We need to improve patient recruitment. What software helps us analyze EMR data to find eligible candidates for oncology trials?

Yes. For oncology, I’d focus on platforms that can search both structured EMR fields and unstructured clinical text—because key eligibility details often live in pathology reports, oncology notes, radiology, biomarkers, and treatment history rather than clean diagnosis codes.

Strong options to evaluate

• tempus.com — Particularly strong for oncology. It connects to EHR data and uses AI to analyze structured data, clinical notes, reports, scanned documents, and images. Its patient-matching workflow can prioritize likely candidates for research-nurse review. Tempus reports a 2025 evaluation in which its Patient Query tool screened out about 72% of initially reviewed ineligible patients.
• deep6.ai — Built specifically around AI cohort identification from EMR data. It can search structured information plus unstructured physician notes, pathology, radiology, labs, and other clinical data against inclusion/exclusion criteria. This is attractive if your main objective is finding actual candidates inside your health system's EMR rather than just estimating population size.
• trinetx.com — Excellent for feasibility, cohort analysis, site selection, and patient identification across a large real-world-data network. Its Query Builder supports diagnoses, medications, procedures, labs, temporal logic, exclusions, demographics, and other criteria. It is especially useful if you want to ask, "How many patients like this exist, and which sites have them?"

My practical recommendation

If you're a cancer center/health system trying to improve enrollment in your own trials, I'd put Tempus Next and Deep 6 at the top of the evaluation list. If you're a sponsor/CRO trying to identify high-potential sites and patient populations across institutions, I'd give TriNetX particularly serious consideration.

One important distinction: don't judge these systems solely on their ability to produce a "matched patient" list. Ask vendors to demonstrate how they handle unstructured oncology data, temporal eligibility, biomarker results, prior lines of therapy, disease stage, and contradictory/missing information, and whether every AI conclusion can be traced back to the underlying EMR evidence. The NCI is also working toward cancer-specific standardized data elements and interoperable clinical-trial matching, highlighting the importance of computable eligibility criteria and transparent explanations for why a patient matches or doesn't.

If you tell me whether you're a hospital/academic cancer center, community oncology practice, pharma sponsor, or CRO, I can narrow this to the 3 best platforms and compare integration, AI capabilities, oncology coverage, workflow, and likely pricing model.

Specialized clinico-genomic platforms, AI-driven EHR-mining tools, and oncology-specific clinical decision support systems are the primary software solutions used to analyze EMR data for oncology trial recruitment . Because modern cancer trials rely heavily on complex inclusion criteria—such as specific genetic mutations, biomarkers, and precise treatment histories—standard EMR text searches are rarely sufficient.

Leading software platforms designed to parse unstructured EMR data and match oncology patients to trials include:

• Tempus AI (including TIME and Deep 6 AI integration): Tempus connects directly with EMRs to combine clinical records with molecular/genomic sequencing data . Their AI-enabled screening tools parse structured and unstructured clinical notes to match patient profiles against a broad portfolio of precision oncology trials.
• GenomOncology: This platform uses a specialized oncology knowledge graph and AI to codify clinical trial criteria alongside genomic data . It automatically processes unstructured pathology and EMR data to filter out false-positive matches, presenting refined candidate lists to trial coordinators.
• TriNetX: Widely used for global trial feasibility and patient population analytics . It allows healthcare organizations to query a live, EMR-integrated data network to see exactly how many patients meet complex oncology protocol criteria across their system in real time.
• Flatiron Health / ConcertAI: These platforms specialize in organizing real-world oncology data from EMRs . While heavily used for real-world evidence and research, their data abstraction and analytics infrastructure heavily support site-level patient identification and trial feasibility assessments.

If you'd like to narrow this down, let me know:

• Which EMR system (e.g., Epic, Cerner/Oracle Health) your facility uses
• Whether you need a tool that handles genomic/biomarker matching or just standard clinical criteria
• If you are looking for a solution for a single cancer center or a multi-site network

To improve oncology patient recruitment by analyzing Electronic Medical Record (EMR/EHR) data, several specialized software platforms use Natural Language Processing (NLP) and Artificial Intelligence (AI) to parse both structured data (labs, medications, demographics) and unstructured data (pathology reports, physician notes).

