The right healthcare knowledge-graph platform is the one that can answer your defined clinical, research, or operational questions with traceable, current data—and enforce the permissions that apply to every answer. Evaluate it against a representative workflow, not a feature list. Start by specifying the entities and relationships you need, then test interoperability, identity resolution, terminology, provenance, privacy, latency, reliability, deployment, and total cost with evidence from the intended sources.
1. Define the use case before comparing platforms
A knowledge graph is useful only when its model reflects a real decision or workflow. Write the questions the system must answer and identify who will ask them. A clinical team might need to find all relevant observations, medications, conditions, and care relationships for one patient. A research group might need to connect cohorts, specimens, protocols, and published results. An operations team might need to locate records across organizations, identify missing information, or route work based on relationships between patients, providers, facilities, and claims.
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Specify the entities and relationships
List the resource types, identifiers, and links that must be represented. Include patient and practitioner identities, organizations, encounters, orders, observations, medications, conditions, devices, documents, claims, locations, and any domain-specific entities. For each relationship, state its direction, cardinality, time period, and whether it is asserted by a source or inferred by the platform.
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- Provenance: Which source supplied a fact, when was it received, and what transformation or reconciliation changed it?
- Terminology: Which coding systems are accepted, and how are local codes mapped to standard concepts?
- Constraints: Which relationships must concern the same patient, encounter, episode, or organization?
- Access context: Which users, applications, purposes, and locations may see or act on each relationship?
Use FHIR graph definitions carefully
HL7 FHIR R5 GraphDefinition describes a graph of FHIR resources by following references and can document relationship rules, including whether linked resources concern the same patient. It is marked Trial Use at maturity level 2. That status means you should confirm the FHIR release, profiles, and implementation guide required for your deployment rather than assuming that an R5 GraphDefinition will be stable or interchangeable with another implementation.
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2. Verify interoperability at implementation level
“Supports FHIR” is not a sufficient acceptance criterion. Ask for the exact release, profiles, operations, capabilities statement, and production status. The CMS Interoperability Framework says networks must provide or facilitate access through FHIR APIs that adhere to the US Core FHIR implementation guide, include a full FHIR capabilities statement, support USCDI v3 or later, and provide terminology compliance such as labs in LOINC, medications in RxNorm, and conditions in SNOMED. The framework labels criteria 13 and 14 as in effect on July 4, 2026; determine whether those criteria apply to your organization and use case.
| Interoperability area | Evidence to request | Why it matters |
|---|---|---|
| FHIR release and profiles | Version, supported resources, profiles, operations, capability statement, and implementation guides in production | Prevents a nominal FHIR connection from failing on required fields or operations |
| US Core and USCDI | Documented element coverage, validation results, and handling of extensions | Shows whether exchanged data is usable rather than merely parseable |
| Terminology | LOINC, RxNorm, SNOMED, local-code mappings, versioning, and update procedures | Controls meaning, searchability, and longitudinal consistency |
| Exchange modes | Single-record APIs, bulk data, subscriptions, documents, and human-readable attachments | Different workflows require different latency and payload patterns |
| Network connectivity | Federated queries, record-locator behavior, authentication, and response semantics | Determines whether the graph can reach the sources users actually need |
Also test document formats, pagination, error responses, rate limits, asynchronous jobs, and version negotiation. If a capability is described as planned, treat it as unavailable until it is demonstrated in the target environment.
3. Measure data quality, coverage, and freshness
A graph can be internally consistent yet incomplete. Request an inventory of upstream systems and data classes for the intended geography and workflow. Ask which sources are connected today, which require separate agreements, and which are excluded.
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Provide duplicate, conflicting, and partially populated records. The platform should show how it scores or qualifies a match, preserve the source identifiers, and route uncertain matches for review instead of silently merging people. Ask how corrections propagate when a source changes an identifier or retracts a record.
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Test provenance and missing data
Every clinically or operationally important fact should carry source, timestamp, and transformation context. Determine whether users can distinguish “not present in the connected sources” from “known to be absent.” Check how the system represents conflicting values, stale records, deleted data, and delayed feeds.
Test network completeness and response outcomes
CMS’s framework includes record-locator functionality, network-wide and targeted searches, timely query responses, and metrics for successful and unsuccessful queries. During a demonstration, measure source coverage, freshness, successful responses, empty results, timeouts, authorization failures, and partial responses. Ask for the denominator behind any success-rate claim and the period over which it was calculated.
