a substantial portion of their day receiving cancer treatment.

Predictability is not about making every day identical. We know that patient volumes have peaks and valleys from Monday through Friday. It is about ensuring that the entire care team has enough advance visibility to prepare for the day they are actually going to have—not the day they think they are going to have.

If I need to put out an SOS for additional staff on a day with very high template utilization, perhaps because Friday is the July 4 holiday, I can do so with forethought. This reduces unnecessary stress and delays and creates a more coordinated experience for patients. That is why predictability is so important.