Make your EHR + claims data reliable enough to power AI, reporting, and interoperability.
VitalFrame helps hospitals, payers, and health authorities quantify data maturity, surface data-quality risk, align to gold-standard models (OMOP) and exchange patterns (FHIR), and keep data health stable over time.
Your path to decision-grade data
A clear, standards-aligned pipeline that turns messy source data into reusable assets for interoperability, reporting, and safer AI.
1 · Source data
EHR · Claims · Labs · Pharmacy
Fragmented systems, inconsistent coding, missingness, and unclear definitions.
2 · Data Quality
Profiling + remediation
Quantify DQ risk and prioritize fixes tied to reporting, compliance, and AI blockers.
3 · Standardize
OMOP-aligned structure
Reusable patient-level datasets that enable consistent analytics and cohort building.
4 · Interoperate
FHIR-aligned outputs
Shareable, system-agnostic resources — Patient, Encounter, Condition, and more.
Why this matters
When data is standardized and reliable, teams unlock higher-value use cases with faster delivery, clearer governance, and lower operational risk.
Identify high-risk cohorts, close care gaps, and support chronic-disease programs using consistent longitudinal records.
Detect anomalies, duplicate billing patterns, upcoding signals, and suspicious provider behavior with cleaner claims + clinical joins.
Reduce reporting friction and improve exchange readiness by standardizing vocabularies, units, and mapping logic.
Spend less time fixing data and more time building dashboards, measures, and repeatable metrics across programs.
Create reusable cohorts and consistent feature definitions that support outcomes research and quality improvement initiatives.
Improve model reliability by addressing missing labels, unit drift, coding inconsistencies, and event-linking issues before they become production failures.
Three-phase approach
Start with a fixed-scope audit. Modernize only where findings justify it. Sustain quality over time.
Data Audit & Maturity Assessment
Quantify maturity and data-quality risk; identify what's blocking reporting, compliance, and AI.
Modernization & AI Readiness
Remediate high-impact issues; standardize structure and outputs to OMOP and FHIR.
Ongoing Data Health Monitoring
Automated checks, alerts, and dashboards to keep quality stable as systems and data evolve.
Want a clear starting point? Start with Phase 1 to establish maturity and data-quality risk, then expand only where the findings justify it.
Book a discovery call