Make Mandates Matter: Build Enterprise AI on Interoperability

Treat mandated changes as a strategic investment in AI success

Federal mandates are forcing a new baseline for interoperability. Treat that spend as more than compliance-it's the foundation for AI that actually works at scale across your organization.

If your data can't move, your AI won't move the needle. Use the required work to modernize how data flows, how it's governed, and how it's trusted across care settings.

Why mandates are a springboard for AI

• Standardization creates reusable data. FHIR APIs, eventing, and TEFCA participation give AI teams consistent inputs instead of brittle, one-off feeds.
• Data liquidity reduces time-to-value. Less custom ETL and fewer point-to-point integrations mean faster model development and deployment.
• Governance lowers risk. Consent, provenance, and auditing built for compliance also protect AI pipelines and outputs.
• Identity and context improve accuracy. Solid patient matching and provider attribution cut false positives and data drift.

Persistent barriers you still need to solve

• Fragmented patient and member identifiers across EHRs, payers, and ancillary systems
• Legacy interfaces that throttle real-time data and force batch jobs
• Inconsistent consent capture and sharing rules across entities
• Poor data quality (duplicates, missing values, conflicting codes)
• Unstructured notes without reliable NLP pipelines
• Security and privacy gaps when PHI moves to new AI services

Align AI goals with compliance-driven data work

• Start with the outcomes. Pick 2-3 enterprise AI use cases (e.g., readmission reduction, prior authorization automation, ambient documentation) and map the exact data elements each needs.
• Build "minimum viable" data products. Define FHIR-based contracts for the features your models require: identifiers, timestamps, provenance, and coding systems.
• Stand up real-time feeds where it matters. Event streams for admissions, vitals, meds, and orders enable timely predictions and interventions.
• Invest in patient and provider mastering. Use MDM and referential matching to unify identities across the enterprise.
• Track lineage. Log where every feature came from, how it was transformed, and who accessed it-critical for audits and model debugging.
• Operationalize consent. Centralize consent policies and enforcement so AI apps inherit the right access automatically.
• Engineer for safety. PHI minimization, differential access, de-identification for model training, and continuous monitoring for drift and bias.

Examples that start with interoperability

• Readmission risk across a network: Combine inpatient, outpatient, and payer data via FHIR and event streams to score risk daily and trigger outreach.
• Ambient clinical documentation: Use structured notes, orders, and problem lists to validate transcripts and reduce error rates.
• Prior authorization acceleration: Pull required clinical facts directly from EHRs into payer workflows to auto-approve standard cases.
• Sepsis early warnings: Stream vitals, labs, and meds with clear provenance to support alerts that clinicians trust.
• Capacity and throughput: Share ADT events and bed status in near real time to predict bottlenecks and improve staffing.

A practical 90-day plan

• Name an executive sponsor and a cross-functional squad (clinical, data, privacy, security, operations).
• Inventory critical data sources and gaps for two priority AI use cases.
• Define FHIR data contracts for those use cases (entities, codes, ownership, latency, quality thresholds).
• Pilot a streaming feed for one high-impact signal (e.g., ADT or labs) with lineage and consent controls.
• Measure outcomes against a baseline: time-to-data, model lift, alert precision, clinician adoption.
• Create a repeatable template for onboarding the next use case.

What good looks like

• Standard interfaces (FHIR, events) available enterprise-wide with documented SLAs
• Identity resolution above agreed accuracy thresholds for patients and providers
• Feature stores with lineage, quality scores, and access controls
• Model performance monitored in production with bias and drift checks
• Clinical workflows show measurable improvements in time, cost, and outcomes

Policy context and helpful references

For context on why this matters and how to align efforts:

• ONC TEFCA overview - nationwide exchange framework and QHINs
• CMS Interoperability initiatives - patient access, payer-to-payer, and prior authorization rules

Leaders across the industry, including Don Woodlock of InterSystems, continue to share practical playbooks for connecting interoperability work with AI programs. The common thread: build once on standards, reuse everywhere, and prove value in the workflow.

If your teams need structured upskilling on AI implementation, data pipelines, and model safety, explore role-based learning paths at Complete AI Training - courses by job.