Deep 6 AI

Natural language processing over unstructured clinical data is the whole product and the company quantifies why it matters: it states 92 percent of trial inclusion and exclusion criteria benefit from unstructured data, and that 15 to 20 percent of eligible patients are found through unstructured data alone. Mining physician notes, pathology reports, genomics results, and labs is described as accessing up to 80 percent more patient data than systems relying on coded fields. Without the language models the platform would be a structured query tool that misses most eligible patients.

The system identifies and surfaces candidates with proof of matching, and human research staff validate and recruit. The company describes a distinct validation step between identification and recruitment, and separately enables treating physicians to refer patients to studies they are eligible for based on targeted study information and matching evidence. Providing the matching rationale rather than a bare score is what makes both the coordinator validation and the physician referral decision meaningful. No published detail describes confidence thresholds or how borderline matches are handled.

The approach is described concretely at the data level, covering AI and NLP applied to structured and unstructured EMR data in real time across notes, pathology, genomics, and labs, with quantified claims about how much additional patient data that unlocks. What is not published is model architecture, matching accuracy, or precision and recall figures. This is a category wide gap: peer reviewed literature has specifically noted that companies in trial matching, naming this one among others, have not published performance data outside small studies or restricted cohort abstracts, which is a fair criticism to record.

Operational claims are specific but vendor reported, with sites stated to find more than 25 percent more patients than traditional recruitment methods and one referenced study citing recruitment acceleration up to tenfold alongside increased trial population diversity. Named partnerships with oncology development organizations indicate real commercial traction. The significant caveat is external and worth recording plainly: published academic literature has observed that this company and its peers have not released performance data beyond small studies or restricted abstracts, so independent verification of matching accuracy does not exist publicly.

The company states the platform operates within a secure HIPAA compliant collaborative environment, which is a direct if brief commitment. The data surface is broad, spanning full unstructured records including notes, pathology, and genomics across health system populations, and the ecosystem model deliberately connects providers, sponsors, and CROs, so where patient level data flows between those parties is the question a buyer should map. No retention or training data terms were located.

HIPAA compliance is stated explicitly for the platform environment. No separate business associate agreement commitment was located, which is what would raise this, particularly given the multi party ecosystem structure where data visibility extends beyond the originating health system.

No SOC 2, HITRUST, or ISO 27001 attestation was located, and no trust center was found.

No FDA pathway applies. Trial matching identifies candidates for research rather than informing diagnosis or treatment. The governing regime is human subjects research oversight, meaning IRB approval, informed consent, and Good Clinical Practice, with the physician referral pathway adding conventional clinical judgment on top.

No governance framework was located. One referenced study reports increased diversity in trial populations through identification of underrepresented patient groups, which points in the right direction, but no published analysis compares the demographic profile of AI surfaced candidates against manually screened ones, which is the measurement that would substantiate it.

Direct EMR integration is the delivery mechanism, with the platform analyzing structured and unstructured record data in real time rather than working from periodic extracts. Real time operation against live records is the harder engineering problem and is what enables the stated ability to surface matches in minutes. Deployment runs on major cloud infrastructure. No named EHR integrations, certifications, or standards support were enumerated publicly.

Described as a cloud based platform deployed on major cloud infrastructure. No tenancy, hosting detail, or data residency terms are published, which matters for an ecosystem model where multiple organizations access a shared environment.

No published pricing. The ecosystem spans health systems, treating physicians, sponsors, and CROs, and as with peers in this category the economics likely flow primarily from the sponsor side while providers host the patients, but nothing about structure or rates is disclosed.

Functional coverage is broad across the trial lifecycle, spanning cohort building and feasibility at study design, site and principal investigator selection, patient recruitment, on study data gathering, and real world evidence generation, with an integrated recruitment module beyond identification. Oncology is the evident depth given named partnerships and product positioning, with fibrosis also referenced. Buyers span health systems, treating physicians, sponsors, and CROs.