AI regulation

This week, world leaders gathered in France for the Paris AI Summit, underscoring AI's growing role in transforming healthcare. From streamlining administrative tasks to predicting clinical outcomes, AI promises significant benefits—but managing its deployment is a complex challenge.

The question is: Who should be in charge of AI, and how do we ensure it’s handled responsibly and sustainably?

As healthcare systems race to adopt AI, governance is key. IT manages tech assets, analytics handles data, and AI requires a specialized team. It’s not just about leadership but also understanding the hidden costs and challenges involved.

Healthcare systems allocate 4%-8% of their revenue to IT, but as AI, cybersecurity, and telehealth evolve, those costs will rise. Small systems struggle with limited resources, while larger ones face issues of scale and compliance.

AI governance is essential to avoid risks like biased recommendations, legal violations, and eroded trust. Proper governance ensures ethical, safe, and cost-effective AI deployment—tailored to the size and complexity of each system.

Small Healthcare Systems:

  • Keep AI simple with vendor-supported tools.
  • Integrate AI into existing clinical leadership, avoiding new roles.
  • Focus on high-impact, cost-effective AI solutions.
  • Use cloud-based tools to save on infrastructure.

Large Healthcare Systems:

  • Create an AI governance committee with leaders from multiple departments.
  • Build an AI Center of Excellence (COE) to manage deployment and monitoring.
  • Standardize AI use across departments for cost control and scalability.

Costs to Consider:

  • Query, integration, compliance, and infrastructure costs can add up quickly.
  • Small systems may struggle with these costs, while larger systems can negotiate better deals.

Practical Tips for Small Systems:

  • Prioritize AI with clear ROI—reduce documentation or optimize scheduling.
  • Use pre-built tools like Microsoft Copilot.
  • Look for grants and collaborate with others to share costs.

For Larger Systems:

  • Centralize AI governance with an AI COE.
  • Optimize cloud spending and consolidate vendors.
  • Invest in AI tools that directly drive revenue, like billing automation.

AI should be an investment that drives efficiency and patient care. For small systems, focusing on simple, vendor-supported solutions with phased rollouts ensures a manageable cost. Larger systems need to standardize their approach to ensure scalable and cost-effective AI adoption.

Effective AI governance is about trust, strategy, and sustainability. With proper planning, AI can become a powerful tool for healthcare’s future. 

Posted 11th February 2025

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