Unstructured, Not Unusable: How AI Unlocks Hidden Patient Insights in Clinical Research

Unstructured clinical data holds answers—millions of them. Each year, patients generate vast volumes of clinical information through free-text notes, imaging reports, pathology narratives, and more. These records are rich in detail and relevance, yet they’ve remained largely untapped in clinical research. The reason isn’t lack of value, but difficulty in processing.

This blind spot has real consequences, especially for clinical trial recruitment. Today, AI-powered tools are transforming unstructured data into one of the most powerful assets for identifying eligible patients with speed and accuracy.

What Is Unstructured Clinical Data?

Unstructured data refers to information that doesn’t reside in predefined fields within an electronic health record (EHR). Examples include:

• Physician notes

• Radiology and pathology reports

• Patient messages or intake forms

• Social determinants of health

• Discharge summaries

These sources contain context-rich patient details that are often missing from structured formats (read more about the value of unstructured EHR data). This is where nuance lives. With the right technology, that nuance becomes searchable and actionable.

Why It’s So Hard to Use

Unstructured data is challenging not because of what it contains, but because of what it requires. Processing it at scale, while maintaining accuracy and compliance, introduces several key barriers:

Volume and Variability

Unstructured data accounts for up to 80% of all healthcare information. One patient can generate thousands of words in notes each year. Multiply that across patient populations and provider systems, and you get an overwhelming range of formats, terms, and styles.

Inconsistency and Ambiguity

There’s no single way to document a diagnosis. A provider might write “T2DM w/ PN,” while another writes “diabetes with nerve issues.” These differences often cause structured queries to miss key eligibility criteria.

Lack of Searchability

Structured data can be queried directly. Free-text content can’t—unless it’s transformed into a structured format. Until that happens, it remains invisible to most matching algorithms and feasibility tools.

Privacy and Compliance Requirements

Narrative notes often contain scattered sensitive information. Centralizing or analyzing this content requires strict adherence to HIPAA and related regulations, adding complexity to already demanding workflows.

How AI Solves the Problem

Artificial intelligence—particularly natural language processing (NLP) and machine learning (ML)—bridges the gap between raw unstructured content and actionable clinical insight. The FDA has also acknowledged the growing role of AI and ML in clinical development. Here’s how it works:

Data Ingestion and Standardization

AI can scan and collect data from diverse sources, then convert it into a structured format that systems can read and analyze. Tools like the BEKplatform automate this process, handling in hours what manual abstraction would take weeks to accomplish.

Understanding Clinical Language

Modern NLP tools do more than recognize words. They understand clinical context. Whether a provider writes “history of ER+ breast cancer” or “hx of breast CA, ER+,” the system interprets both as the same diagnosis.

Supporting Trial Matching

Once transformed, unstructured data can be used to apply trial logic across entire populations. AI can extract:

• Diagnosis timelines

• Comorbidities

• Medication history and usage patterns

• Lifestyle and behavioral indicators (e.g., smoking status or mobility issues)

This enables researchers to find more eligible patients—including those overlooked by structured data alone.

Built-In Data Protection

AI solutions like BEKhealth’s are designed to operate entirely within secure environments. Data never leaves its source system. The process aligns with HIPAA and other privacy standards, ensuring both safety and scalability.

What AI Finds in Unstructured Data

Many key eligibility criteria live in free-text fields. AI tools can surface:

• Social and behavioral health factors such as housing instability, transportation issues, or substance use

• Detailed comorbidities like insulin dependence or disease severity

• Temporal context, including when diagnoses occurred or medications began

• Adverse event history that might be mentioned only in narrative reports

These details often don’t exist in structured form, but they directly impact trial fit.

The Real-World Impact: Equity and Speed

Unlocking unstructured data drives both operational efficiency and broader inclusion. Many patients from underrepresented groups are missed in traditional searches because of inconsistent documentation. AI closes those gaps by analyzing the complete patient record.

It also accelerates timelines. Trials that leverage AI for unstructured data analysis report faster recruitment, fewer screen failures, and better site targeting.

At one BEKhealth partner site, AI surfaced 40% more eligible patients for a complex oncology trial by analyzing pathology reports—patients structured queries would have missed.

See the Full Picture, Not Just the Structured One

Unstructured data doesn’t have to remain hidden. With AI, it becomes one of the most powerful tools for identifying, engaging, and enrolling the right patients in the right trials.

The future of research depends on a complete view of the patient. That means reading beyond the checkboxes—and learning from every note that tells their story.