AI is here, and it’s not going away. Like groundbreaking technologies that have come before it – cloud computing, smartphones, even the Internet itself – AI is poised to transform industries and unlock capabilities that seemed impossible just a decade ago. The rapid adoption of AI, coupled with the speed at which it is evolving, requires that meaningful guardrails be put in place in order to safely and ethically harness its power. Across industries, organizations are building these guardrails through comprehensive AI governance frameworks that guide the evaluation, implementation, and measurement of AI tools.
What does AI governance mean?
AI governance encompasses the processes, policies, and tools used to manage AI systems throughout their lifecycle, ensuring they are developed, deployed, and used responsibly. A successful AI governance program maximizes the benefits of AI while mitigating potential risks like bias, unfairness, and harm.
By establishing an AI governance framework, you’re essentially creating a blueprint for how your organization goes about evaluating, implementing, and measuring AI tools. This helps ensure that everyone across the organization is on the same page about your AI policies, so you can strategically use AI where it has the most impact and lowest risk.
Why do I need an AI governance framework?
The AI landscape is changing every day. Models are getting stronger, capabilities are expanding, and new use cases are being imagined. In 2024, the global market for AI in healthcare grew by 42%. Given the complexity and rapid pace of change, trying to navigate AI initiatives without a guiding philosophy and clearly defined success criteria is nearly impossible.
An AI governance framework allows your organization to more efficiently and effectively evaluate AI tools by ensuring every department follows the same guidelines and that AI tools are not implemented in silos.
AI governance is arguably even more important in healthcare than in other industries. With AI potentially influencing clinical decision making, organizations must have a well defined strategy for how AI can or cannot be used, how to make sure there’s clinical oversight when necessary, and how to avoid any HIPAA-related data privacy concerns.
An AI governance framework serves as the guiding principle to make sure AI is being implemented safely and strategically.
How do I create an AI governance framework?
If your organization doesn’t have any AI governance policies in place, don’t worry – you’re not alone. According to the Center for Connected Medicine and KLAS Research, as of 2024, only 16% of healthcare organizations have a system-wide AI governance framework.
Because AI technology is so new, and healthcare organizations are so complex, there is no “one size fits all” approach to creating an AI governance framework. Most organizations are taking cues from other groups in the industry, reviewing academic literature, and figuring it out as they go. AI governance frameworks are living documents – so it’s best to get started now and continue to adjust it as necessary.
There are 5 key considerations you should take into account when developing an AI governance framework. I’ve summarized them below, but this eBook goes into more detail.
Assemble the right team
Determine who needs to be involved in the process. IT and legal will need to be involved, but it’s critical to have clinical and operational involvement as well. They’re the ones who know what problems frontline care teams are facing and will provide more perspective on how they can be solved with AI.
Balance innovation with risk
Building an effective AI governance framework is an exercise in balance. You want guidelines that help you maintain an acceptable level of risk without stifling innovation.
Don’t try to solve for every use case
With how quickly AI is evolving, there’s no way your framework will account for every possible use case. Focus on making sure the framework meets your organization’s current needs, and establish a regular cadence for reviewing and updating your framework as the landscape changes.
Focus on the business outcomes – not the technology
It’s easy to get lost in all the bells and whistles of AI tools, instead of focusing on the business problem you’re solving. Keep yourself grounded in the outcome you are trying to achieve, not just technology for the sake of technology.
Establish specific success metrics and a process for analysis
When building your governance framework, establish specific safety and performance metrics that can be used to measure the effectiveness of a potential AI solution. Having these guidelines will make it easier to compare different AI initiatives and to judge the success after implementation.
Conclusion
Healthcare is traditionally slow to adopt new technologies. But with looming staff shortages, increasing demand from patients, and razor-thin margins, health systems cannot afford to wait to take advantage of AI. AI-powered tools can help leaders solve significant operational challenges, like driving strategic surgical growth or unlocking inpatient capacity.
Many AI healthcare tools are deeply embedded into clinician and staff workflows, making governance more challenging – and more critical. A strong and well-thought-out AI governance framework helps you mitigate risk, align technology with business outcomes, and more accurately evaluate potential AI solutions.For a detailed guide, download our eBook: Approaching AI governance: a how-to guide for healthcare organizations.
