II
Vision II of V · Five Visions for the Future of HIEs

Project Intelligent Quality

iQHD 2.0 and FHIR·A: quality that flows in both directions.

Timeline18–48 monthsKey enableriQHD 2.0 + AI enrichmentBuiltwith HIEs, for HIEs
The problem

Quality is measured, then wasted

Clinical documents flow through exchange networks at volume, but almost nobody can answer with precision: how complete are the documents from Hospital X? Are coded diagnoses conformant? Are medication lists populated? Are allergies documented consistently? Data quality is trusted blindly, and where it is measured, the measurement dies in a report.

Worse, the flow is one-directional. The source system that produced an incomplete document never hears about it, so the same defect arrives again tomorrow, and the network's quality never compounds.

The vision

What changes

Intelligent Quality makes the Gateway a bidirectional quality engine. Every record traversing it is measured, and the measurement travels back to the source as a standardised, actionable signal.

iQHD 2.0 expands scoring from today's dimensions toward 120 quality dimensions across five families: presence, conformance, recency, semantic richness and provenance integrity. AI enrichment tools repair, reconcile and complete records in flight. And a proposed standard, FHIR·A, extends FHIR R4 with provenance-first, confidence-scored, agent-readable representations optimised for the AI systems that will increasingly consume this data.

The architecture

How it works

01

Score every document in flight

Each CCD and FHIR bundle is assessed across the full iQHD dimension set as it traverses the Gateway, with no added latency budget for the requester.

02

Feed the signal back to the source

Quality findings return to contributing systems as standardised FHIR resources: which sections were missing, which codes non-conformant, which timelines broken.

03

Enrich and reconcile with AI

Where repair is safe and provenance-preserving, enrichment tools normalise versions, deduplicate entries and flag conflicts for human review.

04

Publish FHIR·A for agentic consumers

Confidence-scored, provenance-first representations give AI agents what raw FHIR does not: an explicit statement of how much each element should be trusted, and why.

The return

What HIEs gain

Quality compounds across the network

Source systems improve their own output based on Gateway-derived signals; every quarter's documents arrive better than the last.

The HIE owns the intelligence

Quality reporting, contributor benchmarking and trend analytics are delivered as assets the HIE owns and can take to its board and its participants.

Provider engagement improves

Clinicians who query the HIE and receive high-quality data keep using it, reversing the disengagement spiral incomplete data creates.

Agent-era readiness

When intelligent agents read records at scale, the network that ships confidence-scored data becomes the network they prefer.

Cures Gateway's role

Every document already flows through the Gateway's 47-dimension IQHD engine, and quality scorecards already ship in the HIE dashboard. Intelligent Quality is the natural next release: closing the loop back to sources, expanding the dimension set, and standardising the agent-facing representation.