Reimagining patient recruitment: How artificial intelligence is key to accelerating clinical trials

Modern clinical trials face enrollment challenges. More than 80% of clinical trials conducted in the United States fail to meet recruitment timelines, resulting in delayed treatment development, higher trial costs, and slower patient access to innovative treatments. Inefficient enrollment remains one of the most resource-intensive and time-consuming aspects of the clinical trial process. Despite increasing access to real-world data (RWD), traditional recruiting methods are not evolving fast enough to take advantage of these new information sources.

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

Structured data alone misses critical clinical signals

Most recruitment efforts rely heavily on structured data fields such as claims, lab 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 patient health conditions or the nuanced standards required by modern protocols. As a result, many potentially eligible individuals are missed, especially when eligibility depends on metrics that are often not coded, such as functional status, treatment response, or progression captured by imaging.

These neglected patients are often documented in unstructured portions of electronic health records (EHRs). This includes free-text physician notes, radiology reports, pathology narratives, and other rich clinical documentation. By focusing only on structured data, recruitment teams may be bypassing a large proportion of patients who are eligible for the trial based on their clinical history, but whose eligibility is not reflected in the coded fields.

EHR unstructured data has untapped potential

Most clinically relevant information in EHRs is unstructured. These text-based fields capture physicians' impressions, reasoning, and context that often don't map neatly to drop-down menus or checkboxes. For example, in scan interpretation, disease progression might be labeled as “lesions increasing in size,” or a doctor might describe a patient as “not responding to initial treatment.” These types of insights are critical for trial inclusion, but cannot be captured by standard coding systems.

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

How AI tools unlock recruiting insights

Modern NLP platforms trained on clinical language can analyze unstructured text and extract key data points related to trial eligibility. These tools use rule-based models, machine learning classifiers, and term mapping to identify mentions of specific symptoms, disease stages, biomarker results, or responses to prior treatments. Unlike keyword searches, these systems can interpret context and flag when clinical terms indicate progression, severity, or treatment failure.

For example, AI tools can scan ophthalmology notes for references on vision loss, lesion characteristics, or treatment plans, rather than relying on diagnostic codes for conditions such as geographic atrophy (GA). These data points can then be combined with structured EHR data to create a more complete patient profile.

To ensure the accuracy of these insights, successful implementations combine AI models with expert clinical validation. This process typically involves training the algorithm on an annotated dataset, regularly checking labeled terms and extracted variables, and calibrating the system based on input from practicing physicians. Once validated, these models can be run across thousands of electronic medical records, enabling the identification of patients meeting complex inclusion and exclusion criteria in real time.

Bring structure and meaning to the entire EHR

To be effective, AI models must process structured and unstructured data in a unified and standardized format. This includes ingesting EHR data from multiple sources, de-identifying and normalizing formats, and applying governance rules to ensure integrity and quality. Platforms designed for clinical development often integrate these capabilities, allowing researchers to more specifically define eligibility criteria and translate these criteria into search parameters across large, diverse data sets.

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

Leveraging AI to build smarter, more inclusive trials

By leveraging the depth of electronic medical records, AI-driven recruiting strategies improve accuracy and coverage. These tools enable sponsors to detect patients earlier in the disease course, identify underrepresented populations, and better match trial design to real-world conditions. This will not only help speed up enrollment, but also improve data quality and generalizability of trial results.

In an environment where speed, equity, and scientific rigor are all critical, modernizing patient recruitment is no longer a goal of the future. This is a current necessity.

Real-world data, real-time impact

Artificial intelligence is no longer theoretical in clinical development. It is actively helping to reshape how trials are designed, launched and executed. By transforming EHRs into research-ready resources through advanced artificial intelligence technology, clinical oversight, and data standardization, the industry has the opportunity to fundamentally reimagine what is possible for trial recruitment.

Modern trials require modern infrastructure. Unlocking the full value of real-world data starts with understanding where the information resides, how to responsibly extract it, and how to turn it into insights that accelerate innovation and improve patient outcomes.

Photo: Andriy Onufriyenko, Getty Images

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