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Katie Goodman, RN, BSN, discusses the ways in which artificial intelligence can help speed up the pace of clinical trial accrual
Clinical trials are a critical component of driving innovation in oncologic care, but there are certain recruitment flaws that inhibit their efficiency. In a presentation delivered during the inaugural MiBA Community Summit,1 Katie Goodman, RN, BSN, vice president of clinical research at the American Oncology Network in Fort Myers, Florida, provided a snapshot of how new development pathways seek to encourage the acceleration of studies across oncology.
“It’s very easy in the course of a busy clinic for both research staff and physicians to go about their day managing patients and can forget or overlook the trials that they have available for their patients. It’s sometimes easier to go the standard-of-care route,” Goodman said. “By using plug-ins and tools inside the electronic medical record [EMR] workflow, it really helps the clinician and the staff remain aware of trial options that exist.”
In the interview, Goodman discussed the challenges that exist in clinical development and more efficient ways of accelerating research in oncology.
Goodman: The real challenge in incorporating AI in community-based research is change—having the research staff and the teams that run the day-to-day research understand, adopt, and use these platforms that exist, and see the advantage to how they can augment the work that we already do.
AI is really going to help us all understand the patients and how they’re managed today. In the research process, much of the work that happens to understand the patients and whether you’re going to participate in a clinical trial relies on recall, so questions get asked. [With AI], we’re going to have data-driven decisions around whether we can make this trial work in our practice.
[With MiBA Targeted Intelligence for Precision Support (TIPS)]2 a patient comes in, you can see this MiBA pill right there in front of you on the EMR to remind you that this trial exists in your practice, and hopefully with that reminder, conversations begin earlier with the patients and ultimately result in that patient understanding their options and making a decision to participate in a trial.
When you implement technology, you have to make sure that you have an understanding [of it], [because] behind every piece of technology is people who are managing it. You want to make sure that the folks who are flagging those patients are doing so appropriately—not over flagging too many patients—so that we become sort of desensitized or numb to the alerts that exist there. We want to make sure that teams are always using it when it really makes sense to and not when it’s not the point of time when you really need to have awareness about that trial.
You learn by using it. You learn by understanding how those different levers that you pull, or filters that you set, surface patients. I’m a real proponent of just getting in there and using it [before] we talk about it, but if you’re hesitant to adopt technology, then it really is a difficult hurdle to overcome.
We do a lot of group meetings. We try and pair up people who are big adopters of technology with those who aren’t. [We may] not get everybody on board all the time, but I do think that those who are adopting that tool are going to be more successful.
The MiBA trials platform and demo [showed] how effective it is in finding unstructured data. We talk a lot about the [huge] funnel of patients [we start out with] when we set filters on clinical data looking for a clinical trial patient. There are a lot of patients who will surface initially, but as you start to pull in some of the unstructured data or understand how that patient maybe was previously treated, or what their true stage is, using the unstructured fields, you can really funnel [that] down to a more reasonable list of patients for whom you can then take the next steps to talk through [options with]. I hope that during the demo, folks [realized] how powerful that is.
In research, we [sometimes] just take a guess about which studies we’re going to open. There’s a process called feasibility that we all go through every time we go to open a new trial, and that feasibility has questionnaires in it about the types of patients you have in your practice. It’s been guesswork, largely, [but] I see data-driven decisions [help us] so that we’re only opening studies where we’re going to be successful in enrolling patients. The easiest win here is around feasibility, and then trial matching is a big one that we hope [will become easier as well]. There’s so much more, but those are the top two.
We have been trying to get funding by way of our study budgets, so [we’re] building in technology fees. [The hope is that this will help] pay for that access for us to use this tool to help enroll [patients on trials]. So, we’ll see.
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