Across fall and early winter 2023, The Health Management Academy conducted research and spoke with over a hundred health care leaders about AI governance. These leaders included C-Suite executives, informatics officers, innovation leads, and data experts across Leading Health Systems (LHS). The following insights are the net result of this research and interactions. AI Catalyst will provide additional guidance and discussion opportunities on AI governance in 2024.
For all the specialized, technical language about it, when Leading Health Systems (LHS) talk about AI governance, what they’re really looking for is a way to make sense of and reassert control over an unwieldy, volatile, and evolving technological trend. And that’s fair. Whether we talk about ideal committee structures, parameters for AI pilot life cycles, or methods for data bias mitigation, discussion around AI governance is grounded in a desire for clarity, actionability, and accountability.
IBM defines AI governance as the ability to direct, manage, and monitor AI activities across your organization. Simple words that underline a far-from-simple task. That’s why THMA has put together six action-steps for structured, impactful AI governance.
Download PDF for more information on each step.
Six action-steps for structured, impactful AI governance:
Pinpoint and socialize an organizational definition of AI
Treat baseline AI literacy as a prerequisite for effective implementation of AI governance
Audit existing procurement and governance frameworks at your organization that AI adoption can “plug into”
Build awareness of AI-specific ethical challenges into compliance training
Start your AI governance journey by preparing to navigate worst-case scenarios
Orient your governance structure to rapid evolution in the AI space
Download PDF for more information on each step.