EHR Patient Recruitment Gets A Reality Check

EHR patient recruitment could help clinical trial teams identify eligible participants faster by turning routinely collected health data into recruitment intelligence.

However, the technology still faces significant challenges around workflow integration, data quality, interoperability, and meaningful evaluation. 

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Why EHR Data Could Transform Trial Recruitment

Recruitment remains one of the most persistent operational challenges in clinical development.

Finding patients who meet complex inclusion and exclusion criteria requires significant investigator and research-coordinator time, while manual screening can miss potential participants.

Electronic health records (EHRs) contain a continually updated picture of a patient's disease status, treatments, clinical measurements, and, in some settings, genomic information.

This could make them a potentially valuable source for automated or semi-automated patient-trial matching. 

The technology is generally described as a Clinical Trial Recruitment Support System (CTRSS). These systems analyse patient information against trial eligibility criteria and can identify people who may warrant further assessment.

The concept is not new. Routine clinical data does help support recruitment and reduce administrative work in some studies already, where it identifies additional potential participants beyond manual recruitment and substantially reduces the time required for some data-entry activities. 

The opportunity now is to make these systems more scalable, integrated, and useful within everyday clinical workflows.

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“This is one of the riskiest industries there is because you're dealing with human lives, you're dealing with experimental protocols, and you're dealing with regulatory bodies where you might not get another shot at that clinical trial."

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EHR Supported Clinical Trial Patient Recruitment

A new scoping review examined published evaluations of EHR-based CTRSS between 2013 and 2024.

The researchers screened 927 articles and included 44 evaluations covering different technologies, therapeutic areas, and clinical settings. 

Several findings stand out:

• 75% of systems were trial-centred rather than patient-centred.
• Only 20% provided eligibility information at clinically relevant points in the patient's journey.
• Just 9% actively notified targeted users about potential matches.
• 32% transformed EHR data using standard data models.
• 48% used computer assistance to transform eligibility criteria.
• 55% relied on structured-data queries for patient-trial matching.
• 61% of evaluations were retrospective.
• Only 23% assessed the complete pathway from data transformation through to patient-trial matching. 

These figures reveal an important gap in that having an algorithm that can identify a possible match is not the same as embedding recruitment intelligence into clinical care.

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EHR Patient Recruitment Needs More Than Matching

Clinical workflow, rather than technology, is arguably the most important factor.

Many systems remain designed around the trial itself. They identify potential participants but do not necessarily support the wider collaboration between patients, clinicians, research teams, and study coordinators.

That matters because recruitment is a process, not a single algorithmic event.

A useful system needs to surface relevant information at the right point, to the right person, with enough context to support a decision.

Earlier research into EHR recruitment systems similarly found that standard EHR environments could provide useful query, workflow, reporting, and notification capabilities, but often lacks dedicated structures for managing recruitment and trial information. 

Embedding recruitment intelligence, therefore, rather than another standalone screening application, might be a better way to go with EHR support of patient recruitment.

Data Quality Decides Patient Recruitment Success

Automation cannot compensate for incomplete or poorly structured clinical data.

EHR information is collected primarily to support patient care, not research.

Different systems can record the same clinical concept in different ways. Important information may also sit within unstructured clinical notes rather than standardised fields.

Only 32% of evaluated systems reported transforming EHR data according to standard models, highlighting the continuing importance of interoperability and data harmonisation. 

Eligibility criteria create another challenge. Trial protocols are written for human interpretation, while software requires criteria that can be represented computationally.

Natural language processing, machine learning, structured queries, and other approaches can help bridge that gap. However, the scoping review found that eligibility-criteria transformation was computer-assisted in fewer than half of the evaluated systems.

This means data infrastructure should be part of recruitment strategy.

Proving the Impact of EHR Supported Patient Recruitment 

EHR-based recruitment systems can work but need stronger, more consistent evaluation.

Future systems need to demonstrate whether they improve recruitment rates, reduce delays, lower costs, support clinicians, and improve patient inclusion.

These EHR technology should be assessed across user, technology, and societal dimensions, rather than assessing only whether an algorithm will produce technically accurate matches.

That’s important for sponsors because a highly accurate matching engine has limited value if clinicians ignore its alerts, patients cannot easily act on them, or the underlying data cannot support reliable eligibility decisions.

The strategic opportunity is broader than automated prescreening.

EHR patient recruitment could become part of a connected clinical trial intelligence layer, linking protocol criteria, patient data, investigator workflows, and research operations.

At Pharmatica, we track the technologies reshaping Clinical Development, from digital recruitment to real-world data and AI-enabled trial infrastructure. Our focus is where technology meets operational reality, helping decision-makers understand what is ready to scale, what still needs validation, and where the next gains in Clinical Development may emerge.

Pharmatica: Insight. Connection. Impact.

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