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Demetri Giannikopoulos

AI Governance Best Practices and Pitfalls

Data Management, Information Technology, Workflow
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Best Practices and Common Pitfalls in Effective AI Governance in Healthcare

AI governance isn’t about checking boxes. It should focus on building trust in the technology, empowering health systems to effectively use AI to improve efficiency and outcomes. Instead of simply complying with regulations, AI governance should actively contribute to a positive and safe environment for AI implementation.

As Sunil Dadlani, EVP, Chief Information & Digital Transformation Officer, Chief Cyber Security Officer at Atlantic Health System, said: “The challenge [with governance] is to find the right balance of enough regulation and compliance framework without inhibiting or slowing down the innovation potential that comes with these technologies.” 

The good news? You likely already have the infrastructure in place to manage AI responsibly at your facility, because how AI functions today – as clinical support – is similar to other established technologies. 

Leveraging existing governance committees can help streamline decision-making, alleviate process burden and allow for smoother integration. Still, even with an established infrastructure there are a few best practices to follow as well as pitfalls to avoid.

Expand On A Solid Governance Foundation

  • Integrate AI Expertise: Don’t build a separate silo for AI. Instead add AI expertise to your existing clinical, operational and data governance processes. 
  • Leverage Existing Workflows: This streamlines the AI adoption process and ensures base-level governance and familiarity for those involved.
  • Build a Coalition: Creating a multidisciplinary change management coalition fosters a shared understanding of AI’s impact and facilitates smooth adoption.
  • Embrace Continuous Improvement: AI is constantly evolving; therefore, ongoing evaluation of your processes will ensure they can adapt to new use cases.
  • Focus on Transparency and Explainability: Address the “black box” narrative. This builds trust in how AI solutions reach conclusions and emphasizes patient safety.

Avoid Common Obstacles to Successful AI Governance

  • Unclear Strategy:  Without a well-defined roadmap, governance teams may struggle to prioritize initiatives and allocate resources effectively.
  • Reinventing the Wheel: Creating an entirely new AI governance structure is a recipe for inefficiency. 
  • Lack of Clarity: Without a clear decision-making framework, AI governance becomes murky. Establish a process for evaluating and approving AI solutions.
  • Going it Alone: AI is a complex, ever-changing field. Consulting with external specialists can help you navigate ethical, legal and technical considerations.
  • Metrics Misalignment: Without defined metrics to assess success or areas for improvement, your AI governance remains subjective. 
  • Late-Stage Partner Engagement: Collaborate with vendors early to ensure their solutions align with your governance parameters.

Further Tips to Optimize Your AI Governance Framework

A recent webinar, “Regulating the Future: A Deep Dive into Healthcare AI Governance,” featured healthcare and legal experts from Deloitte Consulting, American College of Cardiology and Epstein Becker Green, explaining potential approaches to governance and essential considerations. Access the recording here. 

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Demetri Giannikopoulos

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