Deep 6 AI: How Top Life Sciences Teams Are Slashing Trial Timelines by Months
What is Deep 6 AI? Deep 6 AI is an advanced artificial intelligence platform that accelerates clinical trial patient recruitment by mining vast amounts of structured and unstructured electronic health record (EHR) data in real time. Using natural language processing (NLP) and machine learning, the platform can analyse millions of patient records to match trial […]
Feature Categories
What is Deep 6 AI?
Deep 6 AI is an advanced artificial intelligence platform that accelerates clinical trial patient recruitment by mining vast amounts of structured and unstructured electronic health record (EHR) data in real time.
Using natural language processing (NLP) and machine learning, the platform can analyse millions of patient records to match trial protocols with eligible participants in minutes instead of months.
Deep 6 AI empowers life sciences companies, CROs, and research hospitals to design more feasible protocols, identify hard-to-find patient populations, and reduce costly trial delays. The result is faster trial start-up, increased diversity in enrollment, and higher trial success rates.
Sites find >25% more patients with Deep 6 AI than traditional recruitment methods.
Why Leading Healthcare Teams Trust Deep 6 AI
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Rapid Trial Participant Identification
Reduced a Crohn’s disease trial’s patient identification time from months to under 45 minutes, finding and validating 36 eligible patients where conventional methods found just 30 (after much delay). -
Extensive Real-World Ecosystem
Facilitates precision recruitment with access to EMR data from 30 million patients, 30,000+ healthcare professionals, and thousands of active trials across numerous leading research institutions, including six NCI-designated cancer centers. -
Expanded Precision-Recruitment Solution
The Recruitment Acceleration platform now allows sponsors and sites to share AI-matched patient cohorts with IRB-approved site teams, enhancing eligibility validation and dramatically shortening enrollment timelines. -
Academic Health Partnership
Texas Tech University Health Sciences Center uses Deep 6 AI to integrate AI into its EMR system for real-time trial matching—boosting patient access and accelerating therapeutic delivery. -
Top-Level Partnership for Trial Diversity & Speed
Strategic collaboration with WCG connects Deep 6 AI’s technology to over 3,300 research institutions, 5,000+ sponsors/CROs, and an ecosystem of 28 million patients across 2,000+ healthcare facilities to drive faster, more diverse participant enrollment. -
Acquired by Tempus AI (March 2025)
Deep 6 AI was acquired by Tempus AI—expanding Tempus’s platform reach to include over 750 provider sites and reinforcing their shared mission of accelerating precision medicine.
Top 3 Pain Points Deep 6 AI Fixes in Healthcare
| Problem | How Deep 6 AI Solves It | Impact on Clinical Trials |
|---|---|---|
| 1. Slow and manual patient recruitment | Automates the process of scanning millions of patient records to find matches instantly. | Reduces recruitment timelines from months to days. |
| 2. Inability to find hard-to-reach or rare disease patients | Uses AI and NLP to analyze structured and unstructured EHR data to uncover eligible patients often missed by traditional queries. | Increases enrollment rates and patient diversity. |
| 3. High trial costs due to recruitment delays | Accelerates site feasibility and patient identification, ensuring trials start on schedule. | Saves millions in operational costs and boosts trial success rates. |
Feature Category Summary: Deep 6 AI
| Feature Category | Summary | Association (YES, NO, NA) |
|---|---|---|
| Regulatory-Ready | Deep 6 AI emphasizes healthcare‑grade security and compliance; its data security statement notes adherence to NIST/ISO standards and confirms the platform is “fully HIPAA‑compliant,” and the company works with hospital IT, compliance, and IRBs when deploying EMR/EHR data connections, but there is no public, detailed description of 21 CFR Part 11 / EMA Annex 11 GxP validation or computer‑system validation packages for the platform itself. “No public documentation found” for explicit FDA/EMA GxP validation claims. | NA |
| Clinical Trial Support | The platform is designed specifically to accelerate clinical research by using AI and NLP to query complete EMRs, identify highly precise cohorts, and provide “screen‑ready” lists to IRB‑approved researchers; marketing and case studies show dramatic improvements in recruitment speed and cohort identification and describe visibility into site‑level recruitment progress, directly supporting trial recruitment and operational monitoring. | YES |
| Supply Chain & Quality | All available materials focus on data mining, cohort discovery, and recruitment workflows; there is no indication Deep 6 AI addresses manufacturing QA, drug product serialization, logistics, or counterfeit detection. “No public documentation found” for supply‑chain or manufacturing‑quality functionality. | NA |
| Efficiency & Cost-Saving | Deep 6 AI reports that its AI mines up to 80% more EMR data by including unstructured notes, enables cohorts to be identified 2× more precisely and patient screening 4× faster, and that real‑world customers (e.g., Cedars‑Sinai) went from 2 enrolled patients in six months to identifying 16 qualified candidates in one hour, demonstrating substantial time savings and reduced site burden. | YES |
