Times are displayed in (UTC-08:00) Pacific Time (US & Canada) Change
11/11/2024 |
3:30 PM – 5:00 PM |
Continental Ballroom 6
S47: Strategies for Developing and Implementing AI/ML in Underserved Healthcare Environments:
Presentation Type: Panel
Strategies for Developing and Implementing AI/ML in Underserved Healthcare Environments
Presentation Time: 03:30 PM - 05:00 PM
Abstract Keywords: Health Equity, Healthcare Quality, Fairness and Elimination of Bias, Data Sharing
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This panel session, titled "Strategies for Developing and Implementing AI/ML in Underserved Healthcare Environments" delves into the nuanced challenges and emerging opportunities associated with deploying Artificial Intelligence and Machine Learning (AI/ML) technologies within safety net heatlh systems. These institutions play a critical role in providing care to underserved populations, making the thoughtful integration of AI/ML not just innovative but essential for advancing health equity.
The session aims to unpack the multifaceted strategies required to effectively implement AI/ML solutions in environments where resources are scarce but the need for sophisticated healthcare interventions is high. Panelists will explore the operational hurdles, from data scarcity to workforce training, and propose actionable strategies to overcome these barriers.
A significant focus will be placed on the importance of incorporating social determinants of health into AI/ML models. This approach is vital for ensuring that technological advancements contribute to equitable health outcomes. The panel will discuss methodologies for integrating these determinants into predictive models and decision-making tools.
Furthermore, the session will highlight the critical need for fostering robust collaborations among safety net health systems. By sharing resources, data, and best practices, these institutions can leverage AI/ML technologies more effectively and efficiently.
Lastly, the panel will address the development of AI/ML technologies tailored to the unique needs of safety net health systems. Custom solutions are often required to address the specific challenges and patient populations of these settings.
Moderator:
Jean Feng, PhD
Speaker(s):
Lucas Zier, MD, MS
Zuckerberg San Francisco General Hospital
Susan Ehrlich, MD, MPP
Zuckerberg San Francisco General Hospital
Paige Nong, PhD
University of Minnesota
Author(s):
Presentation Time: 03:30 PM - 05:00 PM
Abstract Keywords: Health Equity, Healthcare Quality, Fairness and Elimination of Bias, Data Sharing
Primary Track: Applications
Programmatic Theme: Public Health Informatics
This panel session, titled "Strategies for Developing and Implementing AI/ML in Underserved Healthcare Environments" delves into the nuanced challenges and emerging opportunities associated with deploying Artificial Intelligence and Machine Learning (AI/ML) technologies within safety net heatlh systems. These institutions play a critical role in providing care to underserved populations, making the thoughtful integration of AI/ML not just innovative but essential for advancing health equity.
The session aims to unpack the multifaceted strategies required to effectively implement AI/ML solutions in environments where resources are scarce but the need for sophisticated healthcare interventions is high. Panelists will explore the operational hurdles, from data scarcity to workforce training, and propose actionable strategies to overcome these barriers.
A significant focus will be placed on the importance of incorporating social determinants of health into AI/ML models. This approach is vital for ensuring that technological advancements contribute to equitable health outcomes. The panel will discuss methodologies for integrating these determinants into predictive models and decision-making tools.
Furthermore, the session will highlight the critical need for fostering robust collaborations among safety net health systems. By sharing resources, data, and best practices, these institutions can leverage AI/ML technologies more effectively and efficiently.
Lastly, the panel will address the development of AI/ML technologies tailored to the unique needs of safety net health systems. Custom solutions are often required to address the specific challenges and patient populations of these settings.
Moderator:
Jean Feng, PhD
Speaker(s):
Lucas Zier, MD, MS
Zuckerberg San Francisco General Hospital
Susan Ehrlich, MD, MPP
Zuckerberg San Francisco General Hospital
Paige Nong, PhD
University of Minnesota
Author(s):
S47: Strategies for Developing and Implementing AI/ML in Underserved Healthcare Environments:
Description
