With clinical oncology trials put on hold during the COVID-19 pandemic, researchers turned to troves of data to find patients across the country who would qualify for trials, even if they weren’t physically there.
Artificial intelligence enabled this process, and may have created a move toward decentralized trials that potentially could last long after the pandemic is over.
Jeff Elton is CEO of ConcertAI, which works with some of the biggest oncology pharmaceutical companies and research organizations. Healthcare IT News interviewed Elton to get his thoughts on this shift and what it means for both treatments and patient outcomes.
Q: With trials on hold, researchers have been working with all of this data to find patients who would qualify for trials, even if they are not physically there. How did artificial intelligence technology enable this?
A: By putting the data in cancer centers to work. We process structured and unstructured data – combing through EHRs as well as other sources of patient information that EHRs might not include. Natural language processors and other tools integral to workflows are critical here.
The clinical settings have mountains of data. When participation in trials plunged, they had to quickly and efficiently leverage all the data at their fingertips to find as many potential eligible patients. People working manually would have taken too long and might overlook something. AI has been able to do it. AI enhances the ability to identify patients eligible for clinical studies.
It’s a complex process. We need to eliminate false negatives, meaning that if a patient is potentially eligible for a clinical trial, we identify them. We also make sure that we don’t have too many false positives. Otherwise, we just create work.
We also use AI tools to ensure we are seeing what we expect and need in clinical setting data – exception and anomaly detection and reporting tools are key to identifying and understanding the correct data.
It is critical to understand that if there is no data there is no AI. Meaningful AI and machine learning capabilities require broad data access, the ability to prepare data for specific AI methods and tools, and reserved data for independent validation. Of course, we also must be vigilant of underlying health and biological trends for retraining or re-specification of AI models.
We can also generate evidence from complementary data from retrospective sources for prospective studies – and sometimes retrospective data alone for label expansions.
Increasingly, the FDA is accepting studies with retrospective data provided in replacement for forward-recruited patients in standard-of-care controls as “external control arms.” This shift is in the best interest of patients and allows a more efficient study execution, since patients can be recruited exclusively to the treatment arm with the novel therapeutic.
Q: Has AI sparked a move toward decentralized clinical trials, a move that potentially could stick around long after the pandemic is over?
A: We are not going backwards. Decentralized trials have been emerging over the past several years. COVID-19 was the tipping event, or shock, that accelerated the trend.
Decentralized trials do not require AI at all, incidentally, but can leverage AI given that workflows are all digital and most data is machine readable. We will enter a period where decentralized trials are at scale, coexisting with legacy approaches.
But that will only exist for an interim period – eventually digital only – with deeply embedded AI … the only approach. I use the term “integrated digital trials” to describe what’s ahead.
With integrated digital trials, clinical studies are integral to the care process itself, versus being imposed on it. Trials don’t need to place a higher burden on providers and patients than the standard of care.
This point is incredibly important. Reducing the burden that trials put on patients and providers allows us to move clinical trials into the community where 80% of patients receive their care. It is both the democratization and ubiquity of clinical trials.
Q: What does this shift mean for both treatments and patient outcomes?
A: All of this is good. It’s good for patients, first and foremost, because they can participate in trials in a broader array of treatment settings. It’s good for treatment innovation, because more study alternatives are available in more settings with lower barriers to participation.
Standard-of-care treatment for novel therapeutics versus a separate clinical trial should increase the likelihood of a positive clinical outcome. We want to bring more potentially beneficial options to patients, faster and with greater precision.
Q: Please share an anecdote of your work this past year with pharma companies and research organizations about how AI has improved or enhanced oncology clinical trials.
A: One of our partners had a study that was unable to accrue patients. The trial sponsor wanted our tools, clinical sites and data to solve their problem. We did, but the problem turned out to be a trial design that was inexecutable. Our AI-optimized study design solution found the problem. It was not the insight that was expected, but it was nonetheless valuable.
Of greater significance, we and our sponsor partners in the past year have affirmed our commitment to eliminating the research disparities that sometimes underlie health and other inequities.
We have successfully brought together our combination of rich clinical data and AI optimizations to reconsider clinical trial designs to ensure diversity, avoid unintentional exclusions, and identify sites and investigators that can assure study success and timeliness for completion.