HeartSciences needed a production-grade cloud backend for MyoVista Insights, their vendor-neutral ECG management platform. The system had to process ECG signals in real time, support AI-ECG algorithm inference from multiple model providers, integrate bidirectionally with a major EHR vendor via HL7 and FHIR, and meet healthcare regulatory requirements on AWS — all while supporting multi-tenant clinical operations across US and UK deployments.

ML LABS led the engineering effort, designing and building the entire cloud-native backend. AIM Consulting handled the frontend interface. The collaboration is a good example of specialized vendors complementing each other to move faster for the client.

The Problem

Building a medical device backend is not a normal software project. Every design decision carries regulatory weight, and the gap between "working prototype" and "production system regulators will accept" is where most teams stall. The compounding factor was interoperability: the platform had to ingest ECGs from multiple device manufacturers, each with their own DICOM encoding conventions, and integrate with hospital EHR systems that expected standards-compliant HL7 messaging.

graph LR
    A1["Multi-Vendor<br/>ECG Devices"] --> B1["DICOM<br/>Normalization"]
    B1 --> C1["Async AI<br/>Inference"]
    C1 --> D1["Clinical Report<br/>Workflow"]
    D1 --> E1["Bidirectional<br/>EHR Delivery"]

    style A1 fill:#1a1a2e,stroke:#0f3460,color:#fff
    style B1 fill:#1a1a2e,stroke:#ffd700,color:#fff
    style C1 fill:#1a1a2e,stroke:#ffd700,color:#fff
    style D1 fill:#1a1a2e,stroke:#e94560,color:#fff
    style E1 fill:#1a1a2e,stroke:#16c79a,color:#fff

Multi-Vendor ECG Ingestion

The platform had to be genuinely vendor-neutral at the DICOM parsing level. Devices from multiple major manufacturers each encode 12-lead ECG waveforms and measurements differently. Amplitude values arrive in different units, lead ordering varies, and measurement segments use vendor-specific structures.

We built a normalization layer that extracts waveform data and clinical measurements from each vendor's DICOM format into a unified internal representation. Deduplication at the storage layer prevents redundant waveform copies — when a clinic uploads the same ECG through multiple pathways, the system recognizes the duplicate and links rather than re-stores.

The hardest interoperability problems are not protocol-level — they are the subtle measurement normalization differences between vendors that produce clinically incorrect values if handled generically.

AI Inference Architecture

The AI layer processes ECGs through multiple model providers — including rhythm analysis and cardiac function models for ejection fraction assessment. Each model has different latency characteristics, availability patterns, and billing implications.

We built an async inference architecture that decouples ECG submission from AI result delivery. The system handles provider unavailability gracefully, prevents duplicate billing when providers fail mid-processing, and supports per-organization model enablement for staged algorithm rollouts.

EHR Integration Layer

EHR integration required bidirectional HL7 messaging — not just reading patient data, but participating in the clinical order workflow. The platform receives order messages when a physician requests an ECG interpretation, processes the study through AI inference, and returns result messages with clinical measurements and billing codes that downstream hospital systems depend on for reimbursement.

On the FHIR side, the platform implements per-organization authentication that prevents cross-tenant data access. This dual-protocol approach (HL7 for clinical workflow, FHIR for data access and launch context) reflects how EHR integrations actually work in production.

Clinical Report Workflow

Clinicians sign, confirm, and lock clinical reports through a lifecycle that carries legal and regulatory weight. We built a report workflow that enforces clinical accountability at every stage — once signed, every subsequent modification is tracked with full audit trail visibility.

PDF generation handles the visual complexity of clinical ECG reports across multi-page layouts. The system supports mobile viewing on iPad and iPhone with touch interactions for clinical measurement tools.

Multi-Tenant Operations

The platform operates as a true multi-tenant system with per-organization configuration controlling everything from enabled AI models to HL7 field mappings to invoice pricing. New hospitals can be onboarded with site-specific configuration without touching the codebase.

The billing system implements usage-based invoicing that handles partial months, mid-cycle onboarding, per-site minimums, annual caps, and tiered pricing. Multi-environment deployment across TST, PRD-US, and PRD-UK ensures configuration changes are validated before reaching production.

Security Posture

We remediated security findings across the system, spanning credential management, data exposure prevention, injection protection, and authentication hardening. The security work was integrated into the delivery cadence rather than deferred to a separate hardening phase.

Results

MyoVista Insights is now in production as HeartSciences' core platform — connecting ECG hardware from multiple manufacturers, AI-ECG algorithms from multiple providers, and clinical tools into one coherent ecosystem. Clinical reports generated by the platform achieved a 97% acceptance rate across participating institutions.

Clinicians access ECGs, AI results, and prior studies in a single integrated view — including directly within the EHR. A research SDK provides programmatic access to ECG data across all ingested formats. A natural language search capability lets clinicians find studies using clinical language rather than navigating filter interfaces.

First Steps

  1. Start with regulatory requirements as architecture inputs. Build compliance into the data model and deployment pipeline from the first commit.
  2. Design for multi-vendor interoperability from day one. Normalizing data formats after the fact introduces subtle clinical accuracy bugs.
  3. Treat EHR integration as a first-class subsystem. HL7 and FHIR patterns are well-documented in theory but full of per-organization edge cases in practice.

Practical Solution Pattern

Build regulatory compliance and interoperability into the architecture from the start rather than treating them as integration layers bolted on later. Invest in vendor-neutral data normalization, async AI inference with billing safeguards, and bidirectional EHR messaging with per-organization configuration.

This works because the cost structure of regulated systems is fundamentally front-loaded — architectural decisions made in the first months determine whether the system can scale to new sites, new AI models, and new EHR partners without rework. If you need to decide whether a regulated workflow is ready for build, a Strategic Scoping Session can pressure-test scope, constraints, and next steps before engineering work starts.