Healthcare AI · Regulated Industries
Ohana Consulting delivers AI solutions across patient access, care coordination, and clinical engagement — and the governance infrastructure to ensure they're compliant, auditable, and trusted by the clinicians and patients who depend on them.
What we do
Deploying AI in a regulated environment is not the same problem as deploying AI. The clinical workflow, the interoperability requirements, the compliance obligations, and the ethical accountability don't go away because a model is involved — they get harder. We've been living inside these problems for twenty years.
Governance that spans the development cycle and runtime — not a checkbox exercise. Structured context methodology to ensure AI systems in clinical settings are auditable, fair, and aligned with CHAI, HIPAA, and NIST AI RMF.
Learn more →Chronic condition management, care plan adherence, and multi-provider coordination powered by AI — built on FHIR and SMART on FHIR so the data moves with the patient, not the institution.
Learn more →AI-driven triage, appointment optimization, and prior authorization guidance that reduces friction for patients and administrative burden for staff — while staying inside payer and regulatory guardrails.
Learn more →FHIR, SMART, NCPDP SCRIPT, HL7, and 21st Century Cures compliance aren't obstacles — they're the foundation. We design integrations that work across EHRs and payer systems without proprietary lock-in.
Learn more →Experience
The case for AI in healthcare is not new. Neither are the reasons it fails — misalignment with clinical workflow, inadequate governance, and standards that were designed for human readers rather than AI systems. Understanding all three layers at once is rare. It's what Ohana brings.
The governance problem
CHAI, HIPAA, NIST AI RMF, and clinical ethics frameworks are written in natural language for human reviewers. AI systems operate on structured data and explicit constraints. The gap between them is where compliance breaks down — and where patient safety risk accumulates.
Ohana's approach to AI governance in regulated environments uses structured context methodology to bridge that gap: governance requirements travel from the standards body to the AI agent, intact and machine-readable. The result is auditability that doesn't require manual review of every inference.