Services built for data trust, standards, and AI readiness.

Phase 1 is a fixed-scope, fixed-fee data audit lasting 3–6 weeks. We profile your clinical and administrative data, assess your standards posture (OMOP, FHIR), evaluate governance maturity, and identify the specific issues blocking your reporting, compliance, and AI initiatives. At the end you receive executive-ready scorecards by domain, a prioritized findings report tied to business impact, and a phased modernization roadmap. You walk away with a clear, defensible picture of where your data risk actually sits — whether you continue with us or not.

No. Phase 1 is entirely read-only. We work with read-only SQL access, encrypted data extracts over SFTP, or client-run scripts we provide — whichever method your IT and security teams are comfortable with. We never require write access to production systems and we don’t store PHI locally.

Not at all. Phase 1 is designed to stand on its own. Many organizations use the findings to guide their internal teams or inform vendor decisions without engaging further. Phases 2 and 3 are driven entirely by what the audit uncovers — we never pre-sell implementation before understanding your reality.

Two key differences. First, we are deeply specialized in healthcare data standards — OMOP, FHIR, HL7, clinical vocabularies — not general IT delivery. Second, our model is fixed-scope and outcome-oriented, not time-and-materials. We price clarity and risk reduction, not headcount. You know exactly what you’re getting before we start, and we don’t create unnecessarily long engagements.

Yes, both. A Business Associate Agreement (BAA) is mandatory before we access any PHI — we can sign yours or provide our own. An NDA is standard for all engagements and is signed before any scope discussions. We also carry professional liability (E&O) and cyber liability insurance, which most healthcare organizations require before onboarding any data vendor.

They solve different problems. OMOP prepares your data for analytics and AI — it creates a consistent, patient-level structure that makes reporting, cohort building, and modeling repeatable. FHIR makes that prepared data accessible and shareable with other systems. If your goal is purely internal analytics, OMOP alone may be sufficient. If you need to exchange data with partners, regulators, or downstream applications, FHIR becomes essential. Most mature organizations benefit from both, but we never recommend more than your use case actually justifies.

Technically yes — but the model will almost certainly underperform or fail in production. The most common causes of AI failure in healthcare are not algorithmic — they are data problems: missingness in key features, inconsistent clinical coding, broken event linkages, and unit drift that quietly corrupts inputs. Building on unvalidated data means you’re amplifying problems you don’t know exist. VitalFrame’s position is simple: data readiness is AI safety.

We work across the full range of clinical and administrative healthcare data — EHR data (encounters, diagnoses, procedures, medications, labs, vitals), claims and billing data, pharmacy, imaging metadata, and provider and organization master data. For Phase 1 we typically need read-only access to your core clinical and administrative tables. We work with whatever coding systems are in place: ICD-10, CPT, LOINC, RxNorm, SNOMED, and local or custom vocabularies.

Phase 1 is fixed-fee, ranging from $15,000 to $45,000 depending on the number of data sources, data volume, and complexity of your environment. Pricing scales with data complexity — not headcount, not meeting count, not how much chaos we uncover. Smaller organizations with a single EHR system sit at the lower end; large multi-system environments or government entities sit at the higher end. We confirm scope before proposing anything.

Phase 3 is a subscription that keeps your data quality stable after the initial remediation work. It includes automated DQ checks on a monthly or quarterly cadence, alerts for new issues or threshold breaches, vocabulary drift detection (new unmapped codes, deprecated terms), pipeline freshness and SLA monitoring, model and cohort stability signals where applicable, and executive reporting dashboards. It’s designed to catch degradation before it becomes visible in your dashboards or reports.