Clinical-trial screening contains plenty of work a machine can do faster than a person.
It can scan large patient populations, pull relevant data, compare records with eligibility criteria, and move likely matches toward the top of a review queue.
The harder question is: What happens next?
Patient identification isn’t a single task. Some parts are repetitive and computational. Others require someone to interpret incomplete evidence, resolve ambiguity, and decide whether the record really supports a closer look.
The best use of automation is knowing which is which.
A useful dividing line is whether the task is primarily about finding and organizing evidence or deciding what that evidence means.
Automation is well suited to searching large data sets, extracting relevant facts, flagging possible inclusion or exclusion criteria, and prioritizing records for review. A pilot study of AI-assisted trial matching found that clinicians spent about 43% less time screening when AI assistance was available, while accuracy remained comparable.
That’s meaningful because the value isn’t just speed. Reducing a large pool to a smaller group worth reviewing gives research staff more time for cases that actually require their expertise.
Eligibility rarely behaves like a simple yes-or-no checklist.
A recent review of AI-based patient-trial matching identified recurring challenges involving performance variability, interpretability, generalizability, and limitations in the underlying data.
Those limitations suggest a practical boundary. Automation can surface evidence and explain why a record appears relevant. A clinician should step in when the evidence is incomplete, conflicting, unusually nuanced, or dependent on judgment that can’t be reduced safely to a rule.
That boundary can look different across indications. In rare-disease research, small patient populations and genotypic and phenotypic heterogeneity can add complexity to study design and evaluation.
In cardiology, an EHR-screening initiative for a heart-failure trial found that some eligibility criteria couldn’t be translated cleanly into computable rules, leaving a larger patient pool requiring manual review.
A strong recruitment workflow doesn’t have to choose between manual chart review and an algorithm making the final call.
A more useful model is layered. Let software handle the repetitive work of searching, sorting, extracting, and prioritizing. Give the research team the evidence behind the result, then let qualified professionals evaluate the gray areas and make decisions that require clinical context.
That’s also reflected in Third Opinion’s EHR Database Match platform, which analyzes structured and unstructured data against trial criteria and is designed to reduce manual chart review. Third Opinion also describes human clinical oversight as part of its trial-matching approach.
The question for research teams isn’t simply how much screening can be automated. It’s where human attention changes the quality of the decision.
If a task is repetitive, high-volume, and evidence-driven, automation can carry more of the load. When the answer depends on ambiguity, missing context, or professional judgment, the handoff should be obvious.
The point is to give clinical teams fewer charts to chase and better evidence to review while keeping them where their expertise has the greatest impact.
Third Opinion helps research-focused practices enroll more patients, onboard more studies, and fulfill industry data requests while giving patients the opportunity to participate in potentially life-saving treatments. Founded by physicians, Third Opinion is built for patients and researchers.
Contact Third Opinion for more information.