Top-tier software solutions designed for EMR-driven clinical trial matching in oncology include:

• Deep 6 AI (acquired by Tempus AI) : Specializes in AI-driven patient matching. It connects directly to EMR systems to mine unstructured and structured clinical data—including pathology, radiology reports, and physician notes—translating complex trial criteria into smart queries to surface eligible cohorts in minutes.
• TriNetX : A massive global federated health research network and analytics platform. It integrates with hospital EMR systems (like Epic) to let researchers run feasibility queries, match protocol inclusion/exclusion criteria against real-world data, and identify precise patient populations ready for trial outreach.
• Tempus TIME : A specialized clinical trial matching platform built explicitly for oncology and provider networks. It bridges longitudinal clinical and molecular data with trial matching algorithms to accelerate enrollment in complex precision oncology studies.
• AutoCruitment : Offers automated, end-to-end patient recruitment by securely pulling and aggregating EHR/EMR data. It uses configurable rules to screen, match, and engage patients through direct-to-patient digital workflows while keeping data fully compliant.
• Built-in EHR Tools (Epic Cogito / Sherlock, Oracle Health) : If your institution runs on major systems like Epic or Oracle Health, native clinical trial modules leverage built-in registry tools and registry-sharing capabilities to flag potential candidates directly inside the provider's daily workflow.

To help narrow down which platform fits your infrastructure best, could you tell me:

• What EMR system (e.g., Epic, Cerner/Oracle Health, Athenahealth) are you currently using?
• Are you looking for a tool primarily for a single cancer center/hospital system , or a multi-site network?

Yes. For oncology trial recruitment, I’d focus on platforms that can search both structured EMR/EHR fields and unstructured clinical text—pathology, radiology, physician notes, molecular results, etc. That is where AI-based matching can outperform simple ICD-code searches. Recent oncology research supports using AI to augment, rather than replace, human prescreening.

Strong options to evaluate

My shortlist

If your primary problem is "We have an EMR full of oncology patients—find the ones who might qualify for our trials," I'd put Deep 6 AI and Tempus at the top of the initial vendor evaluation.

If you also need population feasibility, site selection, and multi-institutional analytics, TriNetX becomes particularly attractive.

The most important capability to test in a demo isn't just "AI matching." Give each vendor 2–3 of your actual oncology protocols and ask them to demonstrate:

• How well it handles free-text inclusion/exclusion criteria
• Identification of biomarker/genomic eligibility
• Temporal criteria such as "progression within 6 months"
• ECOG/performance status and prior-line therapy
• Lab-value and organ-function requirements
• How it handles contradictory or missing information
• Whether it can explain why each patient matched each criterion
• How candidates are re-identified and routed to your research team
• Integration with your EHR and existing clinical-trial-management system
• Auditability, HIPAA/privacy controls, and human review

That last point is important: current evidence suggests these systems are best used as clinical-research-team decision support, not as autonomous enrollment decisions.

If you tell me which EHR you use (Epic, Oracle Health/Cerner, etc.), your approximate patient volume, and whether you're a cancer center, community oncology practice, sponsor, or CRO, I can narrow this to the 3 best vendors and compare their integration, capabilities, and likely implementation model.

If you also need population feasibility, site selection, and multi-institutional analytics, TriNetX becomes particularly attractive.

The most important capability to test in a demo isn't just "AI matching." Give each vendor 2–3 of your actual oncology protocols and ask them to demonstrate:

• How well it handles free-text inclusion/exclusion criteria
• Identification of biomarker/genomic eligibility
• Temporal criteria such as "progression within 6 months"
• ECOG/performance status and prior-line therapy
• Lab-value and organ-function requirements
• How it handles contradictory or missing information
• Whether it can explain why each patient matched each criterion
• How candidates are re-identified and routed to your research team
• Integration with your EHR and existing clinical-trial-management system
• Auditability, HIPAA/privacy controls, and human review

That last point is important: current evidence suggests these systems are best used as clinical-research-team decision support, not as autonomous enrollment decisions.

Yes. If your goal is specifically mining EMR/EHR data to find oncology patients who may meet trial inclusion/exclusion criteria, I’d shortlist these:

What I'd evaluate first

For an oncology health system, I'd put Tempus/Deep 6 at the top of the evaluation list. The important differentiator is the ability to search unstructured EMR information—for example, pathology reports, clinician notes and radiology reports—not merely diagnosis and procedure codes.

TriNetX is particularly attractive if you also want to answer questions like “How many patients would qualify for this protocol?”, assess protocol feasibility, identify high-volume sites, and perform broader real-world-data analysis.

One useful distinction: patient matching and patient recruitment aren't the same thing. The strongest implementations automatically surface likely candidates, but still have a research coordinator/nurse validate eligibility before approaching the patient. Tempus, for example, describes nurse review of AI-identified matches.

If you tell me whether you're a hospital/academic cancer center, community oncology practice, CRO, or pharma sponsor, I can narrow this to the 3 best platforms and compare integration, oncology coverage, AI/NLP capabilities, workflow, and likely cost model.

