Patient Recruitment Reinvented: How AI is the Key to Accelerating Clinical Trials

Modern clinical trials face a recruitment challenge. More than 80 percent of clinical trials conducted in the United States miss enrollment deadlines, contributing to delays in therapeutic development, higher trial costs, and slower patient access to innovative treatments. Recruitment inefficiencies remain one of the most resource- and time-intensive aspects of the clinical trial process. Despite increasing access to real-world data (RWD), traditional recruiting methods have not evolved quickly enough to capitalize on these new sources of information.

To advance clinical research, the industry must rethink how it identifies eligible participants and deploys its recruitment strategies.

Structured data alone cannot ignore critical clinical signals

Most recruitment efforts rely heavily on structured data fields such as claims, laboratory values, and ICD codes to identify potential participants. While this approach provides consistency and ease of querying, it often fails to capture the complexity of a patient’s condition or the nuanced criteria required by modern protocols. As a result, many potentially eligible individuals are missed, particularly when eligibility depends on indicators that are not typically coded, such as functional status, response to treatment, or progression captured by imaging.

These neglected patients are frequently documented in unstructured portions of the electronic health record (EHR). This includes free-text doctor’s notes, radiology reports, pathology narratives, and other clinically rich documents. By focusing solely on structured data, recruitment teams risk bypassing a large subset of patients who might qualify for a trial based on their clinical history, but whose eligibility is not reflected in the coded fields.

Unstructured EHR data holds untapped potential

The majority of clinically relevant information contained in an EHR is unstructured. These text fields capture a physician’s impressions, reasoning, and context that often do not clearly correspond to drop-down menus or check boxes. For example, disease progression may be noted as an “increase in lesion size” in a CT scan interpretation, or a physician may describe a patient as “not responding to initial treatment.” These types of information are essential for trial inclusion, but are not captured by standard coding systems.

Unstructured EHR data provides a more holistic view of the patient journey. However, accessing it at scale has always been a barrier. Advances in artificial intelligence (AI) and natural language processing (NLP) are now changing this reality.

How AI-powered tools unlock recruiting insights

Modern NLP platforms trained in clinical language can analyze unstructured text and extract key data points relevant to trial eligibility. These tools use rules-based models, machine learning classifiers, and terminology mapping to identify mentions of specific symptoms, disease stages, biomarker results, or responses to prior therapies. Unlike keyword searches, these systems can interpret context and signal when a clinical term indicates progression, severity, or treatment failure.

For example, instead of relying on a diagnosis code for a condition such as geographic atrophy (GA), AI tools can analyze ophthalmology notes for references to visual acuity decline, lesion characteristics, or treatment plans. These data points can then be combined with structured data from the EHR to create a more complete patient profile.

To ensure this information is accurate, successful implementations combine AI models with expert clinical validation. This process often involves training algorithms on annotated datasets, regularly reviewing reported terms and extracted variables, and calibrating the system based on feedback from practicing physicians. Once validated, these models can run across thousands of EHRs, enabling real-time identification of patients who meet complex inclusion and exclusion criteria.

Bringing structure and meaning to the entire EHR

To be effective, AI models must process both structured and unstructured data in a harmonized and standardized format. This includes ingesting EHR data from multiple sources, de-identifying and standardizing formats, and applying retention rules to ensure completeness and quality. Platforms designed for clinical development often incorporate these capabilities, allowing researchers to define eligibility criteria with greater specificity and translate these criteria into research parameters on large and diverse datasets.

The result is a more dynamic, real-time approach to cohort discovery that supports faster feasibility assessments, smarter site selection and earlier patient identification.

Building smarter, more inclusive trials with AI

By leveraging the full depth of the EHR, AI-driven recruiting strategies improve both accuracy and reach. These tools allow sponsors to find patients earlier in their medical journey, identify underrepresented populations and better adapt trial design to real-world conditions. This not only contributes to faster recruitment, but also to better data quality and greater generalizability of trial results.

In an environment where speed, fairness and scientific rigor are imperative, modernizing patient recruitment is no longer a future objective. It is a current necessity.

Real-world data, real-time impact

Artificial intelligence is no longer theoretical in clinical development. He is actively helping to reshape the way trials are designed, launched and executed. By transforming the EHR into a research-ready resource through advanced AI techniques, clinical monitoring, and data standardization, the industry has the opportunity to fundamentally reimagine what is possible in trial recruitment.

Modern testing requires modern infrastructure. Realizing the full value of real-world data starts with understanding where the information is, how to responsibly extract it, and how to convert it into insights that accelerate innovation and improve patient outcomes.

Photo: Andriy Onufriyenko, Getty Images

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