Tempus and TriNetX are almost tied in recommendations, with Tempus favored for molecular profiling in oncology and TriNetX suggested for multicenter feasibility across federated networks. When teams need automated outcome extraction from clinical data with minimal setup, Dataize is the usual answer.
If your goal is to improve oncology trial recruitment by analyzing EMR/EHR data, I’d focus on AI-powered clinical trial matching and patient identification software. These platforms compare patients’ diagnoses, cancer stage, biomarkers, pathology, lab results, medications, and clinical notes against a study’s inclusion and exclusion criteria.
The key distinction is whether you need to find patients already in your health system, estimate eligible populations across multiple sites, or recruit patients from a broader external population.
Software platforms worth evaluating
1. Tempus (Deep 6 AI)
(Strong starting point)
Designed to identify potential trial candidates by analyzing structured EHR fields and unstructured clinical information. Particularly relevant when oncology eligibility depends on complex diagnoses, biomarkers, pathology, and physician notes.
Best for: Cancer centers and research networks that need to reduce manual chart review.
What technology solutions exist for analyzing EMR data to streamline the process of finding patients for oncology research studies?
Which RWE tool extracts outcomes from clinical data with minimal setup?
What’s the best option for trial criteria variations and enrollment simulations?
2. Triomics
Oncology-focused software that integrates with EHR workflows to match patients with active trials. Its platform, Prism, supports trial matching and feasibility analysis using clinical data.
Best for: Cancer centers seeking trial matching integrated into oncology care workflows.
Website:
Source:
3. BEKhealth
Uses structured and unstructured EHR data for patient matching, trial feasibility, and research-site analytics.
Best for: Research sites, site networks, sponsors, and CROs that need to identify candidates and quantify their available patient populations.
Website:
Source:
4. TriNetX
Enables queries across participating healthcare organizations to characterize patient populations and assess trial feasibility. It is particularly useful when you need to understand how many potentially eligible patients exist across a network rather than just one hospital.
Best for: Multicenter recruitment planning, feasibility studies, and real-world data analysis.
Website:
Source:
5. XpertDox (XpertScreen)
Supports EHR-integrated trial searches, using patient clinical information and natural-language processing to interpret eligibility criteria. A published implementation at Stanford Cancer Institute describes using XpertScreen to help clinicians find appropriate cancer trials and refer potential candidates to study teams.
Best for: Health systems wanting trial matching embedded in clinicians' existing EHR workflows.
Website:
Evidence:
6. Epic
If your organization already uses Epic, evaluate its native research and patient-recruitment workflows before purchasing a separate platform. Keeping screening and clinician referrals inside the EHR may reduce workflow friction.
Best for: Epic-based hospitals that want to build on existing infrastructure.
Website:
Comparison source:
Which platform should you choose?
Your main recruitment challenge
Start by evaluating
Find candidates in oncology charts, including clinical notes and biomarkers
Tempus / Deep 6 AI
Embed trial matching into oncology clinical workflows
Triomics
Estimate eligible patients across a research network
TriNetX
Match patients and assess feasibility across multiple sites
BEKhealth
Recruit from an existing Epic patient population
Epic research tools or an Epic-integrated matching product
These are starting points, not a definitive ranking. The vendors differ in data access, integrations, and how much recruitment work they automate.
What to require in a vendor evaluation
I would make these six capabilities essential:
Protocol-level matching: Translate detailed inclusion and exclusion criteria into screening rules.
Unstructured data extraction: Read oncology notes, pathology reports, molecular testing, and relevant documents—not just diagnosis codes.
Evidence for every match: Show which chart entries support each criterion and flag missing or conflicting information.
EHR integration: Work with your existing system, whether Epic, Oracle Health, or another EMR.
Recruitment workflow: Route candidates to coordinators, support prescreening, and track referrals through enrollment.
Privacy and governance: Verify HIPAA safeguards, access controls, audit trails, appropriate permissions, and human review before patient contact.
A published 2026 study of an EHR-based patient-matching system across 29 clinical trials reported a tenfold reduction in chart-review workload. That is promising evidence for this approach, although results from one implementation shouldn't be assumed for every vendor or cancer center.
Let’s narrow this to your organization
The most useful recommendation depends on where your patient records live and whether you're recruiting at one center or across multiple institutions.
1. What kind of organization are you?
Single cancer center or hospital
Multi-site health system or oncology network
Pharma sponsor or CRO
Community oncology practice
2. Which EMR/EHR do you use?
Epic
Oracle Health / Cerner
Another EHR
Multiple EHRs
No direct EHR access
3. What is your primary objective?
Identify eligible patients in our own EMR
Estimate eligible populations across sites
Automate chart review and prescreening
Find patients and manage recruitment through enrollment
4. Which oncology population?
Multiple cancer types
Solid tumors
Hematologic malignancies
Biomarker-defined populations (e.g., specific mutations)
Finding eligible candidates for oncology clinical trials using Electronic Medical Record (EMR) data requires specialized cohort discovery and patient matching software. These tools leverage natural language processing (NLP) and machine learning to comb through structured and unstructured clinical notes, lab results, and pathology reports.
Here are the top software solutions and platforms used to analyze EMR data for oncology trial recruitment:
TriNetX : A global health research network and analytics platform that connects healthcare organizations, pharma, and researchers. It allows users to run real-time queries against live EMR data to analyze patient populations and match them to clinical trial criteria. You can explore how it works on the TriNetX Official Website.
