AI and predictive tools need continuous, time-sequenced data. Episodic snapshots limit accuracy and reliability.

FHIR APIs improve data exchange but do not solve identity matching, terminology alignment, or data quality.

Gaps in patient histories lead to delayed diagnoses, missed screenings, and widening health inequities.

Healthcare decisions slow down when data lives in silos. A harmonization layer turns this noise into meaningful, real-time signals.

Missing clinical context weakens care decisions, affects reimbursement, and limits the success of value-based care programs.

Device clouds record signals. EHRs record encounters. Claims record payment events. Without a middle layer that aligns identifiers and formats, raw telemetry never becomes clinical

Interoperability shapes referral trust, payer confidence and contract readiness. Device companies that support clean data exchange advance faster than those that treat it as a

AI models in device care fail when data is fragmented. A unified layer aligns identifiers, timestamps and terms, giving models a stable base for prediction.

A structured three phase roadmap helps device programs move from manual work to unified insight. Each phase reduces load and increases visibility.