03 / CASE STUDY
Respiratory intelligence and connected health-data infrastructure
Healthmetryx.
Connected health-data platform work spanning respiratory sensors, wearable integration, cloud infrastructure, analytical services and institutional workflows.
SYSTEM MODEL
Architecture overview
01 / CONTEXT
The problem space
Health-data products combine components that evolve independently: physical devices, operating-system capabilities, data transport, storage, analytical services and application interfaces. Each component can work alone while the integrated product remains unreliable.
The architecture must handle data quality, time context, connectivity interruptions, cloud costs and integration boundaries while preserving the distinction between raw observations, derived indicators and consequential health decisions.
TECHNICAL DETAIL / 02
Device and signal integration
The project context includes Clarinet respiratory sensors and a Samsung Galaxy Watch 7 / Wear OS prototype context involving Knox and Samsung Synaptics.
Device integration requires more than connecting an application. Sources differ in permissions, data structures, timing, connectivity and supported signals. Compatibility must be verified for each integration; a prototype does not establish clinical validity.
- Define device-facing interfaces
- Preserve source and time context
- Handle missing observations and connectivity changes
TECHNICAL DETAIL / 03
Data ingestion and organization
A dependable platform needs a controlled path from incoming observations to usable records. Ingestion should validate payloads, preserve source context and separate original observations from transformations and derived indicators.
These controls become more important as additional devices, user groups and institutional workflows are introduced.
- Consistent representations across sources
- Validation and malformed-record handling
- Traceability of transformations
- Access, retention and provenance controls
TECHNICAL DETAIL / 04
Cloud and application infrastructure
The development context includes an RCM AI demonstration MVP using Google Cloud and Firebase, alongside broader cloud and migration considerations.
An earlier demonstration environment became unavailable following a cloud billing overage. This illustrates that cost controls, environment visibility and recovery planning are engineering requirements—not just administrative tasks.
- Separate application, data and analytical boundaries
- Make dependencies and configuration explicit
- Establish cost limits and operational visibility
- Document recovery expectations
TECHNICAL DETAIL / 05
AI and analytical services
AI-enabled analysis is a downstream consumer of data infrastructure. The architecture separates collection, input preparation, model execution and presentation so analytical components can be evaluated or replaced without rebuilding the whole platform.
A model cannot compensate for unreliable source data. An analytical score is not automatically a validated clinical finding; predictive accuracy and health-outcome claims require supporting evidence.
TECHNICAL DETAIL / 06
Applications and institutional workflows
The application layer makes data and analytical outputs usable within intended workflows. Clear contracts between data services and applications reduce the risk that every use case creates a separate, incompatible pipeline.
The architecture should evolve incrementally: reliable data path and observable application first, verified analytical behavior next, then integrations justified by product requirements.
TECHNICAL DETAIL / 07
Security, privacy and validation
Health-related data requires governance around access, transmission, storage, retention and downstream use. Controls must be verified for the actual implementation and deployment rather than assumed from cloud-provider use.
Technical reliability and clinical validity are separate requirements. A functional data pipeline does not establish the safety or effectiveness of a clinical intervention.
- Authentication and authorization
- Data minimization and retention
- Integrity and provenance
- Device and integration testing
- Validation for each intended use
DESIGN RATIONALE
Key decisions
Stabilize before expanding
Build a reliable, observable minimum workflow before adding more devices, models or integrations.
Separate source data from interpretation
Preserve the boundary between raw observations, transformed data, model outputs and consequential decisions.
Treat cost and continuity as architecture
Cloud spend, dependency visibility and recovery behavior affect whether a prototype can remain available.
Validate each intended use
Technical integration, model performance and clinical validity are separate claims requiring separate evidence.
VERIFICATION
Evidence and boundaries
Evidence available
- CTO role across architecture and implementation workstreams.
- RCM AI demonstration MVP context involving Google Cloud and Firebase.
- Connected-device and wearable integration workstreams.
- Cloud migration and operational stabilization considerations.
Claims not assumed
- The workstreams do not imply every integration reached production deployment.
- No clinical outcome, predictive-accuracy improvement, regulatory status or financial return is asserted without evidence.
- Security and compliance claims must be verified for the specific deployment and requirements.
ARCHITECTURE PRINCIPLE
“A health-intelligence product is only as dependable as the complete path from source data to the workflow that consumes it.”