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Addressing staff shortage in infusion centers: how AI can help

  • Ashley Joseph
    Ashley Joseph

    Vice President, Client Services - Infusion Centers at LeanTaaS

Louis Pasteur said, “Chance favors the prepared mind.” In highly specialized and unpredictable environments like infusion centers, which often struggle to retain nurses and staff for the long term, it often seems impossible to adequately prepare. 

Scheduling in infusion settings is highly complex, wait times are long, and nurses are usually rushed. Clinician burnout and job dissatisfaction are systemic, an issue that both feeds and is fed by an overall shortage of nurses and other infusion staff. As a result, patient experience and care quality can become suboptimal. COVID-19, which adds critical new concerns to patient and staff safety as well as logistical challenges, has turned this ongoing problem into an urgent crisis. 

Yet new technologies make it possible to reduce uncertainties, adapt to change, and fuel a new level of preparedness that supports staff and patients by enabling centers to function efficiently.  

A typical chaotic day for infusion center staff

The person who reminded me of Pasteur’s quote is the director of nursing in a top-tier cancer center. In 2019, even before the added stressors of COVID, she and a colleague began to overhaul operations in the center’s infusion clinics. Nurses routinely began the day independently with no visibility beyond their own schedule and thus no ability to balance patient loads or prepare for early patient arrivals. Days quickly spiraled out of control. Even as the administrators and their team worked diligently to overhaul existing processes and better prepare for appointments, arguments continued over daily schedules. 

By mid-morning everything was chaos. Days extended well beyond the scheduled close. Nurses missed both lunch breaks at work and family dinners at home. Clinics paid out significant overtime. Patient wait times often exceeded an hour. Nobody won. This pattern was not unique and is still commonly experienced by infusion centers across the country.

Nurse and infusion staff shortage and COVID: no end in sight

Two years after this conversation, the chaos has only increased. COVID-19 created an unprecedented strain on hospitals and infusion clinics, and nurses bore the brunt of this burden. Many nurses, after experiencing high levels of burnout or furloughs, retired early. There is now a severe nurse shortage, and it’s estimated that 1.2 million RNs will be needed to cover the gap by 2030. Some infusion centers can afford to contract travel nurses or sponsor overseas nurses. Facilities who cannot afford these options struggle with recruiting and retention.  Pressure on nurses and staff, already afraid their exhaustion and distraction will cause negative outcomes for patients, has intensified. 

Given the circumstance of a broader infusion staff shortage, it’s more important than ever to manage and predict appointments and staffing needs to avoid the “midday chaos”, missed lunches, and excessive overtime that leads to burnout and dissatisfaction. To do this, infusion centers need more real-time insights and analytics to effectively delegate available staffing budgets and operate with staff on hand. Intelligent solutions are available now. 

What intelligent infusion staffing looks like, even for limited staff 

Artificial intelligence (AI) is transforming operations — from scheduling appointments to facility utilization to staff allocation — with the desired effects of happier patients and staff. Applying AI to infusion centers is a natural choice. These types of platforms can connect multiple dependencies with precision, even in the face of changing variables, and provide insights and predictions. For example, an AI tool can intake real-world infusion center data and then predict the volume and duration mix of appointments on any given day of the week and account for the number of likely cancellations, no-shows or add-ons. AI helps clinic personnel make crucial operational decisions based on its own data and has done wonders to level-load patient scheduling. 

AI-based platforms also help leaders fully utilize and support their personnel with optimal nurse allocation. These systems “read” the recommended patient schedule, examine who is scheduled to work, and account for acuity. Intelligent allocation of patients to nurses can ensure that the nursing workload at the start and end of each appointment is fully dedicated to a single patient, while the nurse’s workload during appointments can be directed across a small handful of patients that are seated near each other. 

This system optimizes the number of nurses available without overwhelming them. It can direct planning at multiple “time horizons”. Appointments made weeks in advance will not be allowed to exceed available staff capacity for those times, nurse allocation for patients can be level-loaded for upcoming days, and templates for today show where add-ons can be accommodated. 

The impact of AI-optimized infusion staffing 

AI-based solutions for infusion centers are not hypothetical. LeanTaaS has created the market leader, iQueue for Infusion Centers. Many of the leading cancer centers in the United States have deployed this product very successfully over the past few years. Feedback across the board has been a reduction in overtime, an increase in nurse satisfaction, a leveling of workload — across the day and across staff — and a decrease in staff turnover. All are factors that alleviate infusion staff shortage. 

The colleagues I mentioned above instituted iQueue for Infusion Centers at their clinics and are thriving. The nurses became much happier and have clear rules for making tradeoffs that no longer negatively affect the schedule or patient care. Nursing assignments were capped daily to prevent staff from getting overwhelmed. Wait times fell from a high of over an hour to less than 30 minutes.

This same solution continues to show results for other cancer and infusion centers, before COVID and throughout the pandemic. UCHealth Cancer Center saw a 28% reduction in nurse overtime hours, and improved confidence and wellbeing overall as nurses and other staff could now take regular breaks and feel focused and rested. The Huntsman Cancer Institute (University of Utah) was able to consistently take unexpected add-on patients with existing staff while avoiding going over capacity. “Our days run much smoother because we are really utilizing our time better,” one oncology nurse says, describing how staff can make the best decisions to accommodate add-ons and take patients as soon as they arrive. 

The power of the AI engine, when fueled by the facility’s historical and real-time data, was also reflected at a 60-chair Stanford Health Care infusion center. The solution already did the hard math of accounting for the number of infusion chairs, operational constraints, and the number of staff actually available. Staff could create schedules that were optimally and appropriately level-loaded. In this environment, the percentile of nurse satisfaction improved by an entire 25%.


No technology can deliver a perfect solution to every challenge facing an infusion center, where staff turnover is often high and circumstances change every day, especially in a pandemic, but AI-based systems like iQueue for Infusion Centers can learn based on what happened before. Predictive analytics are constantly improving, and as a result, each day becomes significantly closer to the standardized ideal, effectively preparing them for whatever chance throws their way. In a time when an infusion staff shortage is a critical concern, this intelligent preparation and support is key to maintaining the continuity of safe and effective patient care.

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