Building an Audit-Ready AI Governance Program for a [ORGANIZATION TYPE]
Last updated: April 2026
From shadow AI inventory to ONC HTI-1 compliance, algorithmic bias auditing, and NIST AI RMF implementation — in [ENGAGEMENT DURATION] months.
The Situation
[CLIENT DESCRIPTION: Brief, anonymized description of the organization — type, size, services, states of operation. No identifying details.]
Like most organizations at this stage, the client had invested in AI capabilities well ahead of governance infrastructure. [SPECIFIC AI TOOLS IN USE: describe category — e.g., "predictive clinical decision support integrated into their certified EHR," "AI-assisted prior authorization review," "AI-enabled diagnostic imaging tools"] were in active clinical and operational use, but the governance program consisted of informal review by IT and a general technology committee that had never formally evaluated an AI submission.
Three converging pressures drove the engagement:
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[PRIMARY PRESSURE: e.g., ONC HTI-1 enforcement deadline]
[DESCRIPTION of specific regulatory pressure and the client's current gap against it.]
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[SECONDARY PRESSURE: e.g., Upcoming URAC health plan accreditation renewal]
[DESCRIPTION of how this created additional compliance exposure or timing constraint.]
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[TERTIARY PRESSURE: e.g., Board-level concern about algorithmic bias liability following California AB 316]
[DESCRIPTION of the board or leadership concern and how it created urgency.]
The initial gap assessment confirmed what the leadership team suspected but had not formally documented: the organization had no centralized AI inventory, no formal pre-implementation approval process, and no source attribute documentation for any of the predictive DSIs running in their certified EHR. The gap was significant — but addressable within a defined timeline tied to the regulatory deadlines.
The IHS Approach
IHS structured the engagement in three phases, sequenced against the client's most pressing regulatory deadline as the first forcing function.
Phase 1: Shadow AI Inventory and Gap Assessment ([DURATION] weeks)
[DESCRIPTION of shadow AI scan methodology, what was discovered, how many AI tools were identified, categories of tools found, initial regulatory mapping against FDA/ONC/CMS/state law requirements. Describe the gap analysis output and how the remediation roadmap was prioritized.]
The inventory phase surfaced [NUMBER OR DESCRIPTION: e.g., "significantly more AI tools than leadership had formally approved"] — a finding consistent with the industry-wide pattern where over 90% of healthcare organizations lack automated AI product monitoring and 51% rely on ad hoc discovery. [SPECIFIC FINDING about shadow AI discovered in the client's environment, without identifying details.]
Phase 2: Remediation and Documentation Build ([DURATION] weeks)
The remediation phase produced the core governance documentation package:
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AI Governance Charter
[DESCRIPTION of charter development — committee composition, approval workflow designed, decision rights established. Note any specific challenges or design decisions.]
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Intervention Risk Management Records
[DESCRIPTION of IRM record development — how many algorithms were documented, what the FAVES criteria review revealed, any algorithms that were suspended or required remediation before approval.]
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ONC HTI-1 Source Attribute Documentation
[DESCRIPTION of source attribute documentation work — how many predictive DSIs required documentation, how the documentation was structured for clinical end-user accessibility, how vendor relationships were engaged to obtain training data transparency information.]
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Algorithmic Bias Impact Assessments
[DESCRIPTION of bias audit methodology, demographic subgroups evaluated, any material findings, remediation steps taken or recommended. Note any findings that were escalated to the governance committee.]
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AI Vendor BAA Remediation
[DESCRIPTION of BAA review findings — how many agreements were identified as deficient, what specific gaps were found, how vendor negotiations were structured, outcome of renegotiation process.]
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State AI Law Compliance Mapping
[DESCRIPTION of applicable state laws, how the compliance mapping was structured, any specific operational changes required to satisfy state requirements (e.g., Texas SB 1188 practitioner review protocol).]
Phase 3: Pre-Assessment and Validation ([DURATION] weeks)
[DESCRIPTION of mock survey / pre-assessment process, what gaps remained after Phase 2, how corrective actions were addressed before formal assessment, outcome of the formal validation process.]
Results
Regulatory Outcomes
- [REGULATORY OUTCOME 1: e.g., "ONC HTI-1 source attribute documentation complete for all predictive DSIs before February 2026 enforcement discretion deadline"]
- [REGULATORY OUTCOME 2: e.g., "NIST AI RMF Govern and Map functions implemented — organization moved from 0% to documented framework implementation"]
- [REGULATORY OUTCOME 3: e.g., "Colorado AI Act algorithmic impact assessment requirements satisfied ahead of June 2026 enactment date"]
- [REGULATORY OUTCOME 4: e.g., "URAC health plan accreditation renewed with AI governance documentation submitted as evidence for health equity and quality management standards"]
Operational Outcomes
[DESCRIPTION of operational changes — how the AI governance program changed day-to-day operations, what the approval workflow looks like now vs. before, how clinicians interact with AI transparency documentation, any clinical or quality improvements observed.]
[CLIENT QUOTE: Direct quote from a named or titled representative at the client organization, if available. If not available, use bracket notation: "[Quote from [TITLE] regarding the value of the governance program and the IHS engagement process.]"]
[TITLE, Organization Type]
Why IHS Was the Right Fit
[DESCRIPTION of why the client chose IHS over alternatives — what specific capabilities or experience were determinative. This section should reference the URAC/ACHC accreditation crossover if applicable, the mid-market pricing advantage if relevant, or the healthcare-exclusive practice focus.]
The client's existing URAC accreditation relationship with IHS proved materially valuable: the health equity and quality management infrastructure already documented for URAC purposes provided the foundation for the algorithmic bias auditing and governance committee charter, reducing Phase 2 timeline by [ESTIMATED REDUCTION] weeks. This is the core IHS positioning advantage — AI governance built on a compliance infrastructure that already exists, rather than created from scratch.
[ADDITIONAL DIFFERENTIATOR: Any specific IHS capability that proved uniquely valuable in this engagement — e.g., state AI law expertise, FDA SaMD pathway knowledge, ONC HTI-1 implementation experience.]
Ready to Build Your AI Governance Program?
The gap between AI committee formation and operational governance implementation is where regulatory exposure lives. IHS closes that gap with documented programs that satisfy FDA, ONC, CMS, and state requirements — and that hold up when auditors ask for evidence.
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