Can AI Find the Right Clinical Trial Patients
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Identifying patients who may benefit from a clinical trial can require physicians to piece together information scattered across electronic medical records, including biomarkers, prior treatments, disease characteristics, and clinical notes. Shaalan Beg and Eron Kelly of ConcertAI describe an AI digital patient object trials approach designed to bring those scattered data points together into a single, usable view.
How This AI Digital Patient Object Trials Approach Extracts Unstructured Data
For oncology practices, finding the right patients for a clinical trial can involve more than searching structured fields in an electronic medical record. Physicians may need to determine a patient’s biomarker status, previous lines of therapy, and other clinical characteristics, much of which can be contained in unstructured clinical notes. “It’s only the development in technology because of these LLMs and SLMs that we’ve developed internally that are allowing us to extract that information and tell the physician at the point of care that this is a person who meets these criteria,” Beg said.
A HER2 Example of How This Works in Practice
Using a HER2 example, Beg described how the technology can identify patients who have a particular biomarker status, prior treatment history, and other characteristics that could make them eligible for a specific therapy or clinical trial, looking beyond structured record fields to extract relevant information directly from clinical notes.
Surfacing Care Gaps Through AI Digital Patient Object Trials Technology
The technology can also help surface potential care gaps. By synthesizing information on biomarkers, treatment history, and other patient characteristics, the system can identify areas where additional testing or treatment options may need to be considered.
When Level-One Evidence Falls Short
A second application involves patients for whom clinical trial evidence may not provide a clear treatment path. Beg noted that real-world patients do not always resemble the populations enrolled in clinical trials, leaving physicians with situations where there may be limited level-one evidence to guide treatment decisions.
The Cohort Matching Tool Within This AI Digital Patient Object Trials Framework
For these patients, Beg described a cohort matching tool that allows physicians within the CancerLinQ network to identify other patients with similar disease and treatment characteristics, then examine which treatments those patients received and how they fared, including whether treatments used in comparable patients were categorized as NCCN category 1 recommendations.
The Digital Object Concept Behind Both Applications
Kelly described the technology underpinning these applications as a digital representation of a patient’s characteristics, the same representation used both to identify comparable patient cohorts and to evaluate whether a patient meets the inclusion and exclusion criteria for a clinical trial. “The underpinning there is we’re effectively creating a digital object that represents that patient’s characteristics,” Kelly said.
Connecting Sponsors and Sites Through This AI Digital Patient Object Trials Platform
The same technology can also connect clinical trial sponsors and sites. According to Kelly, using a common technology platform across both sides could make communication and collaboration more efficient, while helping sponsors identify patients who are appropriate for their trials.
A Dual Benefit for Patients and Sponsors
For clinical trials, that alignment could serve two purposes: helping patients access potentially appropriate care while also helping sponsors enroll the patients needed to generate meaningful trial evidence, a framing that positions this technology as benefiting both sides of the clinical trial ecosystem simultaneously.
What This AI Digital Patient Object Trials Approach Means Going Forward
Given the emphasis on creating a reusable digital representation of each patient that serves multiple functions, from trial eligibility screening to care gap identification to cohort comparison, this technology approach could reduce redundant data extraction work across different clinical and research use cases within the same health system. Health systems and clinical trial sponsors evaluating similar AI tools may want to consider whether a unified patient data representation, rather than separate point solutions for each function, offers a more efficient long-term architecture.
What to Watch Going Forward
As ConcertAI and similar vendors continue developing these unstructured data extraction and cohort matching capabilities, industry observers will likely watch whether shared sponsor-site technology platforms meaningfully improve trial enrollment efficiency in practice, beyond the conceptual benefits described here. Given the broader industry interest in AI-driven trial matching tools, this AI digital patient object trials approach may offer a useful reference point for how oncology practices and clinical researchers evaluate competing platforms addressing the same fundamental challenge of surfacing eligible patients from complex, unstructured medical records.
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