From AI Governance Policies to Practical Evidence: A Healthcare and Insurance Perspective

Expert Contribution

By Vinod G.  |  Founder, Goshen AI Labs

Introduction: Governance must work beyond the policy document

Healthcare and insurance organisations are increasingly exploring artificial intelligence to improve documentation, streamline operations, support risk assessment, and assist decision-making. These applications offer potential benefits, but they also introduce questions about accountability, reliability, privacy, human oversight, and the ability to explain decisions when challenged.

A common governance challenge is the gap between having an AI policy and demonstrating that governance works in practice. An organisation may have an approved Responsible AI policy and documented principles, yet struggle to produce consistent evidence of how a particular AI system is controlled. Effective AI governance requires operational controls, assigned responsibilities, and evidence that those controls are implemented and reviewed.

1. Start with the actual use case

AI governance should begin with understanding what a system does, where it is deployed, and how its outputs affect people and business decisions.

Consider an AI-assisted clinical documentation system that converts consultation transcripts into draft clinical notes. Risks may include omitted clinical details, incorrect medication information, inaccurate summaries, or unsupported statements. An insurance claims prioritisation system presents different risks: it may influence which claims receive attention, how cases are escalated, or where investigation resources are allocated. Poorly designed prioritisation could overlook urgent claims or encourage excessive reliance on automated recommendations.

Both systems use AI, but their intended purposes, failure modes, and appropriate controls differ. Organisations should maintain an AI inventory recording each system’s purpose, deployment context, data involved, business owner, level of human involvement, and potential consequences of failure. This inventory provides the foundation for proportionate risk assessment.

2. Translate risks into operational controls

Identifying a risk is only the beginning. Organisations must decide how to prevent, detect, or respond to it.

For AI-assisted clinical documentation, controls might include testing outputs against representative clinical scenarios, requiring clinician review before draft notes become part of the official medical record, checking for critical omissions and unsupported statements, defining escalation procedures, and monitoring recurring errors.

For insurance workflows, controls might include validating prioritisation against business requirements, checking performance across relevant claim categories, preserving records of the information and outputs that informed prioritisation, defining when staff must override or escalate recommendations, and monitoring errors and exceptions.

These are examples, not universal requirements. Controls should reflect the actual system, its risk assessment, applicable obligations, and operating environment. The aim is to connect each material risk to a control, an accountable owner, and evidence that the control operates.

3. Build evidence that supports assurance

A control is difficult to assure without reliable evidence that it exists and works as intended. Depending on the system, evidence may include risk assessments, test results, approval records, human-review procedures, incident logs, monitoring reports, and corrective actions.

A policy stating that clinicians must review AI-generated notes does not, by itself, demonstrate that the review process operates effectively. An organisation also needs an appropriate way to check whether the process is followed, investigate exceptions, and address recurring failures.

Evidence should be proportionate to risk and designed with privacy, access controls, retention requirements, and data minimisation in mind. Healthcare and insurance records may contain sensitive personal information, so governance evidence should not become an uncontrolled repository of patient or customer data.

On frameworks: ISO/IEC 42001 provides a management-system framework for establishing, implementing, maintaining, and continually improving an AI management system. Organisations should evaluate their specific legal and regulatory obligations as well; adopting a framework does not automatically establish compliance with every applicable requirement.

4. Make AI-assisted decisions defensible

When an AI-assisted recommendation is challenged, an organisation should be able to reconstruct the relevant decision process to an appropriate level of detail. This includes understanding the system’s intended role, the information considered, the recommendation produced, known limitations, and the human review or intervention that occurred.

A practical approach is to connect five elements:

The level of detail should fit the use case. The goal is not excessive paperwork or a lengthy report for every AI-assisted decision. It is to preserve sufficient, reliable evidence to support review, accountability, and investigation.

5. A practical starting point

Healthcare and insurance organisations do not necessarily need to begin with a large governance transformation programme. They can select one AI use case, document its purpose and deployment context, identify material risks, map those risks to controls, assign accountability, and determine what evidence should be retained.

The organisation can then test whether the controls work in practice, identify gaps, and establish monitoring and improvement processes. This approach helps teams move from abstract governance principles to tangible implementation and generates lessons that can inform wider governance efforts.

Conclusion

Responsible AI in healthcare and insurance requires more than policies, checklists, or framework adoption. It requires a clear connection between intended use, risk assessment, operational controls, accountable human oversight, and reliable evidence.

When organisations build that connection, they are better positioned to evaluate AI systems, investigate problems, demonstrate governance practices, and explain decisions under scrutiny. Effective AI governance is not just about documenting what an organisation intends to do. It is about demonstrating what it actually does, why it does it, and how it responds when things go wrong.

This article is an expert contribution. The views expressed are those of the author. AI Governance Desk welcomes practical contributions from practitioners in regulated fields.