AI news ·
Building Safe and Scalable AI in Health Care: Best Practices, Governance, and Legal Insights
Health care AI requires strong leadership and clear policies to ensure safety and trust. Continuous monitoring and transparency help manage risks and improve patient care.
Health Care's AI Playbook: Building Safe, Smart and Scalable Systems
As health care systems adopt new technologies, strong leadership and clear policies are essential to ensure these tools support patient safety. A recent virtual event gathered AI experts in health care to discuss best practices for implementing AI, maintaining oversight, and building trust among clinicians and patients.
AI models have shown value in many health care settings, but they are not flawless. They can produce inaccurate results and carry biases, which could impact patient care. Because AI technology evolves quickly, health care leaders must proceed carefully and continuously adjust strategies as new challenges arise.
Challenges in AI Implementation
Many health systems now use dozens of AI tools, but managing them effectively over time is challenging. Unlike traditional tools, AI models can change in performance and behavior. This dynamic nature means constant monitoring is needed to ensure safety and effectiveness.
One approach is centralized tracking of AI tools. For example, Duke Health has a chief health information officer overseeing an Algorithm-Based Clinical Decision Support initiative. They combine overarching standards with input from local clinical experts to monitor each AI application closely.
The Importance of AI Governance
Good AI governance starts with production-level oversight to catch errors early. Real-time, actionable insights allow hospital staff to mitigate risks during daily operations, improving care quality proactively rather than reactively.
The Coalition for Health AI (CHAI) promotes transparency, privacy, and security by offering a framework and public review process. Its “model cards” act like product labels, detailing how AI tools are trained and how they perform. Clear disclosure helps clinicians and patients understand when AI is appropriate and when caution is needed.
Managing Legal Risks
Transparency is key when addressing legal concerns around AI in health care. The current phase of AI governance involves figuring out how to maintain up-to-date, informative documentation like model cards. Laws and regulations will evolve through ongoing litigation and policy development to provide clearer answers.
Health systems looking to build AI governance policies should start simple. Taking inventory of existing AI tools is critical—you can’t manage what you don’t know you have. Governance efforts can then focus resources on the highest-risk applications, rather than applying the same level of scrutiny to every tool.
Next Steps for Health Care Professionals
- Establish clear policies for AI oversight and update them regularly.
- Create centralized tracking systems for all AI tools in use.
- Engage clinical experts in monitoring AI impacts within their specialties.
- Promote transparency through detailed documentation of AI models.
- Prepare for evolving legal requirements by staying informed and adaptable.
For health care professionals interested in deepening their AI knowledge, Complete AI Training offers courses tailored to health care roles, helping teams implement AI responsibly and effectively.