| Scalable / Enterprise-Grade | The company describes “real‑time EMR data feeds across 30+ health systems” and an ecosystem of over 1,000 active research facilities using the platform, plus its acquisition by Tempus AI as a “leading AI‑powered precision research platform” for healthcare and life sciences organizations, indicating large‑scale, multi‑institution deployments. | YES |
| HIPAA Compliant | Deep 6 AI’s data security page states that its systems are “fully HIPAA‑compliant,” built on NIST/ISO‑based cloud security frameworks, and deployed in close collaboration with hospital security, compliance, and IT teams, confirming explicit alignment with HIPAA for PHI handling. | YES |
| Clinically Validated | Publications and case reports show that Deep 6 AI substantially accelerates and improves recruitment efficiency at health systems and research networks, but there is no evidence that the platform has been evaluated as a clinical diagnostic/therapeutic device or undergone formal clinical validation trials in that sense; validation is operational (recruitment metrics), not clinical outcome–based. “No public documentation found” for device‑style clinical validation. | NA |
| EHR Integration | Deep 6 AI advertises real‑time EMR/EHR data feeds across numerous health systems and the ability to query entire medical records, including structured data, clinician notes, labs, pathology, and omics, as well as tools enabling in‑network providers to identify and refer eligible patients through the Deep 6 interface, which implies deep integration with EHR/clinical data systems. | YES |
| Explainable AI | The platform describes constructing patient graphs that represent each individual’s clinical profile and then matching these graphs to trial criteria, making cohorts and matches directly reviewable by clinicians and coordinators; however, there is no explicit mention of formal explainability techniques (e.g., feature‑level attributions or reason‑code explanations for each match) beyond transparent presentation of criteria and patient data. “No public documentation found” for model‑level explainable‑AI tooling. | NA |
| Real-Time Analytics | Deep 6 AI highlights “real‑time EMR data feeds” across many health systems and describes its Recruitment Acceleration solution as matching patients to trials at “real sites in real time,” with sponsors gaining visibility into recruitment progress at sites via the platform, indicating real‑time or near–real‑time analytics on recruitment activity. | YES |
| Bias Detection | Despite operating on rich EMR data and supporting diverse patient identification, there is no documentation that Deep 6 AI provides specific tools to measure or mitigate algorithmic bias (e.g., performance by race/ethnicity, age, sex) or audits cohort diversity beyond general claims of broader catchment; bias detection is not described as a product feature. “No public documentation found” for bias‑detection functionality. | NA |
| Ethical Safeguards | Deep 6 AI’s deployment model includes IRB‑approved research staff, HIPAA‑compliant infrastructure, and close collaboration with hospital compliance/IT, which provide governance around data use and patient contact, but there is no explicit description of built‑in ethical AI controls such as configurable use‑case restrictions, consent management workflows for AI‑driven outreach, or formal human‑in‑the‑loop policy enforcement beyond standard research oversight. “No public documentation found” for explicit ethical‑AI safeguard tooling. | NA |
Risks & Limitations: Deep 6 AI
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Predictive performance depends on the quality, completeness and timeliness of source data (EHR, coding, notes); gaps or inconsistent records reduce accuracy.
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Outputs are decision-support only; clinical and trial teams must validate cohorts and selections before action.
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Integration with proprietary EHRs, clinical data warehouses, or CTMS often requires IT mapping, identity resolution and middleware.
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Regulatory, privacy and compliance review is required when using AI outputs to inform patient recruitment, trial inclusion/exclusion or safety monitoring; maintain audit trails.
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Generalisability risk: models trained on one network or coding practice may underperform at other sites—local validation and calibration are essential.
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Bias and representativeness: under-captured populations or coding disparities can skew cohort identification—monitor equity metrics.
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Model drift and data-schema changes (new codes, templates, or EHR upgrades) can degrade performance—plan for monitoring and periodic retraining.
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False positives/negatives in cohort matches can waste site resources or miss eligible patients—expect human review and sample validation.
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Operational & governance overhead: effective use requires COE processes, clinician/researcher oversight, and documented SOPs for safe scaling
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