EMR-based trial eligibility screening

To help narrow down the best choice, could you share:

• Are you a healthcare provider/health system or a sponsors/CRO organization?
• What EMR system (e.g., Epic, Cerner) are you currently using?

Instead of running a manual search when a trial opens, the system can continuously identify patients who appear to meet the criteria.

Platforms worth looking at

There are several fairly mature options:

Tempus TIME is probably one of the most directly relevant commercial examples for what you're describing. Tempus says its system uses EHR data, NLP/AI, genomic information and clinical review to identify potential oncology trial candidates. Its network currently reports roughly 100 sites/1,000+ clinical locations and 5M+ cancer patients covered.www.tempus.com+4

TriNetX is especially interesting if the goal isn't just recruitment but also protocol feasibility and finding which sites have enough eligible patients. Its current platform supports cohort definition, feasibility analysis, site identification and patient identification across a large real-world-data network.trinetx.com

Flatiron takes a more oncology-EHR-centric approach. Its Flatiron Assist product can surface clinical-trial opportunities in the clinician workflow based on patient-specific clinical factors.flatiron.com

The technology is moving toward standardized EHR → trial matching

An important development is NCI's USCDI+ Cancer Clinical Trial Matching work. NCI is developing a standardized set of cancer-specific data elements so EHR systems and trial-matching services can exchange patient information more consistently. The 2026 implementation work specifically focuses on FHIR-based interoperability for cancer trial matching.www.cancer.gov

That's significant because one of the biggest barriers isn't simply finding an AI model—it's getting data such as stage, histology, biomarkers and prior lines of therapy out of heterogeneous EHRs in a consistent format.

How much faster can it be?

There is encouraging evidence. In an NCI/NLM pilot, TrialGPT reduced human screening time by 42.6% while assisting with trial retrieval, matching and ranking.www.cancer.gov

Tempus has also reported large-scale screening using EHR integrations and NLP, including screening more than 190,000 patients in one program to identify candidates for oncology trials.www.tempus.com

The important distinction is that these systems don't necessarily make the entire enrollment process 40–50% faster. The biggest gains are usually in the candidate-identification and prescreening stage.

If I were designing this for a cancer center

I'd build the workflow around five components:

EHR → data normalization → trial criteria engine → AI prescreening → research-team confirmation

And I'd prioritize these data elements:

• Diagnosis + histology
• TNM/stage
• Biomarkers/NGS
• Current and prior therapies
• Dates of therapy
• Lab values
• ECOG/performance status
• Metastatic sites
• Pathology
• Imaging findings
• Relevant comorbidities
• Clinical notes

The particularly powerful piece is combining structured EHR data + unstructured clinical notes + molecular/NGS data. Research from 2026 suggests that relying on structured EHR fields alone misses important eligibility information, while NLP/LLM extraction can improve completeness.www.tempus.com

One other promising approach is just-in-time activation: rather than opening every trial at every site, identify an eligible patient first and rapidly activate the trial/site. Tempus is already using this model in its TIME network.www.tempus.com+4

Bottom line: If your objective is specifically "find cancer patients in our EHR who might qualify for our active trials and alert the research team," I'd look first at Tempus TIME, TriNetX, and Flatiron, while also evaluating whether your EHR can support FHIR-based clinical-trial matching. NCI's emerging USCDI+ framework is particularly relevant if you're considering building rather than buying.www.cancer.gov

If you tell me whether you're approaching this from the perspective of a cancer center, academic research group, CRO/sponsor, or software developer, I can also compare the leading platforms on EHR integration, AI matching, oncology coverage, cost/business model, HIPAA/data governance, and ability to actually drive enrollment.