Artificial intelligence (AI)–based point-of-care tools are uniquely positioned to alleviate the burdens of information overload, decision fatigue, and administrative tasks that many oncologists—particularly those practicing in community settings—face, giving them more time and energy to prioritize patient interactions and deliver personalized care, according to Douglas Flora, MD, LSSBB, and Renee Pearl, MSN, OCN.1
“I’m an AI advocate,” Flora said in a presentation at the inaugural
“As a nurse myself, and having worked in oncology for over 20 years, it’s been amazing to watch as treatments have advanced for our patients, [and] now we’re able to put technology in front of providers,” Pearl added in her portion of the presentation.
In the talk, which was intermixed with real-time audience polls, Flora emphasized how these tools can directly benefit oncologists based on the challenges and needs they face every day in clinical practice. He then passed the mic to Pearl, who introduced MiBA Targeted Intelligence for Precision Support (TIPS), which is an example of a clinical decision support system that has made its way into real-world clinical practice.
Flora is the executive medical director of the Yung Family Cancer Center at St Elizabeth Healthcare in Edgewood, Kentucky.
Pearl holds the position of Clinical Data Delivery at MiBA.
AI Clinical Decision Support Systems: Key Takeaways
- Oncologists face burdens from information overload and administrative tasks, leading to long working hours spent trying to keep up with the “tsunami of information.”
- Clinical decision support mechanisms are intended to function as “copilots” that augment, rather than supplant, the oncologist’s role, helping reduce cognitive strain, check complex treatment orders, and provide real-time suggestions based on current patient data.
- Tools like MiBA TIPS represent practical point-of-care educational models designed to integrate into existing EMR workflows to consolidate relevant, personalized patient information to enhance precision and improve practice efficiencies, especially in community oncology settings.
What Are Clinical Decision Support Systems? How Might They Factor Into Day-to-Day Practice?
Flora began by acknowledging the hesitancy that many oncologists may feel about using AI tools in their practices, juxtaposed with the unique challenges embedded in practice today that these technologies could combat.
“It either feels like it’s a science fiction novel, which is disconcerting, or it feels like we’re losing our autonomy,” he said of the growing AI boom.
However, Flora referenced the “tsunami of information” that oncologists wade through each day and the resulting overwhelm he and his colleagues face when trying to juggle all the variables and determine which tasks can be delegated, which often leads to increased working hours. For instance, findings from a survey published in JCO Oncology Advances, showed that in 2023, oncologists (n = 328) worked a mean of 60.0 hours per week (SD, 15.0) compared with 57.6 hours per week (SD, 20.8) in 2013 (n = 1117; P = .02).2
“We’re desperate for help, and we’re turning everywhere we can to nurse practitioners and social workers…to try to help us get through our day so we can do things that only we should be able to do: a change in a treatment regimen, interpretation of a hard CT scan, end-of-life discussions; important, daunting things that we cannot hand off to someone else,” he emphasized, likening the role of the oncologist to that of an airplane pilot.1
The pilot is ultimately the one in control of maneuvering and landing the plane, he reminded the audience. They make all the final decisions and have the necessary training and expertise to navigate through each flight. However, Flora remarked that pilots could benefit from a copilot: someone or something to serve as an extra set of eyes and offer suggestions in dynamic care situations.
“This copilot is there to unload some of that cognitive strain and stress we’re feeling, to replace the feeling we all have of…not being as adequate as we should be, not keeping up with every single trial,” he explained. “Think of this clinical decision support mechanism as the copilot.”
Flora emphasized that clinical decision support is not designed to supplant the role of oncologists, but rather, to work as another member of the care team. For instance, an oncologist might use technology embedded into the electronic medical record (EMR) and other platforms they use daily that can assess the details of a patient visit in real time and suggest next steps that are tailored to that patient’s unique characteristics, such as proposing a clinical trial the patient is eligible for based on the mutational status of their disease, or questioning a dosing regimen based on the patient’s laboratory reports.
“This point-of-care clinical decision support has great power for us to take away the fact that we have to be perfect almost all the time,” he noted. “They can pop up in real time and help you stay where you need to be, which is focused on landing the plane and not [being] the only source of recall to keep that patient safe. This is not to replace us. This is to augment us. This is something we all probably would love to have, if we can trust it [and] it doesn’t interrupt our workforce.”
What Are the Biggest Challenges Oncologists Face When Trying to Keep Up With Advances in Care? How Might Point-of-Care Education Models Help?