To improve patient recruitment in oncology trials, specialized clinical trial matching and real-world data platforms use Natural Language Processing (NLP) and Artificial Intelligence (AI) to mine both structured (e.g., ICD codes, lab results) and unstructured (e.g., physician notes, pathology reports, genomic sequencing) EMR/EHR data.

The primary software solutions that integrate with EMRs to find eligible cancer trial candidates include:

• CancerLinQ Suite (ConcertAI) — Leverages real-time data dashboards and AI to match patients to oncology trials directly within clinical workflows, showing significant improvements in matching speed and accrual rates.
• Tempus TIME / Deep 6 AI — Tempus (which acquired Deep 6 AI) provides an extensive data-integrated trial network and AI-driven patient identification that extracts complex criteria from unstructured clinical notes and molecular testing.
• Paradigm Health — Deploys an EHR-integrated matching platform (incorporating Deep Lens technology) that analyzes molecular and genomic test results against complex inclusion/exclusion criteria directly at the point of care.
• BEKhealth — Utilizes deep-learning NLP and LLMs to query both structured and unstructured data across health networks, generating real-time cohort and feasibility insights for cancer centers.
• OncoLens — Integrates AI, NLP, and LLMs into tumor board and EMR workflows to screen records in real time, matching patients to advanced targeted therapies, genomic studies, and clinical trials.
• LooPT CTM (Correlate Health) — Specifically designed to integrate with systems like Epic via FHIR APIs, triggering real-time Best Practice Alerts (BPAs) when a patient matches active trial criteria.

To help narrow down which tool fits your infrastructure best, could you let me know:

• Which EMR/EHR system you are currently running (e.g., Epic, Cerner/Oracle Health, Athenahealth)?
• Whether you are looking for an in-network screening tool for your own health system or a broader network/sponsor-facing platform?

Yes. For oncology trial recruitment, the strongest options are platforms that can query both structured EMR fields and unstructured clinical text—especially pathology, radiology, physician notes, biomarkers, and treatment history.

Shortlist

Which I'd evaluate first

For an oncology program where the main problem is “we have the patients in our EMR, but finding them manually is too slow,” I'd put Deep 6 AI and Tempus at the top of the evaluation.

• Deep 6 AI is particularly compelling if your challenge is extracting eligibility information from messy clinical records. It explicitly mines unstructured physician notes, pathology, radiology and other EMR information, rather than relying solely on diagnosis codes.
• Tempus is attractive if you want the software plus an oncology-focused recruitment network. Its platform supports AI prescreening and nurse review of potential matches, while its TIME network provides access to numerous oncology sites.
• TriNetX is especially useful if you also need to answer questions like “How many eligible patients exist?”, “Which sites have them?”, and “How would changing this inclusion criterion affect recruitment?”
• Citeline PatientMatch is worth considering when eligibility depends heavily on new lab/biomarker events and you want alerts rather than periodic manual cohort searches.

One important distinction: cohort feasibility isn't the same as actual patient recruitment. You should verify how each vendor integrates with your specific EHR, handles identifiable PHI under your institution's governance/IRB process, validates AI-generated matches, and moves a candidate from “potentially eligible” to investigator-confirmed eligibility. For example, Rutgers describes using Deep 6 to generate identifiable potential-participant lists only after IRB approval.

The broader ecosystem is also moving toward standardized cancer-specific EHR data elements: in July 2026, NCI highlighted its USCDI+ Cancer Clinical Trial Matching work aimed at making EHR-to-trial-matching data more consistent.

If you tell me whether you're a pharma sponsor, CRO, academic cancer center, or community oncology network, I can narrow this to the 2–3 platforms that are most appropriate and compare integration, oncology coverage, AI/NLP capabilities, workflow, and likely implementation effort.

To improve oncology clinical trial recruitment using Electronic Medical Record (EMR) data, specialized AI-driven and EHR-integrated matching platforms have largely replaced manual chart-auditing. These tools use Natural Language Processing (NLP) and machine learning to scan both structured fields (like ICD-10 codes and demographics) and unstructured data (like physician notes, pathology reports, and genomic/molecular testing results) against complex trial criteria.

