[MED] Healthcare & MedTech

One team for connected medical devices — sensor to cloud.

Connected medical device companies have long paid a hardware house, a firmware contractor and a cloud team separately — and paid again for the gaps between them. Rivix is the team that closes those gaps, with AI doing the heavy lifting that used to eat months, and a named senior engineer accountable end-to-end.

What this covers

The full stack, scoped for a connected health product.

Technical considerations

What actually shapes the architecture in this space.

Regulatory process, not just paperwork

IEC 62304 governs the software development lifecycle for medical device software, with process rigor scaled to safety classification and forward traceability required from requirements through design, code and verification evidence. ISO 14971 covers risk management — hazard identification, risk estimation and controls — extended for connected devices to firmware bugs, connectivity loss, cloud outages and cybersecurity threats.

HIPAA-relevant data handling

Any cloud, infrastructure or analytics vendor touching PHI needs a Business Associate Agreement in place. PHI in transit uses TLS, not a legacy or proprietary protocol; PHI at rest — on device, in transit queues, in cloud databases — stays encrypted, with role-based access control scoped to minimum need and audit logging of PHI access at the platform layer.

Fail-safe firmware patterns

A hardware watchdog timer resets the device on a firmware hang. Periodic self-tests check sensor plausibility, battery/storage range and wireless-stack liveness. Critical state — current session, last N readings, calibration data — is written to non-volatile memory frequently enough to survive power loss, structured to avoid mid-write corruption.

What the hardware and firmware look like in practice

Our fall-detection wearable pairs an IMU with a Cortex-M-class MCU running on-device inference, holding sub-second detection latency and sending only classified events over BLE, not raw motion streams. Our vitals patch combines a Cortex-M-class MCU with an analog front-end for ECG/PPG-class sensing, an RTOS-based scheduler separating sampling, radio and power-state duties, a BLE 5.x link to the companion app, and REST/HTTPS for the device-to-cloud hop.

Proven in the field

Real builds in health and medtech.

Pulled from our case-study library — same AI/human split shown in full, industry context included.

On-Device Early Fall-Detection Wearable

US-based aging-in-place / digital-health wearable startup

A wearable continuously analyzes motion data locally to identify movement patterns associated with potential falls, without continuously transmitting raw sensor data to the cloud.

~35%Estimated AI-first engineering effort reduction
Read full case study →

Remote Patient Vitals Patch

US-based digital-health / remote-patient-monitoring startup

A wearable medical patch continuously captures patient vital signs and securely transmits physiological data for remote monitoring.

~33%Estimated AI-first engineering effort reduction
Read full case study →

AI-Personalized Wearable Sleep Coach

US-based consumer wellness / wearable startup

A wearable captures sleep and physiological signals and uses AI to transform longitudinal data into personalized sleep insights and recommendations.

~30%Estimated AI-first engineering effort reduction
Read full case study →
Field Notes

What we've written about building in this space.

[MED]

HIPAA-Compliant Data Handling for Health Devices

Every hop a device's data takes is a place compliance can be designed in or quietly left out.

Read the article →
[MED]

Regulatory Basics for Connected Medical Devices

Regulatory strategy determines architecture and process from the first design decision, not paperwork layered on after.

Read the article →
[MED]

Reliability and Fail-Safe Design Principles for Medical Wearables

In a medical wearable, the failure case has to be designed first, not handled as an afterthought.

Read the article →
Common questions

What people ask before scoping a health device.

Do you design for HIPAA-relevant data handling?

Every hop a health device's data takes — device, transmission, storage — is a place compliance can be designed in or left out. We design it in from the architecture stage.

What's the AI/human split for a medical-grade product?

AI accelerates model-integration code and data pipelines; a senior engineer owns model selection, accuracy/recall tradeoffs and false-positive management — the calls that actually determine whether the feature works in the field.

Can firmware failure modes be designed for, not just handled after the fact?

Yes — in a medical wearable, a firmware bug can mean a missed alert or lost readings at the moment they mattered most, so the failure case is designed first, not handled as an afterthought.

Do you handle device certification too?

We design for pre-compliance from the first revision — EMC-aware layout, component margins and test-point planning — and coordinate with your certification lab through submission.

Building a connected health device and need one team for all of it?