Artificial Intelligence in Healthcare: Best Practices and Success Stories
Introduction
In this episode of HIMSSCast, Greg Kuhnen, System Director for Analytical Solutions at UNC Health, and Ram Rimal, Manager of Data Science Engineering, share their insights on the efficient and effective deployment of artificial intelligence in healthcare. They discuss the best practices, challenges, and successes in implementing AI tools, as well as their experience with the HIMSS Analytics Maturity Assessment Model.
Key Takeaways
- UNC Health is using AI in various areas, including predictive models and large language models, with early successes in operational and administrative use cases.
- The organization is focusing on responsible adoption of AI tools, ensuring that they are used in a way that benefits both the organization and its patients.
- The HIMSS Analytics Maturity Assessment Model has been helpful in guiding UNC Health’s AI journey, particularly in its focus on real-time data and predictive AI.
Tips and Best Practices
- Automate repetitive tasks to free up staff for more complex work.
- Use AI to improve patient outcomes and reduce costs.
- Ensure responsible AI adoption by considering the potential biases and limitations of AI models.
- Leverage the HIMSS Analytics Maturity Assessment Model to guide your AI journey.
Early Wins and Future Plans
- UNC Health has seen early successes in areas such as predictive modeling and large language models.
- The organization is looking to expand its use of AI in areas such as natural language processing and computer vision.
- Future plans include rolling out AI projects across the organization and scaling up its use cases.
Conclusion
In this episode of HIMSSCast, we’ve learned about the importance of responsible AI adoption, the benefits of automating repetitive tasks, and the value of leveraging the HIMSS Analytics Maturity Assessment Model. By following these best practices and staying focused on patient outcomes, healthcare organizations can successfully implement AI and improve patient care.
Frequently Asked Questions
Q: What are some early wins for UNC Health in AI adoption?
A: Predictive modeling and large language models have shown early success.
Q: How is UNC Health ensuring responsible AI adoption?
A: The organization is considering the potential biases and limitations of AI models and prioritizing responsible adoption.
Q: What role does the HIMSS Analytics Maturity Assessment Model play in UNC Health’s AI journey?
A: The model has been helpful in guiding the organization’s AI journey, particularly in its focus on real-time data and predictive AI.
