# Healthcare AI Governance and Bias Auditing > Healthcare · AI Course · MED-9 > Source: https://ibl.ai/solutions/medical-healthcare/course/healthcare-ai-governance-bias-auditing > Last updated: 2026-08-25 **Stand up an AI governance program in a clinical organization — inventory, review committee, bias auditing across patient populations, and post-deployment monitoring.** ## The Short Answer **Clinical AI governance must audit bias across patient populations where the consequence is clinical harm, and most organizations cannot inspect vendor models to do it. ibl.ai runs inside the organization where you own all the code and the data — so bias auditing examines the actual deployed system on your own population.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Healthcare](https://ibl.ai/solutions/medical-healthcare) ## Course facts - **Level:** Intermediate - **Duration:** 6 hours across 8 modules - **Format:** Cohort workshop with audit labs - **Modules:** 8 - **Catalog code:** MED-9 - **Frameworks covered:** NIST AI RMF, ONC algorithm transparency, Section 1557, HIPAA ## What is this course about? Clinical AI governance has to cover tools nobody approved, assess bias across populations where the consequence is clinical, and monitor for drift after deployment. This course builds the inventory, the committee, a bias audit protocol, and the decommissioning process for a model that stops performing. ## Who is this course for? - Chief medical informatics officers - AI governance committees - Quality, safety, and health equity officers - Compliance and risk staff ### What do I need before starting? - Clinical or healthcare governance background - Familiarity with your organization's governance structures ## What will I be able to do afterwards? - Build a clinical AI inventory including unapproved and embedded tools - Stand up a governance committee with genuine authority - Require local validation before clinical deployment - Run bias audits across race, language, payer, and disability status - Decommission a model that is no longer performing ## What does each module cover? ### Module 1 — What clinical AI is already running? The inventory including tools embedded in systems nobody classified as AI. _(50 min)_ **Objectives** - Inventory clinical AI including embedded features - Discover unapproved clinical use - Capture the attributes governance needs **Topics:** Clinical AI inventory · Embedded vendor AI · Unapproved use · Attribute capture **Activity:** Run discovery in one service line and inventory everything found. ### Module 2 — Who sits on the governance committee? Composition spanning clinical, legal, IT, quality, and equity, with real authority. _(45 min)_ **Objectives** - Define committee composition and authority - Include health equity representation meaningfully - Design a review process clinicians will use **Topics:** Committee composition · Equity representation · Authority · Review process **Activity:** Draft the committee charter and run a mock review of two real tools. ### Module 3 — What must be validated before clinical deployment? Pre-deployment requirements including local validation on your own population. _(50 min)_ **Objectives** - Define pre-deployment validation requirements - Require local performance data - Set criteria that can block a deployment **Topics:** Pre-deployment requirements · Local validation · Blocking criteria · Exception handling **Activity:** Write the pre-deployment requirements and test them against a tool leadership wants. ### Module 4 — How do you audit for bias? Bias auditing across race, ethnicity, language, payer, and disability status. _(60 min)_ **Objectives** - Design a bias audit protocol - Select subgroups and comparison methodology - Interpret findings clinically **Topics:** Audit protocol · Subgroup selection · Comparison methodology · Clinical interpretation **Activity:** Run a bias audit on one deployed model across four subgroups. ### Module 5 — What do you do when the audit finds something? Response when a model performs worse for a subgroup, including when to stop using it. _(50 min)_ **Objectives** - Determine whether a disparity is acceptable - Design mitigation where possible - Decide when to stop using the model **Topics:** Disparity acceptability · Mitigation · Stopping decisions · Communication **Activity:** Work through the response to a real disparity finding. ### Module 6 — How do you monitor after deployment? Post-deployment monitoring for drift, including drift the vendor caused. _(45 min)_ **Objectives** - Design post-deployment monitoring - Detect performance drift - Detect vendor-caused model changes **Topics:** Post-deployment monitoring · Drift detection · Vendor changes · Alerting **Activity:** Design monitoring for one deployed model with drift alerting. ### Module 7 — What do clinicians and patients get told? Transparency to clinicians using the tool and to patients affected by it. _(40 min)_ **Objectives** - Determine clinician transparency requirements - Determine patient disclosure obligations - Draft the disclosure language **Topics:** Clinician transparency · Patient disclosure · Consent questions · Language drafting **Activity:** Draft clinician and patient disclosure language for one deployed tool. ### Module 8 — Building the bias audit protocol The workshop module: a complete, repeatable audit protocol. _(50 min)_ **Objectives** - Produce a repeatable audit protocol - Define audit cadence - Plan the decommissioning path **Topics:** Protocol construction · Audit cadence · Decommissioning · Documentation **Activity:** Complete the protocol and schedule the first audit cycle. ## What is the capstone project? **Clinical AI governance program with a bias audit protocol.