Interoperability and AI are a strategy problem - not an IT project
Health systems are hitting a wall: workforce shortages are growing, the population is aging, and the current operating model can't keep up. That was the clear message from Dan Liljenquist, chief strategy officer at Intermountain Health, during a keynote on AI and interoperability at the 2026 HIMSS Global Health Conference & Exposition in Las Vegas.
His stance is blunt: the system we built was for another era. If leaders keep treating data and AI as back-office tech, they'll miss the only lever big enough to change the trajectory.
Why the urgency
Clinicians are leaving. Demand is rising. Traditional workflows and tools weren't designed for this load, and incremental fixes won't bridge the gap.
Two forces create room for real change: a regulatory push from CMS around data exchange and the speed of AI advancement. Together, they open the door to new models of care and operations.
Interoperability isn't "we have an API" - it's computable data
APIs and standards are necessary, but not sufficient. You can move data with FHIR and still end up cleaning it by hand because values, codes, and context aren't aligned.
Computable data means standardized, structured, and consistently mapped so systems can understand and act on it without manual effort. That's the foundation AI needs to work at scale.
What Intermountain Health is building
Intermountain, a nonprofit with 34 hospitals across six states and nearly 70,000 employees, is standing up a unified data layer in the cloud. They ingest EHR data daily, normalize it with common semantic models, and make it usable for analytics and AI.
The target is clear: reduce the estimated $750 billion spent each year on administration driven by messy data. Free up resources for care by standardizing the data model and automating the work that doesn't need a clinician.
Where AI can move the needle first
- Medication management: safer orders, better reconciliation, and timely interventions.
- Chronic disease: earlier risk signals and outreach based on consistent longitudinal data.
- Administrative automation: prior auth, coding, and denials management grounded in clean source data.
- Population health: accurate registries, risk stratification, and program targeting across settings.
Make interoperability your strategic platform
This isn't a compliance checkbox. It's the operating system for how your organization will deliver care, run revenue cycle, and manage risk. Treat it that way and the ROI compounds; treat it as a project and you'll stall in pilots.
Practical playbook for health leaders
- Set outcomes up front: pick 3-5 metrics (e.g., LOS, readmissions, denials, time-to-appointment) and tie every data and AI initiative to them.
- Build for computability: centralize EHR and claims data in the cloud, map to common clinical terminologies, and enforce a single, consistent data model.
- Go beyond FHIR: pair FHIR APIs with identity resolution, data quality rules, provenance tracking, and a master patient index so records actually match.
- Harden governance: define data stewardship, PHI safeguards, model validation, and human-in-the-loop review for clinical use cases.
- Start with high-yield use cases: medication safety, chronic care pathways, prior auth, and coding assistants. Prove value in 90-day cycles.
- Operationalize measurement: track clinician time saved, admin cost per claim, adverse drug events averted, and gap closure rates - then reinvest winnings.
- Upskill the workforce: train clinical, operations, and IT leaders on data literacy and AI oversight to scale adoption responsibly.
Bottom line
AI and interoperability are not technical problems to hand off to IT. They are the strategic engine for future healthcare - the only path big enough to meet demand with fewer hands and better outcomes.
Relevant resources
CMS Interoperability initiatives
HL7 FHIR standard
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