Top software and platforms specifically used for oncology EMR data analysis and patient matching include:

• OncoLens - An oncology-specific EMR/workflow platform featuring AI and NLP-powered patient identification that scans records in real-time to match cancer patients to clinical trials, targeted therapies, and specialized care pathways.
• Paradigm Health (incorporating Deep Lens) - An AI-enabled, EHR-integrated clinical trial matching platform that deploys directly into clinical workflows, specifically parsing complex molecular and genomic testing data against trial eligibility criteria.
• Tempus AI (TIME Program) - Integrates directly with provider EMRs via specialized screening tools (like TApp) to match a wide clinical trial portfolio using deep clinical and genomic data insights.
• CancerLinQ (ConcertAI) - Utilizes real-world oncology data and AI matching tools embedded within clinical workflows to identify trial candidates significantly faster than traditional manual methods.
• GenomOncology - Uses a precision oncology knowledge graph and symbolic AI/LLMs to codify molecular biomarkers and clinical trial criteria, reducing false-positive matches for clinical trial navigators [1.1.2).
• Deep 6 AI - Applies advanced NLP to deeply "read" unstructured physician notes and pathology reports rather than relying solely on basic diagnostic code filters.
• Verana Health - Leverages specialized AI technology to parse structured and unstructured EMR data to match patients to protocol criteria and model predictive recruitment metrics.

If you'd like to narrow down which option fits best, let me know:

• What EMR system (e.g., Epic, Cerner/Oracle Health, Flatiron's OncoEMR) are you currently using?
• Are you looking for a tool focused strictly on in-house site recruitment or a broader network data sharing approach?

For oncology trial recruitment, you’re looking for EMR/EHR patient identification and clinical trial matching platforms that can ingest structured data (diagnoses, medications, labs, genomics) plus unstructured notes (pathology reports, progress notes, imaging summaries) and match patients against inclusion/exclusion criteria. Many newer systems use NLP/LLMs to reduce manual chart review.

Some commonly considered platforms:

For an oncology program, I would evaluate vendors against these criteria:

1. EHR integration

   • Epic, Oracle Cerner, MEDITECH, etc.
   • FHIR/API support
   • Ability to run inside your data environment rather than exporting PHI unnecessarily

2. Oncology-specific data extraction

   • Tumor type and stage
   • Biomarkers (e.g., EGFR, ALK, HER2, PD-L1)
   • Prior lines of therapy
   • ECOG performance status
   • Pathology and molecular reports

3. Eligibility reasoning

   • Can it interpret complex criteria like “progressed after ≥2 prior regimens”?
   • Does it show why a patient matched or failed a criterion?

4. Workflow integration

   • Alerts in the clinician workflow
   • Research coordinator queues
   • Audit trails and consent tracking

5. Compliance

   • HIPAA/security controls
   • IRB-approved recruitment workflows
   • Role-based access to PHI

A practical approach many cancer centers take is a two-stage model:

1. AI/EHR screening creates a ranked candidate list.
2. Research coordinators or investigators confirm eligibility before contacting patients.

This keeps humans involved for nuanced oncology decisions while reducing the burden of manually searching charts. Studies evaluating AI-assisted oncology prescreening have found potential improvements in identifying eligible patients and reducing screening workload, though human review remains important.

If you’re at a hospital/cancer center, a different shortlist may make sense than if you’re a CRO, sponsor, or community oncology network. The right tools and procurement path differ quite a bit.

Improving oncology patient recruitment requires software that can parse both structured data (like ICD-10 diagnosis codes and lab results) and complex, unstructured data (such as physician narrative notes, next-generation sequencing, and pathology reports).

Leading software solutions designed to analyze EMR/EHR data and match patients to oncology clinical trials include:

• Paradigm Health (incorporating Deep Lens) : Offers an AI-enabled, EHR-integrated clinical trial matching platform that deploys directly within clinical workflows. It analyzes structured and unstructured data, including molecular and genomic testing results, to surface eligible oncology candidates.
• ConcertAI (CancerLinkQ Suite) : Utilizes real-time, AI-powered matching tools aligned with NCCN guidelines. It integrates into clinical workflows to match eligible patients with relevant trials significantly faster than manual chart reviews.
• OncoLens : An oncology-specific platform utilizing AI, Natural Language Processing (NLP), and Large Language Models (LLMs) to screen structured and unstructured EHR data in real time for clinical trials and precision therapies.
• TriNetX : Provides a global federated health research network and EMR analytics tools that allow sponsors and health systems to run complex queries, evaluate trial feasibility, and proactively identify potential candidate pools.
• Deep 6 AI : Known for pioneering advanced NLP applied to medical records, its engine reads clinical notes and pathology reports to accurately infer complex cancer stages and biomarkers rather than relying solely on billing codes.
• GenomOncology : Utilizes a precision oncology knowledge graph and AI tools to match patient genomic and clinical data against an extensive curated database of active clinical trials.
• Tempus (TIME) : Connects a massive data-integrated oncology network with clinical trial matching solutions, helping provider networks accelerate study onboarding and precision matching.

If you'd like to narrow down the best fit, let me know:

• What EMR system (e.g., Epic, Cerner/Oracle Health, OncoEMR) are you currently running?
• Are you looking for a solution that operates strictly in-house or an externally networked platform for sponsor matching?