** Produce a governance program: inventory with discovery findings, committee charter with equity representation, pre-deployment validation requirements with blocking criteria, a completed bias audit across four subgroups, monitoring design, and disclosure language. _Deliverable:_ A governance program with one completed bias audit and a scheduled audit cycle. ## How are learners assessed? - Inventory must surface embedded or unapproved AI the committee did not know about - Bias audit completed on real data across at least four subgroups - Pre-deployment criteria tested against a tool leadership actively wants ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Quality Improvement Agent](https://ibl.ai/solutions/medical-healthcare/agent/quality-improvement-agent) - [Compliance Training Agent](https://ibl.ai/solutions/medical-healthcare/agent/compliance-training-agent) - [Clinical Support Agent](https://ibl.ai/solutions/medical-healthcare/agent/clinical-support-agent) - [Knowledge Management Agent](https://ibl.ai/solutions/medical-healthcare/agent/knowledge-management-agent) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) — NIST. Governance structure and bias testing methodology. - [HealthIT.gov](https://www.healthit.gov/) — ASTP/ONC. Algorithm transparency requirements for certified health IT. - [Agency for Healthcare Research and Quality](https://www.ahrq.gov/) — AHRQ. Health equity measurement methodology. - [Augmented Intelligence in Medicine](https://www.ama-assn.org/practice-management/digital/augmented-intelligence-medicine) — American Medical Association. Professional guidance on clinical AI oversight. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 4's bias audit must run on real organizational data across real subgroups. A methodological overview without execution produces committees that believe they have audited when they have not. - Module 5 must include stopping as a genuine option. Governance that can only recommend mitigation cannot address a model that should not be used. - Module 1 will find embedded AI in the EHR and other systems that nobody classified. Build discovery to catch vendor features rather than only standalone tools. - Health equity representation in Module 2 must be substantive. A committee with an equity seat that never blocks anything is worse than none, because it provides cover. - Coordinate with MED-6 — local validation is covered there in clinical depth and this course should govern rather than re-teach it. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the Healthcare AI Governance and Bias Auditing course cover? Clinical AI governance has to cover tools nobody approved, assess bias across populations where the consequence is clinical, and monitor for drift after deployment. This course builds the inventory, the committee, a bias audit protocol, and the decommissioning process for a model that stops performing. It runs 6 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: Clinical AI governance program with a bias audit protocol. ### Who should take Healthcare AI Governance and Bias Auditing? It is written for Chief medical informatics officers, AI governance committees, Quality, safety, and health equity officers, Compliance and risk staff. Prerequisites: Clinical or healthcare governance background; Familiarity with your organization's governance structures. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for healthcare teams that cannot send work to a public AI tool. ### How do we get access to Healthcare AI Governance and Bias Auditing? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for healthcare cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Healthcare courses - [HIPAA-Compliant AI: PHI, BAAs, and Where the Data Lives](https://ibl.ai/solutions/medical-healthcare/course/hipaa-compliant-ai): What HIPAA actually requires of an AI deployment — the BAA analysis, Security Rule safeguards, minimum necessary, and the architecture that keeps PHI inside your boundary. - [Clinical Documentation with AI: Ambient Notes and Review](https://ibl.ai/solutions/medical-healthcare/course/clinical-documentation-with-ai): Deploy ambient documentation safely — accuracy in clinical language, the attestation requirement, note bloat, and measuring whether it returns clinician time. - [Medical Coding with AI: ICD-10, CPT, and Denial Prevention](https://ibl.ai/solutions/medical-healthcare/course/medical-coding-with-ai): AI-assisted coding that improves accuracy rather than just speed — code suggestion, documentation gap detection, denial prevention, and staying clear of upcoding. - [Prior Authorization Automation with AI Agents](https://ibl.ai/solutions/medical-healthcare/course/prior-authorization-automation): Compress the highest-friction administrative process in healthcare — payer requirement lookup, packet assembly, status tracking, and appeal drafting. - [Patient Education Agents: Health Literacy and Safety](https://ibl.ai/solutions/medical-healthcare/course/patient-education-agents): Patient-facing AI that explains conditions and discharge instructions safely — reading level, language access, scope boundaries, and symptom escalation. - [AI in Clinical Decision Support: Evidence, Limits, and Liability](https://ibl.ai/solutions/medical-healthcare/course/ai-clinical-decision-support): Deploy clinical decision support responsibly — evidence grounding, automation bias, transparency obligations, and where liability actually lands.