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Quantum

Longitudinal clinical-data intelligence.

Quantum unifies fragmented patient records into a single longitudinal timeline, readable at a glance even at the end of a shift. It reconciles multiple EHRs into a canonical model without ever writing back to the source systems, and pairs the clinician with an agent that surfaces slow, low-frequency patterns. It is a read-only review and evaluation aid for decision support, not a medical device.

−64% PATIENT LOOKUP TIME91% ALERT PRECISION0 WRITES TO SOURCE SYSTEMSADVISORY MODE · READ-ONLY−64% PATIENT LOOKUP TIME91% ALERT PRECISION0 WRITES TO SOURCE SYSTEMSADVISORY MODE · READ-ONLY
01In figuresFIELD DATA
−64%
patient lookup time
91%
alert precision
0
writes to source systems
02The problem

The patient's history is scattered across systems that don't talk.

On the ward, clinicians reconstruct a patient's history by jumping between Epic, Cerner and local spreadsheets. That fragmentation costs time, introduces errors of omission and hides slow but dangerous trends: a creeping creatinine rise, an unflagged pharmacogenomic interaction. FHIR standards have existed for years, yet they are rarely used to deliver a single, high-density, non-invasive view.

03What it does04
01

Longitudinal timeline

The whole patient history on one timeline with semantic zoom: the deeper you go, the more detail emerges.

02

Multi-EHR normalisation

Reconciles divergent FHIR profiles (e.g. Epic R4 and Cerner STU3) into a canonical model, with provenance tracked at the resource level.

03

Anti-alert-fatigue agent

A background agent over a rolling window combines codified clinical rules with low-frequency anomaly detection, surfacing only relevant patterns for review.

04

Read-only, non-invasive

No writes to the source systems: Quantum reads and presents, without altering clinical data or interfering with EHR workflows.

04How it works04 STEPS
01

Connect

Quantum attaches to the EHRs over FHIR in read-only mode, without touching the systems.

02

Normalise

Resources from different profiles converge into a canonical model with tracked provenance.

03

Analyse

The agent evaluates the rolling window and highlights patterns and anomalies for review.

04

Present

The high-contrast timeline makes what matters legible at a glance.

05Architecture

Quantum is designed around one non-negotiable principle: read-only. It attaches to EHRs over FHIR without ever writing to the source systems, reconciles divergent profiles into a canonical model with tracked provenance, and pairs the clinician with a background agent and a high-density timeline. It is a review and evaluation aid, not a medical device.

01Read-only FHIR connectionQuantum attaches to EHRs (Epic FHIR R4, Cerner FHIR STU3) in read-only mode only: no writes to the source systems, no interference with existing clinical workflows.
02Canonical normalisationA Python FHIR normalisation layer reconciles divergent profiles into a canonical model, with provenance tracked at the individual-resource level, so every datum stays traceable to its source.
03Anti-alert-fatigue agentA background agent over a rolling window combines codified clinical rules (CDS Hooks) with low-frequency anomaly detection, surfacing only relevant patterns for review rather than raw alarms.
04Longitudinal timelineA D3.js visualisation presents the whole patient history on a single high-contrast timeline with semantic zoom, legible at a glance even at the end of a shift.
06Specifications
Patient-lookup time reduction−64%
Alert precision91%
Writes to source systems0
Interoperability standardHL7 FHIR
Supported EHR profilesEpic R4 / Cerner STU3
Clinical rulesCDS Hooks
Access modeRead-only
ClassificationValutazione / non-dispositivo
07Deployment

Managed cloud

Deployment in a compliant healthcare cloud, with outbound FHIR connection to the EHRs and region-configurable data residency; suited to hospital networks that want a unified view without managing infrastructure.

Hospital on-premise

Installation inside the hospital IT perimeter, alongside the EHRs: the canonical model and timeline stay within the clinical infrastructure and patient data does not leave the institution.

Isolated / closed clinical network

For contexts with segregated clinical networks: Quantum operates entirely on the internal network, with no external connectivity, updating via signed packages and keeping read-only as an architectural constraint.

08Security & compliance

Standards and regulatory status

Quantum is a read-only review and evaluation aid, not a medical device, and it generates neither diagnoses nor clinical decisions. Data handling is aligned to ISO 27001, SOC 2 and health-data practices (HIPAA, GDPR).

Data sovereignty

Read-only is the primary safeguard: no writes to the source EHRs. In on-premise and isolated modes patient data does not leave the clinical perimeter; in cloud, residency is region-configurable.

Access and audit

RBAC aligned to clinical roles and an immutable audit trail of every consultation, with provenance tracked at the resource level: it can always be demonstrated who saw what and which source system a datum came from.

Encryption

AES-256 encryption at rest for the canonical model and TLS 1.3 for inbound FHIR connections; no clinical data is written in cleartext and keys remain within the institution's perimeter in isolated modes.

09Integrations
Epic (FHIR R4)Cerner (FHIR STU3)CDS Hooks for clinical rulesPython FHIR normalisation layerD3.js timeline visualisationHL7 FHIR-conformant data sources
10FAQ

Can Quantum modify the medical record?

No, never. Read-only is an architectural constraint, not a setting: Quantum reads and presents, without ever writing to the source systems or interfering with EHR workflows. Writes to source systems are zero by design.

Is it a medical device?

No. Quantum is a review and evaluation aid for decision support: it presents data and surfaces patterns for review, but it generates neither diagnoses nor clinical decisions, which stay with the clinician.

How does it handle EHRs that differ from each other?

The normalisation layer reconciles divergent FHIR profiles, for example Epic R4 and Cerner STU3, into a single canonical model, keeping provenance at the resource level so it is always clear which system each datum comes from.

How do you avoid overwhelming the clinician with alerts?

The anti-alert-fatigue agent combines codified clinical rules with low-frequency anomaly detection over a rolling window, surfacing only relevant patterns: alert precision is 91%, so attention goes to the slow signals that would otherwise be missed.

11The markGRID 64 · STROKE 5

The mark encodes the mechanism, not the sector: la croce ricavata dal pieno, non disegnata sopra.

44302216
ProofDeployed in production: read the case study
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