Infusion centers are caught between two forces that won’t relent: patient volumes that keep climbing and clinician pipelines that can’t keep pace. For many organizations, the instinct is to hire more nurses or expand physical capacity, but that answer isn’t always available. The more pressing question is whether existing resources are being fully utilized.
During a webinar hosted by Becker’s Healthcare and sponsored by LeanTaaS, Amber Castillo, BSN, RN, regional nurse manager for Texas Oncology’s San Antonio region, and Sharfa Huq, product implementation and customer success manager for infusion at LeanTaaS, shared how predictive analytics are helping Texas Oncology unlock hidden capacity, reduce scheduling friction and improve patient flow across the region’s 10 infusion sites — without adding staff or chairs.
Below are four takeaways from their conversation.
Note: Quotes have been edited lightly for length and clarity.
1. Even during a staffing crisis, greater capacity exists
Texas Oncology’s San Antonio region manages 179 infusion chairs and delivers more than 84,000 infusions annually across 10 sites. When a severe staffing shortage hit, the team faced a problem with no easy answer: patient demand wasn’t going to slow down, and hiring alone wasn’t going to solve it.
By modeling different staffing scenarios and scheduling templates in iQueue for Infusion Centers, the team found that untapped capacity existed within their current resources. By extending hours and adjusting schedules strategically, the region increased infusion volume by 16% and patient hours by 18%.
“Instead of assuming that we were maxed out, we used data to identify where capacity actually existed and then we structured our schedules around it,” Ms. Castillo said. “For us, this was a turning point.”
2. Static reports can’t surface the bottlenecks that slow throughput
Before implementing advanced analytics, Texas Oncology was tracking acuity and staffing needs manually in spreadsheets and eyeballing patient volumes in their EHR. The data existed, but it didn’t reveal where scheduling templates, chair utilization patterns and appointment mix were quietly limiting throughput.
Ms. Huq described the shift: rather than relying on traditional EHR reporting, iQueue provided dynamic, real-time insights into capacity and template performance — surfacing constraints that weren’t visible in standard reports.
3. Aligning schedules to real-world workflows drives measurable improvement
At Texas Oncology’s medical center location, physicians require finalized lab results before beginning treatment — a reasonable clinical standard that was creating downstream delays when scheduling templates didn’t account for actual transition times. Patients were arriving late to the infusion room, stacking arrivals later in the day and disrupting on-time starts.
After analyzing the gap between scheduled and actual appointment timing, the team implemented a one-hour buffer between lab, provider visit and treatment. Within two to three months, on-time starts increased by 6%.
“The nursing team was excited that what was on paper was actually happening in real life, and they could feel the difference in flow almost immediately,” Ms. Castillo said. “This wasn’t about adding resources. It was just about structuring the day and the way that reflected reality, and that made a measurable difference.”
4. Intentional fast-track scheduling reduces wait times without disrupting complex cases
Ms. Castillo described how shorter infusions — port flushes, injections, pump disconnects — were being mixed into the same scheduling flow as longer treatments, creating unnecessary wait times and slowing overall throughput.
The team modeled a fast-track template in iQueue that grouped shorter-duration treatments intentionally. “Rather than mixing appointments and lengths in a way that created variability, we designed a template that allowed fast-track treatments to move through the center more effectively,” Ms. Huq said.
Since launching the model in July 2025, the team reduced average wait time for those patients from 15 minutes to 10 minutes — a 30% decrease.
“Most importantly, this has been sustained since implementation at our medical center,” Ms. Castillo said. “For our patients, that means less waiting time. For our nurses, it means a more predictable flow. And for leadership, it’s an example of how small, intentional scheduling changes can create meaningful operational improvement.”
The bottom line
Texas Oncology’s results in the San Antonio region reflect a broader operational principle: data-driven scheduling changes can generate real capacity gains before an organization ever adds a chair or a nurse.
Ms. Castillo summed up the team’s new orientation: “Sometimes you hear your nurses say ‘we’re super busy’ or ‘we don’t have enough chairs’ — and this allowed us to look at the data and show it’s not that you don’t have enough nurses or chairs, you just have to maximize your infusion room throughout the day.”
For infusion centers facing similar pressures, the path forward may already exist within existing operations.
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