Three areas where AI can help understaffed infusion centers

Local infusion centers are jumping in to fill doctors’ infusion orders when their hospitals – such as Sacred Heart and St. Joseph’s in Eau Claire, Wisconsin – preparing to closewhich Hospital Sisters Health System said last month would happen by April 21. They are also crucial services to address the peak in cancer incidence.

However, the shortage of infusion staff still poses a challenge to their operations.

Based on a survey of 100 nursing directors and operations leaders at cancer centers in the US, LeanTaas, a predictive analytics company, said in its reportThe State of Cancer Centers in 2023, states that artificial intelligence could drive the digital transformation of infusion centers in three key areas: ongoing staffing shortages, operational constraints and access to healthcare data.

Infusion center staff working under high demand and shortages need ‘real-time recommendations on how to transfer patients or assign them to nurses and chairs, based on the actual condition of the center, rather than depending on what is happening that day’ was ‘expected’. said Ashley Joseph, vice president of customer service, infusion centers at the company.

Healthcare IT news asked her how AI and data-driven technology can help improve infusion center planning, decision-making and operations.

Q. How can infusion centers improve scheduling and workload so that they are not as often overbooked and experience a rapid increase in appointment requests?

A. Infusion centers are under enormous pressure to treat more patients with fewer resources. Too often, they view busy schedules and difficult choices about the safe and efficient treatment of patients as unexpected events.

However, making these choices is the rule and not the exception.

Figuring out where to schedule last-minute additions, what to do when the number of patients needing treatment exceeds the number of appointments, and/or how to predict the day of cancellations and varying capacity is what makes the difference between a smooth day and a stressful day for patients and staff. Capacity management is also a crucial part of ensuring patient safety.

These unplanned events are simply part of daily operations, and infusion centers can plan and handle these events with the same efficiency and confidence as “routine” scheduling – with the help of AI and predictive analytics.

AI-powered solutions use historical data to determine predictability patterns that the human eye cannot see or process. They also analyze this data to determine a center’s unique volume and mix trends, all of which are used to create optimal scheduling templates that create the right space for all patients needing treatment, including add-ons.

As volumes increase, scheduling templates are adjusted accordingly. It takes advanced data science to sort through the literal stuff google of possible permutations to help a center have the best chance of having a smooth, safe day – every day – for their patients and staff.

The most advanced AI-powered solutions can also adjust templates based on changes in hours, nurse coverage, seat numbers and other variables, helping infusion centers make wise and safe capital and other operational decisions.

Q. How can they improve decision-making, especially when treating patients outside the hospital setting, such as the a la carte services that are expanded when hospitals close?

A. The goal of every cancer center – and every person involved in oncology surgery at any level – is to get every patient diagnosed with cancer started on the right treatment as quickly as possible.

It’s great to have more pathways that increase the likelihood that this is possible for every newly diagnosed or relapsed patient, and it’s great that innovative problem solving is underway in this area. If standalone centers can provide the same quality of care as centers within hospitals, especially in terms of patient safety and appropriate nurse and caregiver support, more patients will receive treatment faster, which is a win for the entire cancer care universe.

That said, these standalone centers must be as focused on strong operations and processes as centers within hospitals, as the patient experience in cancer care is critical. This means that patient wait times must be kept to a minimum, drug wait times must be reasonable, and disruptions to the daily schedule must be managed appropriately.

AI-powered solutions can reliably reduce patient wait times by 30% and reduce staff overtime by 50%, while also enabling a 15% increase in patient volume in infusion centers. When centers adopt technology that delivers operational excellence, it translates into the patient experience – creating a more seamless and smoother care journey, even as care is transferred between hospital and non-hospital providers.

Q. If 31% of the infusion centers as indicated in the LeanTaas cancer center status report from last year would consider using AI to improve their operations. What is your advice as they continue to expand the service?

A. AI is only now becoming part of the cancer center’s everyday language, but it has been used for years to inform and improve operations. Every other service industry that is also asset intensive (airlines, rideshare, package delivery) is using advanced AI to predict demand patterns at a very fine level of granularity, understanding capacity constraints – you need people, equipment, rooms and supplies to deliver a clinical service – and develops intelligent, (AI/machine learning) based algorithms to dynamically balance supply and demand on a minute-by-minute basis to optimize capacity and maximize throughput.

Infusion centers must also turn to AI-powered solutions to provide lifesaving services to the growing number of patients who need them.

There is an incredible opportunity for infusion centers to rely on AI-powered solutions when it comes to day-to-day operations. For example, process steps such as patient readiness, patient assignment, and drug premixing algorithms are all areas that can benefit from the integration of AI tools.

These are all tasks that currently require a multi-step, often human-intensive focus, but also have billions of possible permutations that even the smartest human cannot evaluate simultaneously. Finding ways to tap into AI to create initial options – even if a human is ultimately needed to complete/validate the answer – can create efficiencies for both nurses and infusion center administrators.

Another area of ​​opportunity concerns strengthening the staff of infusion centers.

Advanced AI-powered solutions can give charge nurses real-time access to every dynamic happening in their center at their fingertips. This type of tool can also make real-time recommendations on how to move patients or assign them to nurses and chairs, based on the actual state of the center, rather than depending on what was “expected” for that day. This kind of prediction and prescription is the next horizon in leveraging AI to maximize the efficiency of a center’s physical and human resources.

Finally, my advice is to pair the best AI with the best service.

Throwing software over the wall yields no results. People, processes and technology are needed to achieve and sustain meaningful improvements. Pairing AI and automation with a dedicated team of experts focused on helping the center drive change management, establish system-wide governance and deliver customized support is key to continued success.

Andrea Fox is editor-in-chief of Healthcare IT News.
Email: afox@himss.org
Healthcare IT News is a HIMSS Media publication.