Results from an audience poll revealed that most oncologists’ struggles on the job come not from a lack of resources—which they have in overabundance—but in how they can most efficiently and effectively integrate those resources into their clinical practices. Here is where point-of-care education—delivering updated, personalized information during the day-to-day workflow—becomes a priority, Flora stated. He reported that oncologists on all levels are facing the difficulties of integrating the latest clinical trial data into daily practice.
“I strive, you strive, to have the best decision-making with the best information available,” he shared. “I’d love to have more confidence when I’m making decisions. I feel inadequate almost all the time. It’s not because I don’t study. It’s because things are coming at me so fast that I don’t have time to sit with them.”
He highlighted the importance of precision in clinical practice, explaining that technology is advancing in ways that can help oncologists be more precise and thereby reduce errors without needing to memorize all the data that are entering the field at record speed.
How Often Do Oncologists Access Educational Resources During Patient Encounters? How Might AI Tools Augment This Process?
Flora’s next audience poll showed that many oncologists make good use of the technology they already have at their fingertips when meeting with individual patients. He argued that the logical evolution of this process is point-of-care models that save oncologists time and reduce mental load by crawling online databases and publications to make real-time clinical suggestions based on relevant data.
“At first I was bruised, and I thought: I should have known that [information that was suggested to me], I used to know that when I was a younger attending,” Flora contextualized about his own experience using these tools in his practice. “[However], I’ve got 50 other responsibilities, so I love having somebody check my work, just like I’m appreciative of my PharmDs, who are doing the same every day when they call me on my chemotherapy orders and ask: Do you still want to do a dose reduction again, or do you still want to give the full dose, because you dose reduced them last week? I love having that backup in my clinic.”
He noted that these tools could be especially useful in community oncology settings, where generalists are responsible for keeping tabs on several different oncology fields at once.
“We are responsible for having the most possible information before we make decisions,” Flora stated. “AI-powered alerts…that’s decision support. That’s a nudge theory. We can do that on computers to say: Are you sure this is really what you want to do?”
What Is the MiBA TIPS Point-of-Care Education Model?
Pearl then took to the podium to introduce TIPS—a novel tool developed by MiBA to address some of the challenges oncologists face in practice. She highlighted that this platform was developed in collaboration with the MiBA advisory board, which comprises community oncologists who were vocal about the types of services that would provide the most benefit to them on the job.
“We want to make sure we provide the education that you as a physician need at the moment you’re taking care of that patient,” she noted. “There [are] so much data coming our way. How can we possibly keep up with it, especially in community oncology, where perhaps you’re not able to specialize in just one disease state?”
She acknowledged the need for timely, relevant assistance that can be easily integrated into existing workflows without interrupting interactions between oncologists and patients, noting how TIPS can be embedded into the EMRs that oncology practices already use. She also highlighted that TIPS can be used across oncology care teams, from medical oncologists to financial counselors.
Central to the TIPS system is the MiBA Pill, a platform that provides a comprehensive set of materials for individual patient cases, including treatment plans, previously administered treatments, practice-specific value-based care contracts, personalized testing information, notifications about changing laboratory reports, and links to prescribing information for relevant drugs. Pearl emphasized that having all this information in 1 place can reduce time spent clicking through tabs and searching for resources, therefore giving oncologists more time to spend with the patients themselves.
Pearl described how TIPS can also be integrated with tools used by clinical trial research coordinators, allowing for notifications about patient trial eligibility directly in the MiBA Pill. These notifications are coupled with summaries of the trial enrollment criteria, as well as links to the ClinicalTrials.gov sites, giving oncologists an array of information all in one place to help them decide next steps.
“My goal as a nurse is to make sure the data you’re seeing is relevant, and overall, to make sure that patient is getting the appropriate care at the right time, making a difference for that patient and in the meantime, also improving your practice’s efficiencies,” Pearl summarized.
She noted that the TIPS platform is meant to be dynamic based on real-world feedback from oncologists who use it in their practices. As the technology evolves, information about which tools are helpful and which are irrelevant to specific patient circumstances can help TIPS grow into a resource that is as useful as possible for individual oncology practices and ultimately advances patient care.
“That’s what we’re passionate about,” Pearl concluded. “Being in this field for such a long time, it is exciting to see the advances in technology, to be part of it. Let’s use AI in a way that matters for our patients.”
References
- Flora, D, Pearl R. POC education: AI-driven insights and point of care education solutions for guideline driven treatment adherence. Presented at: MiBA Community Summit; September 27-28, 2025; Scottsdale, Arizona.
- Schenkel C, Levit LA, Kirkwood K, et al. State of professional well-being, satisfaction, and career plans among US oncologists in 2023. JCO Oncol Adv. Published online January 29, 2025. doi:10.1200/OA.24.00010