The framework also describes a 60-day window for payer access to relevant data tied to a claim submitted in the last 60 days. This is a framework time window, not a measured platform-performance statistic; confirm how your workflows map to it.
4. Make privacy, security, consent, and audit testable
Healthcare data requires controls that follow the data through ingestion, graph construction, query execution, exports, and downstream applications. ONC guidance on healthcare APIs emphasizes privacy and security considerations, while its developer resources link to HIPAA information and a Security Risk Assessment tool. Use those resources to frame an evidence request, but do not treat a framework or certification as a legal determination for your organization.
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Identity and authorization
Require support for strong application and user identity, role or attribute-based authorization, service-account controls, and separation of administrative duties. Ask whether policy decisions can use patient, encounter, organization, purpose-of-use, location, and emergency context. Verify that permissions apply consistently to graph traversal, search suggestions, exports, subscriptions, and bulk jobs.
Consent and restrictions
Determine how consent directives, sensitive-data restrictions, segmentation rules, and revocations are represented and propagated when one record is linked to another. Test a user who is allowed to see demographics but not a restricted clinical category, and verify that the platform does not leak restricted facts through counts, relationship names, search results, or error messages.
Audit and incident response
CMS calls for purpose-of-use information, contextual access controls, and verifiable audit records. Ask for immutable or tamper-evident event records showing who or what accessed which data, under what purpose, what policy decision was made, and what was returned. Confirm retention, review workflows, alerting, export controls, clock synchronization, and procedures for investigating an incident. HITRUST certification, where offered, does not replace an organization’s HIPAA Security Rule obligations or its own risk assessment.
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5. Match data operations to the workflow
Classify each data flow as read-only or bidirectional, real-time or delayed, mandatory or optional, and low- or high-sensitivity. Microsoft’s healthcare reference architecture uses these characteristics to shape ingestion, management, persistence, enrichment, and open-interoperability strategies. Treat that architecture as guidance, then validate the behavior in your environment.
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- Read-only retrieval: Confirm caching, freshness indicators, source-of-truth links, and what happens when a source is unavailable.
- Bidirectional workflows: Identify which operations create or update records in systems of record, how write authorization is enforced, and how failures are reconciled.
- Low-latency decisions: Measure end-to-end time from source update to an answer, including identity matching and policy checks.
- Bulk analytics: Test large exports, asynchronous jobs, cancellation, throttling, and de-identification controls.
- High-sensitivity data: Verify encryption, segmentation, key management, masking, and least-necessary access for the relevant use.
6. Evaluate FAIR metadata and governance
HL7’s “FHIR for FAIR” guide frames findability, accessibility, interoperability, and reusability as principles for data and metadata management. FAIR is guidance, not a standard or a compliance certificate. Translate it into concrete platform requirements:
- Persistent identifiers for datasets, resources, and important graph assertions
- Rich, machine-readable metadata describing scope, ownership, update time, quality, and restrictions
- Qualified references that explain how one resource relates to another
- Provenance records for source, transformation, validation, and stewardship actions
- Clear licenses, permitted uses, retention rules, and contact points
- Documented community standards and terminology versions
Ask who owns each mapping and relationship rule, how changes are approved, and how a previous version can be reconstructed for an audit or reproducibility review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Prove performance and operational reliability
Do not accept an architecture diagram as proof of production behavior. Define service levels for the actual workflows: interactive patient lookup, network-wide search, subscription delivery, bulk export, and write-back if applicable.
| Operational dimension | What to measure |
|---|---|
| Latency | Percentiles for representative queries, including policy evaluation, identity resolution, and slow or unavailable sources |
| Throughput | Concurrent users, subscriptions, imports, and bulk jobs at expected and peak loads |
| Availability | Service uptime, dependency handling, maintenance windows, and behavior during partial outages |
| Recovery | Backup scope, recovery point and time objectives, replay of failed events, and reconciliation after restoration |
| Observability | Metrics, traces, logs, source-level health, query success/failure counts, and operator alerts |
| Scaling | How capacity changes with more sources, relationships, users, and historical data |
Require demonstrations with representative data volumes and realistic authorization policies. Have the platform distinguish a complete answer from a partial answer caused by a timeout or source outage.
8. Compare deployment, lifecycle, and cost
Include integration effort, migration, environment separation, data residency, support, portability, documentation, and upgrade responsibilities in the evaluation. Ask where data and backups are stored, which regions are available, how keys are controlled, and how data is returned if the contract ends.
