How Health Care Leaders Can Build a Robust AI Risk Management Framework

Artificial intelligence is rapidly moving from pilot projects to enterprise-wide deployment in health care. From clinical decision support to revenue cycle optimization, AI promises efficiency and improved outcomes. Yet it also introduces new risks — clinical, operational, ethical and regulatory — that hospital and health leaders cannot afford to overlook.

The Current Framework

In 2025, Health Canada emphasized that AI- and machine learning-enabled medical devices require rigorous life cycle oversight, including transparency, performance monitoring and real-world evaluation. While the proposed Artificial Intelligence and Data Act did not pass into law, the federal government has created an implementation guide to help organizations identify and manage AI-related risks in a structured way.

Against this backdrop, health care executives must know how to translate high-level principles into an operational, system-wide AI risk management framework.

Start With Governance and Accountability

Health care organizations should establish a multidisciplinary AI oversight committee that includes clinical leaders, compliance officers, IT, data scientists, risk managers and patient safety representatives. This body should define:

• Who approves AI tools before procurement.
• What evidence is required for adoption.
• How performance and bias are monitored post-deployment.
• When and how models are retrained or retired.

AI risk management is not a once-and-done review. It is an ongoing process. AI-enabled systems can evolve over time, requiring continuous evaluation rather than static validation.

Conduct Structured Risk Assessments

Before deployment, hospitals should conduct formal risk assessments tailored to AI-specific issues, including:

• Data provenance and representativeness.
• Model transparency and explainability.
• Clinical validation and performance in local populations.
• Bias, equity and unintended consequences.
• Cybersecurity and data privacy controls.

Health systems should integrate AI review into existing enterprise risk management and quality structures, rather than treating it as a siloed IT issue.

Embed Continuous Monitoring

Post-implementation monitoring is critical. AI systems may degrade over time due to data drift, population changes or evolving clinical practices. To ensure AI systems remain safe, equitable and aligned with ethical standards, health care organizations should implement:

• Real-time performance dashboards.
• Defined thresholds for acceptable error rates.
• Escalation pathways for adverse events.
• Regular bias and fairness audits.

Use URAC to Create a Risk Management Framework for AI in Your Health System

One practical answer is to align your program with an established accreditation body that has formalized standards for AI governance in health care. URAC offers an AI accreditation program designed to recognize responsible AI practices in health care.

The organization provides recognition that “clearly symbolizes your organization’s commitment to health care quality improvement,” emphasizing standards designed to promote transparency, accountability and responsible innovation. “We seek to inspire health organizations and communities to deliver a higher level of care,” says Shawn Griffin, MD, URAC President and CEO.

For health care leaders, partnering with URAC can help operationalize AI risk management through key features embedded in the process.

• Strong AI governance: The program requires and supports you in creating a well-defined structure for AI initiatives.
• Risk management prioritization: A comprehensive risk assessment is a central component of accreditation.
• Focus on health equity: Accreditation requires organizations to actively identify and mitigate biases in their AI models.
• Life cycle management: The process includes everything from initial data inputs and model development to ongoing performance monitoring and adjustments over time.
• Validation and transparency: URAC’s standards require organizations to validate AI performance to ensure accuracy and reliability, and to use it transparently.

As a reputable independent third party, URAC accreditation provides an external validation of your health system’s commitment to responsible AI use. This can enhance trust among patients, providers and payers and serve as a competitive differentiator in the market. Accreditation can be completed in as little as six months.

Frequently Asked Questions

Here are common questions regarding AI risk management framework in health systems.

Who should own AI risk management in a health system?

Organizations should create a multidisciplinary governance committee with executive accountability.

Is accreditation required to deploy AI?

While not legally required in most cases, accreditation strengthens credibility and oversight.

How often should AI systems be reviewed?

AI systems should be continuously monitored and undergo formal reviews at defined intervals.

Integrate AI Risk Management Into Enterprise Strategy

By integrating AI risk management into existing governance structures and aligning with recognized accreditation standards, health care systems can scale innovation while maintaining clinical integrity and public trust. AI is now operational, not experimental, in health care. The organizations that thrive will be those that treat AI governance as core infrastructure, building trust and accreditation as they go.