Industry

Healthcare AI,
inside your perimeter.

A clinician stays accountable. Documentation, trial evidence and patient access — built to GDPR Article 9-aligned controls, on infrastructure inside your own perimeter. A clinician stays accountable for every record.

Last reviewed 4 September 2026 · Engineering guidance, not legal or regulatory advice.

In context

Documentation, trial evidence and patient access — built to GDPR Article 9-aligned controls, on infrastructure inside your own perimeter. A clinician stays accountable for every record.

Where it breaks today

Unstructured by default

Patient charts, trial reports, claims and the literature arrive as prose and PDF. Most of what a clinical or research team needs is in there; almost none of it is in a form a system can act on.

Documentation competes with care

Documentation burden is one of the most consistently cited contributors to clinician burnout, and it is work that competes directly with patient care and with research.

The data cannot go to a public endpoint

Off-the-shelf tools without a business-associate agreement (US) or a lawful basis and processor terms for special-category data (EU, GDPR Article 9) cannot be used on patient data — so most clinical workflows stay manual.

OUR APPROACH

Reducing administrative burden

The systems we build in healthcare share one design rule: the model changes how long the administrative work takes, not who is accountable for it. A note is drafted, but a named clinician reads and signs it. A trial value is extracted, but it carries a citation to the page it came from. A patient contact is resolved, but any hint of clinical urgency goes to a person first.

That rule is what keeps the work on the right side of the medical-device boundary. A system that suggests a diagnosis or assesses severity is a medical device in most jurisdictions, with a regulatory pathway to match. We build the administrative layer around the clinical one, and we say clearly where the line is. Whether a given system crosses it is for your regulatory function to determine.

HOW IT DEPLOYS

PHASE 01

Map the workflow

We audit clinical and administrative data flows — EHR entries, claims, trial documentation — and identify the highest-burden extraction and routing targets.

PHASE 02

Deploy inside the perimeter

Models run on infrastructure you control, designed so that PHI does not leave your perimeter — controls enforced by the architecture, not only by policy.

PHASE 03

Return time to care

Extraction, drafting and routing are handled by the system; review and signature stay with a clinician, and every routed record carries a full audit trail.

What we build

Clinical Trial Data Extraction

Multimodal models that extract cohorts, adverse events and efficacy measures from PDF trial reports into a reviewable table, with a citation on every value so a researcher can verify it at source.

SEE RAG SYSTEMS →

Patient Access Automation

Scheduling, preparation instructions, intake and forms, with any indication of clinical urgency routed immediately to a person. The system does not triage clinically and does not assess severity.

SEE PATIENT ACCESS →

Perimeter-Bound Orchestration

Models on infrastructure you control, designed so that Protected Health Information (PHI) does not leave your perimeter or reach a provider that could train on it. Built to GDPR Article 9-aligned controls, with access scoped by role and every record access logged.

SEE SECURE PIPELINES →

EHR System Workflows

Cross-platform workflows that bridge legacy Electronic Health Records (EHR) and modern clinical tools, so data is read, structured and routed across systems without replacing the EHR.

SEE CROSS-PLATFORM WORKFLOWS →

Controls we implement

GDPR ART. 9-ALIGNED CONTROLS PHI INSIDE YOUR PERIMETER NO TRAINING ON YOUR DATA ROLE-SCOPED ACCESS AUDIT LOG ON EVERY RECORD READ SOVEREIGN INFRASTRUCTURE

These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.

Healthcare use cases

HLT.01

Clinical Documentation

Ambient scribing and coding support, and the review step that keeps a clinician accountable for the note.
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HLT.02

Patient Access

Scheduling, triage and intake automation that knows the difference between an inconvenience and an emergency.
Open page
HLT.03

Life-Sciences Research

Literature synthesis, trial-protocol drafting and evidence extraction with citation as a hard requirement.
Open page

Questions

Questions about Healthcare & Pharma.

How do you handle special-category health data under GDPR Article 9?

By architecture. Models run on infrastructure you control, designed so that patient data is not sent to public API endpoints and does not reach a provider that could train on it. Where a hosted model is used at all, it sits behind processor terms and a documented lawful basis, and the controls are enforced by the architecture rather than only by policy. Two limits worth stating plainly: the legal determination of whether those controls are sufficient for your context is yours to make, and if US health data is in scope, the HIPAA programme, the business-associate agreements and any attestation are yours — we hold none and cannot supply them.

How does AI extract data from clinical trial documents?

Multimodal models read PDF trial reports directly — tables, charts and free text — and extract cohorts, adverse events and efficacy measures into a structured, reviewable form, with a source citation on every extracted value so a researcher can verify it. Adverse-event extraction feeds a human case assessment; it does not replace one. How much abstraction time this saves depends on your document set, which we measure on a sample before scoping.

Can AI agents triage patients?

Clinical triage is a clinical activity and in many jurisdictions a regulated one, so no. The patient-access systems we build handle scheduling, preparation and intake, and screen every contact for any indication of urgency before anything else — with the threshold set deliberately low. A contact touching symptoms, medication, deterioration or distress goes to a person immediately, with the full conversation attached. The system does not assess severity and does not advise.

Does AI automation integrate with legacy EHR systems?

Yes. We build cross-platform workflows that bridge legacy Electronic Health Records and modern clinical tools, so the system can read, structure and route data across them without a rip-and-replace of your existing EHR. That integration is usually the larger part of the build, and it is where the realistic timeline is set.