Originally Aired - Tuesday, March 12 • 3:00 PM - 4:00 PM Eastern Time (US & Canada)
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Location: W304E
Type: General Education
Session ID: 59
Title: Demystifying Artificial Intelligence: Practical Lessons for Successful Adoption and Provider Satisfaction
Description: While all the hype right now, generative AI is still a relatively new and untapped development presenting enormous potential for the future of healthcare. However, first-generation AI models (also referred to as traditional AI) still hold immense value, and to effectively introduce generative AI to your organization, the key is to have a successful background in implementing older generations of AI that have shown measurable results. In this presentation, athenahealth will discuss how lessons learned from using first-generation AI to develop and deploy more than 15 machine learning models in six years has prepared the organization for the next step: fielding generative AI models. Additionally, we will discuss the importance of including insights from clinicians when developing technologies as well as the considerations necessary when using generative AI, like patient safety, along with risk and legal frameworks that include regulatory compliance and privacy rights. Audience members will walk away with tangible takeaways as they embark on their own AI journeys, including having a clear understanding of methodology used to develop AI models as well as the current risks of the technology within the healthcare industry that should be considered.
Endorsed By: HIMSS CXO Experience
Level: Intermediate
Format: 60-Minute Lecture
Learning Objective #1: Analyze the enormous value and potential of AI when used to solve specific healthcare challenges
Learning Objective #2: Examine the successes and lessons learned when building first-generation AI models prior to generative AI adoption, and the practical considerations necessary to improve experiences and outcomes with AI
Learning Objective #3: Identify the clinician’s critical role and partnership with technologists to ensure AI project efficiency, measured technology success, physician satisfaction and increased physician adoption
