Telemetry & TimescaleDB Architecture

This document tracks the design decisions and architecture for handling high-frequency health data (IoT devices, wearables, continuous monitors) in Health Assistant.

The Problem

Standard clinical data maps beautifully to the HL7 FHIR Observation model. A blood test taken once every 3 months is easily stored in the fhir_observations PostgreSQL table. However, devices like the Apple Watch, Oura Ring, or continuous glucose monitors (CGMs) can generate health measurements every 1 to 5 minutes. Routing this data into standard FHIR structures results in massive table bloat, drastically degrading dashboard performance and slowing down routine clinical queries.

The Solution: Split Architecture

Health Assistant Implements a Frequency-based Routing Architecture

Low-Frequency (Clinical) Data -> FHIR Observations
Stored in standard fhir_observations table.
Used for blood panels, point-in-time weight, diagnostic results.
High-Frequency (Telemetry) Data -> TimescaleDB
Stored in the telemetry_data hypertable.




Extremely high ingestion limits and heavily compressed.
Energetic Routing via Biomarker Definitions

Instead Of Hardcoding Which Metrics Go To Which Database, Routing Is Configurable. The Biomarkerdefinitions Table Includes An Istelemetry Boolean Flag. When Integration Webhooks Push Data To The Backend, The Parser Resolves The Data To a Standard Biomarker. The Core Sync Engine Then Inspects This Flag

If is_telemetry = true: Data is mapped to a TelemetryDataModel and saved to TimescaleDB.
If is_telemetry = false: Data is mapped to an Observation and saved to FHIR.

Automated Data Migration: System administrators can toggle this flag via the UI, making the system infinitely expandable for future IoT devices without requiring code changes (see "Long-Format Storage" below for why this is now truly zero-code). When the flag is toggled on an existing biomarker, the Celery task migratebiomarkerdata performs an automatic, batched (5000 ro

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