Near-real-time monitoring for every connector: success %, backlog, last sync, error taxonomy, and end-to-end latency from device to FHIR publish.
Transforms heterogeneous wellness payloads (Apple, Google, Fitbit, Samsung, etc.) into a standard schema with units, timezones, and sampling intervals normalized. Each record carries device provenance and quality flags for downstream filtering.
Operational cockpit showing freshness, completeness, gaps, and outliers by user, device, and connector. Highlights drop-offs (e.g., no sync in 72h), clock skew, and obviously bad values for triage or automated suppression.
Benefits
- Consistent units and semantics across vendors
- Easier clinical thresholds & analytics
- Reduced mapping churn as APIs evolve
- Faster detection of broken device links
- Cleaner datasets for clinical review
- Prioritized follow-ups with patients
Notes
- Store raw payload alongside normalized record for defensibility.
- Distinguish user behavior gaps vs. technical failures in KPIs.
Great for:
- Security or Compliance Officers: Monitor data integrity and provenance across device apis. Surface validation issues and sync gaps for defensible audit posture.
- Production Support and IT Operations: Detect broken links, stale syncs, and malformed payloads in near-real-time. Use error taxonomy and latency metrics to triage issues quickly.
- IT Managers and Platform Architects: Visualize connector health and data quality across vendors. Reduce mapping churn and support scalable normalization pipelines.