AI-Powered Patient Matching: Why It’s Essential for Modern Clinical Trials

When it comes to clinical trial recruitment, patient matching remains deceptively complex. On the surface, it sounds simple—find patients whose medical history fits the study’s eligibility criteria. But in practice, it’s anything but straightforward.

Today, sponsors and research sites face two accelerating challenges: the explosion of available patient data and the growing complexity of trial protocols. Identifying the right patients quickly, accurately, and at scale has never been harder—or more critical. That’s why it’s time to rethink what AI-powered patient matching really demands in modern clinical research.

What Patient Matching Really Requires

At its core, patient matching is about aligning a trial’s inclusion and exclusion criteria with a patient’s health data—structured data (like diagnosis codes) and unstructured data (like clinical notes or imaging reports).

Effective AI-powered patient matching systems must:

• Ingest diverse patient data from EHRs and clinical systems

• Normalize that data into a usable structure

• Interpret complex protocol logic across dozens of variables

• Output candidate matches that are current, accurate, and actionable

It sounds straightforward—but two realities consistently undermine the process: data complexity and protocol complexity.

Challenge 1: Mountains of Disorganized Patient Data

Most healthcare organizations are sitting on oceans of patient data—yet much of it isn’t ready for trial matching.

• Structured data (ICD codes, lab results) is easy to extract but often lacks context.

• Unstructured data (clinical notes, pathology reports) holds richer insights but is messy and hard to analyze at scale.

In fact, studies estimate that up to 80% of healthcare data is unstructured. Further complicating the landscape:

• Data is scattered across disparate systems

• Notes often contain outdated or conflicting information

• Diagnoses may be implied rather than explicitly stated

Without AI-powered patient matching that can parse narrative context, most of this valuable patient information remains inaccessible—slowing down clinical trial recruitment.

Challenge 2: Protocols Are Growing More Complex

Traditional recruitment approaches are increasingly inadequate for modern studies (Tufts CSDD).

Modern trials often require:

• Narrow biomarker-defined eligibility windows

• Exclusions based on social determinants of health

• Adaptive trial designs with dynamic eligibility changes

Take a breast cancer trial, for example: it might require postmenopausal women with HER2-negative, hormone receptor-positive metastatic breast cancer who have failed prior systemic therapy, have a measurable lesion within the last 45 days, and no cardiovascular events in the past six months.

Manually finding such highly specific patients through basic EHR filters or manual review is virtually impossible at scale—especially under the time pressures of clinical trial recruitment.

The Limits of Traditional Recruitment Tools

Many sites and sponsors still rely on:

• Manual chart reviews

• EHR keyword searches

• Basic spreadsheet tracking

These outdated methods might work for small trials—but they crumble under the demands of:

• Large health systems managing tens of thousands of patients

• Trials with dozens of detailed eligibility criteria

• Urgent timelines requiring rapid enrollment

The result?

• Missed qualified patients
• Wasted screening resources
• Slower clinical trial recruitment timelines

The Case for AI-Powered Patient Matching

AI, especially natural language processing (NLP) and machine learning, offers a fundamentally better path for clinical research.

AI-enabled platforms like BEKplatform offer:

• Reading and interpreting unstructured clinical data at scale

• Understanding clinical nuance (“denies stroke” = stroke-free)

• Mapping complex eligibility criteria across massive datasets

The use of AI in clinical research is accelerating as trials become more data-driven. AI-powered patient matching unlocks insights that were previously hidden—and dramatically accelerates recruitment timelines.

Without AI, There’s No Practical Path Forward

Patient matching is no longer just a step in the recruitment process—it’s the foundation of trial success. Without an AI solution designed for data complexity and protocol nuance, future clinical trial recruitment efforts will struggle to succeed.

Platforms like BEKhealth offer sponsors and research sites a way to:

• Accelerate first-patient-in timelines

• Reduce screen failure rates

• Broaden trial access for overlooked patient populations

As clinical research becomes more personalized, data-intensive, and time-sensitive, relying on traditional methods simply won’t cut it.
AI patient matching isn’t a luxury—it’s a necessity for advancing trials and delivering therapies to the patients who need them most.