The Impact of AI on Infusion Patient Care and Operational Efficiency

Ashley Joseph, Senior Vice President of Client Services, Infusion Centers, LeanTaaS

Please share your name, title, and brief overview of your professional history.

My name is Ashley Joseph, I am the Senior Vice President of Client Services and Infusion Centers at LeanTaaS. I came to LeanTaaS with over 20 years of service operations experience. I was integral to the early creation and building of the service operations practice at McKinsey & Company, where I served clients in the financial services, insurance, retail, energy, and health care industries. I also owned and operated a chain of service-based franchises for over a decade. I have a bachelor’s degree from Georgia Tech and an MBA from Harvard Business School.

Can you discuss the specific advancements in artificial intelligence (AI) that are revolutionizing the infusion therapy industry?

Infusion centers are under incredible pressure to treat more patients with fewer resources amid ongoing staffing shortages and increasing patient demand. According to LeanTaaS’ State of Cancer Centers 2023 Report, 64% of infusion centers say staffing shortages are still causing challenges, with centers reporting a 15% increase year-over-year in higher nurse-to-patient ratios and a 13% increase year-over-year in longer patient wait times. To streamline operations and meet these challenges, many infusion centers are turning to AI tools for their efficiency and cost-reduction potential.

AI-powered solutions can use historical data to determine patterns of predictability, analyze these patterns to determine a center’s unique volume and mix trends, and subsequently create an optimized scheduling template or share staffing allocation recommendations based on that center’s specific conditions all in a fraction of the time it would take a staff member to do so manually.

Should any variables like patient volume, operating hours, nurse coverage, or chair numbers change unexpectedly throughout the day, AI tools can also adjust those scheduling templates accordingly. With these insights readily available, infusion centers can remain agile, and staff are empowered to make key operational and capacity management decisions wisely and safely. 

How do AI-driven predictive analytics empower health care providers and payers with real-time data and insights for improved care coordination?

Infusion centers are one of the most critical points of care, and they are charged with managing a unique set of operational challenges, such as managing patient appointments that are linked to clinics, labs, and pharmacies, retaining specialized staff, and rising patient demand. The result is often packed schedules, imbalanced workloads, and tough choices about how to treat patients efficiently without sacrificing high-quality care and safety. 

Traditional approaches to care coordination and capacity management, like relying on the EHR or process improvement initiatives, are not equipped to effectively address these unpredictable events, which are more often the rule of daily operations, not the exception. The only way to accommodate patient demand and all needs in this highly constrained environment is to unlock capacity and resources through a combination of AI, automation, and smart operational choices. AI-powered predictive and prescriptive analytics have only recently become a part of daily infusion treatment vernacular, but these tools present an incredible opportunity for infusion centers when it comes to proactively eliminating some of this day-to-day operational chaos. 

For example, process steps like patient assignment and readiness or drug premixing algorithms are critical areas that can benefit from the incorporation of AI tools. These tasks currently require multiple manual steps with intense human focus and have endless possible permutations that are beyond what any human could evaluate. AI-powered solutions, however, can analyze this data quickly and accurately to streamline these operations and save staff valuable time. 

Advanced AI-powered solutions can also give charge nurses access to real-time insight into every dynamic happening in their center, all at their fingertips. These AI-driven recommendations support better-informed decision-making regarding how to shift or allocate patients to nurses and chairs, based on the actual state of the center, instead of what was expected for the day. This type of prediction and prescription is the next horizon in harnessing AI to maximize efficiency and ultimately empower staff in achieving the goal of every infusion center – getting every patient diagnosed with cancer started on the appropriate treatment as quickly as possible.  

How is the regulatory landscape influencing the adoption and use of AI technologies in infusion therapy?

The regulatory landscape around AI is continuously evolving and plays a significant role in informing how the technology is used in day-to-day operations across the health care industry. Patient data is extremely sensitive, so the testing and validation for AI algorithms is often rigorous, and rightfully so. Ensuring AI tools meet all clinical standards is critical in building trust among providers and patients, and helps address equally important ethical concerns. 

However, health care decision-makers must be prepared to take a long-term approach to adopting AI and factor lengthy approval processes into their implementation strategy. After all, the more rigorous the testing process, the longer it takes to move these technologies from development to deployment. Providers must balance the lure of the potential benefits of AI-driven innovation and the subsequent desire to rush implementation with the need to remain compliant with patient safety standards. 

What are some potential concerns or risks associated with the use of AI in infusion therapy, and how can they be mitigated?

With any emerging technology, the implementation of AI does bring with it some concerns, particularly around ethics, transparency, and privacy. Decision makers need to take their time to ensure they choose the right AI solution, as opposed to the first or most exciting, as well as have the proper cybersecurity and compliance checks and balances in place.

Additionally, the implementation of AI technology in infusion therapy alone isn’t enough. No matter how cutting-edge the technology may be, a human touch is an important piece of the care delivery process, and pairing the best AI with the best service is critical. It takes people, processes, and technology working together toward the same goals to achieve real results and sustain meaningful improvements. 

When adopting AI tools, infusion leaders must put an equal emphasis on building a dedicated team of experts who are laser-focused on helping the center drive change management, establish systemwide governance, and deliver customized support.

How do you believe these new technologies may impact overall patient experience? 

Receiving cancer treatment on its own is a stressful and vulnerable process, which is why the patient experience in infusion therapy is critical. Patient wait times need to be minimized, drug wait times need to be reasonable, and disruptions in the daily schedule need to be managed appropriately. 

AI-powered solutions can reliably lower patient wait times by 30%, reduce staff overtime by 50%, and accommodate a 15% increase in patient volume growth, meaning more patients will be able to easily and efficiently access the life-saving treatments they need. When centers utilize AI technology to achieve operational excellence, they are also creating a seamless and smooth experience at every step of the patient care journey.

What are the key insights you hoped to convey during your session at NICA 2024?

Advancements in AI have already begun to transform the way infusion centers are run and deliver care, by empowering providers and promoting seamless care coordination through real-time data-driven insights – even taking today’s complex health care dynamic into account. However, these new technologies aren’t without risk and limitations, and health care decision-makers can’t rely on AI tools alone to improve patient care and eliminate inefficiencies. AI-powered operational excellence requires a thoughtful approach that enhances and supports staff.