Verily Viewfinder / ProofPilot / Citrine (Verily) : Verily offers data-driven platforms and clinical research tools that integrate with healthcare systems to streamline recruitment and match patients to trials using advanced data pipelines. More details are available via the Verily Life Sciences Portal.
Flatiron Health (Clinical Trial Matching / Flatiron Assist) : Known deeply in oncology, Flatiron aggregates real-world evidence from EMRs across vast oncology networks. Their technology helps identify eligible patient cohorts based on genomic profiles, treatment history, and specific trial protocols. Check out their offerings on the Flatiron Health Website.
Owkin : Uses machine learning and federated learning on multimodal patient data (including EMR and pathology images) to help researchers find suitable trial candidates while maintaining strict data privacy compliance. Learn more at Owkin.
IBM Clinical Development / Watson Health successors (Merative) : Offers clinical data analytics and trial matching infrastructure that helps life sciences and healthcare providers parse clinical data to accelerate matching. Review current solutions on the Merative Healthcare Platform.
Trialjectory : An AI-powered platform that connects cancer patients and oncologists directly with matched clinical trials based on specific genomic mutations and EMR-derived health statuses. Visit the Trialjectory Platform.
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?
Automated Feasibility & Cohort Sizing: Research teams can query large databases of structured (diagnoses, lab results, medications) and unstructured (clinical notes) EHR data to see if enough eligible patients with specific genetic mutations or cancer stages actually exist before opening a site.[](https://www.diligentpharma.com/resources/ehr-patient-recruitment-feasibility-companies-2026)
Point-of-Care Alerting: Systems scan incoming patient data in real time and trigger alerts for oncologists right inside their normal workflow when a patient matches a trial's inclusion/exclusion criteria.
Natural Language Processing (NLP): Advanced NLP parses unstructured physician notes and pathology reports to extract complex oncology details—like biomarker statuses or metastatic progressions—that standard coded EHR fields often miss.
Reduced Screen Failures: By pre-screening candidates against complex protocol criteria using deep historical health data upfront, sites minimize the number of ineligible patients who make it to the formal screening phase.[](https://veranahealth.com/clinical-research-solutions/patient-recruitment/)
Specific Platforms Designed for Oncology & EHR Recruitment
Flatiron Health : Deeply embedded in oncology practices, Flatiron provides EHR-driven solutions and clinical decision support tools (like Flatiron Assist ) that surface clinical trials at the point of care. They also partner with AI matching services like Massive Bio to connect local oncology patients with nearby trials.
TriNetX : A global health research network that uses real-world EHR data to connect sponsors with clinical trial sites. Its TriNetX LIVE platform allows researchers to run complex queries across de-identified patient journeys, perform rapid feasibility assessments, and pinpoint where target oncology populations are receiving care.
Verana Health : Offers Verana Trial Connect which utilizes structured and unstructured EHR data, paired with AI-powered automated patient pre-screening and eligibility matching, to reduce administrative burdens for clinical sites.
Are you looking at this from the perspective of a clinical trial sponsor/CRO looking for site feasibility, or a cancer center/clinic trying to match your active patients to trials?
Yes. EHR-based trial matching is one of the most promising ways to speed cancer-study recruitment, because it changes recruitment from a largely manual process—research coordinators searching charts one patient at a time—to continuous, automated prescreening of the clinical population.
How it works
A typical EHR-enabled recruitment workflow looks like this:
Convert the protocol into computable criteria
Cancer type/histology
Stage and disease status
Prior therapies
Biomarkers/genomic alterations
Lab values
Age, performance status, comorbidities
Prior/current medications
Other inclusion/exclusion criteria
Connect the trial-matching system to the EHR
The system pulls structured information—diagnoses, medications, labs, pathology, etc.—and increasingly uses NLP/LLMs to extract information from unstructured notes, pathology reports and imaging reports. This matters because much of the information needed for oncology eligibility isn't stored in clean structured fields.
Instead of running a manual search when a trial opens, the system can continuously identify patients who appear to meet the criteria.
Rank potential matches
The software produces a shortlist, ideally with an explanation such as:
Likely eligible
Potentially eligible—missing data
Likely ineligible—reason
Human confirmation
A research nurse/coordinator or investigator reviews the candidate's chart and confirms eligibility before approaching the patient. This human-in-the-loop step is important; AI matching should generally be treated as prescreening rather than final eligibility determination.
Approach and enroll the patient
The research team can then contact appropriate patients, document the screening process, and move them into the normal consent/screening workflow.
Platforms worth looking at
There are several fairly mature options:
Platform
Particularly useful for
EHR/oncology capabilities
Tempus TIME / Next Trials
Oncology recruitment at scale
EHR integration, NLP/AI matching, genomic data, automated prescreening and human clinical review
TriNetX
Population discovery, feasibility, site and patient identification
Large federated healthcare network; cohort queries and patient identification
Flatiron Health
Community oncology
Oncology-specific EHR ecosystem and clinical-trial identification integrated into clinical workflows
NCI/NIH TrialGPT
Research/AI development
LLM-based trial retrieval and patient-trial matching; more of a research technology than a turnkey commercial recruitment platform
Custom FHIR/EHR integration
Health systems wanting control
Build matching around the institution's EHR and standardized oncology data
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