Confirm the process for FHIR-version changes, profile updates, terminology releases, schema changes, and backward compatibility. Identify whether your team or the provider maintains mappings, connectors, validation rules, and custom extensions. Product-specific pricing and deployment terms are not established by the standards and guidance cited here, so obtain a written quote that separates recurring platform fees, implementation, data transfer, storage, support, and usage-based charges.
9. Use a consistent evaluation matrix
Score every candidate against the same representative data and workflow. A useful matrix has these dimensions:
| Dimension | Questions for every platform |
|---|---|
| Use-case and graph fit | Can it represent required entities, relationships, identities, constraints, and cross-source queries? |
| Interoperability | Which FHIR release, guides, US Core elements, USCDI version, terminologies, documents, bulk operations, subscriptions, and networks are live? |
| Quality and completeness | How are coverage, freshness, provenance, missing data, uncertain matches, and failed responses reported? |
| Privacy and governance | How are identity, authorization, purpose, consent, restrictions, audit, retention, and risk assessments implemented? |
| Performance and operations | What latency, throughput, availability, monitoring, recovery, and scaling evidence exists under realistic load? |
| Deployment and lifecycle | What are the integration effort, residency, migration, versioning, portability, support, and total costs? |
Set pass/fail gates for safety, privacy, required interoperability, and critical workflow latency before assigning weighted scores. A high aggregate score should not compensate for failure in a mandatory control.
10. Run a proof-of-value pilot
- Choose one workflow: Select a decision with a measurable outcome, such as locating a patient’s recent information across defined sources.
- Freeze the test contract: Document entities, relationships, profiles, terminology versions, user roles, purposes, latency targets, and expected error handling.
- Supply difficult cases: Include duplicates, conflicting values, missing fields, stale records, restricted data, source downtime, and terminology changes.
- Execute interoperability tests: Validate capability statements, required FHIR operations, US Core and USCDI elements, terminology mappings, documents, bulk exchange, and subscriptions where relevant.
- Exercise controls: Test allowed, denied, emergency, revoked-consent, and break-glass scenarios; inspect audit records and downstream exports.
- Measure outcomes: Record source coverage, freshness, match quality, latency percentiles, complete versus partial answers, successful and failed queries, and recovery behavior.
- Review lifecycle evidence: Ask the provider to demonstrate a terminology update, schema change, failed load, retry, rollback, and data export.
- Document gaps: Classify each issue as a blocker, accepted risk, contractual requirement, or future enhancement with an owner and date.
11. Questions to put in the procurement record
- Which FHIR release, profiles, and implementation guides are in production today?
- How are terminology versions, local mappings, and provenance represented and updated?
- Which upstream systems and data classes are available for the intended geography and workflow, and how is completeness measured?
- How does identity resolution work, and how are uncertain matches surfaced for review?
- How are purpose of use, consent restrictions, access policies, and audit evidence enforced across queries and downstream use?
- Can you demonstrate the intended low-latency workflows and distinguish real-time updates from delayed feeds?
- Which operations are read-only, and which create or update records in systems of record?
- How are bulk loads, failed queries, retries, source outages, schema changes, and terminology updates handled?
- What deployment, data-residency, support, portability, and pricing terms apply?
12. Warning signs that should stop or narrow a purchase
- “FHIR compatible” is stated without a release, profiles, capabilities statement, or test results.
- Identity matching produces a single answer but cannot expose uncertainty, source identifiers, or correction history.
- Coverage claims omit the connected sources, geography, freshness, or denominator for success rates.
- Consent and authorization are enforced only in the user interface, not in APIs, exports, subscriptions, or bulk jobs.
- Provenance is unavailable at the assertion level, making conflicting or stale facts impossible to investigate.
- Performance demonstrations exclude policy checks, source delays, partial outages, or realistic data volumes.
- There is no documented process for terminology, profile, schema, backup, or migration changes.
Make the decision on evidence, not branding
A healthcare knowledge graph earns consideration when it demonstrates the exact relationships your workflow needs, interoperates at the profile and terminology level, preserves provenance, enforces contextual privacy, and produces measurable results under failure as well as normal conditions. Require written commitments for unresolved gaps and involve clinical, informatics, privacy, security, legal, and operations stakeholders before treating a platform as ready for production.
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