FAIR HEALTH

January 8, 2024

Our workshop will delve into the critical issue of algorithmic bias and clinical decision making. This is an opportunity to gain essential insights and practical strategies to identify, mitigate, and evaluate bias in clinical algorithms. We will also explore the legal and ethical implications of algorithmic bias in healthcare, an aspect that is gaining increasing importance in today’s dynamic healthcare landscape. To enrich the workshop and encourage active participation, we have incorporated a combination of lectures and an interactive case study discussion, making it an even more rewarding experience for all attendees. By engaging in this workshop, and learning from one another, we can pave the way for a future where healthcare algorithms enhance patient care, minimize bias, and prioritize equity.

Target Audience

Allied Health Professionals

Medical Students

Nurses

Physicians

Learning Objectives

1. Understanding of the landscape of algorithmic bias in clinical algorithms, including its significance and impact on healthcare outcomes.

2. Navigating the legal and ethical frameworks surrounding algorithmic bias in healthcare, identifying regulatory challenges and integrating principles of responsible AI into their understanding.

3. Identifying and mitigating algorithmic bias during the development and deployment of clinical algorithms, with a focus on data source evaluation and bias reduction techniques.

4. Developing the skills to evaluate the performance of clinical algorithms, employing metrics that go beyond accuracy to encompass fairness considerations, and understand the distinctions between bias and fairness in algorithmic outcomes.

Course summary
Available credit: 
  • 2.50 AMA PRA Category 1 Credit(s)
  • 2.50 Attendance
Registration Opens: 
01/08/2024
Registration Expires: 
01/07/2025
Activity Starts: 
01/08/2024 - 9:00am EST
Activity Ends: 
01/08/2024 - 12:00pm EST
Rating: 
0
Durham, MD 27705
United States
  • Michael Cary, RN 
  • Armando Bedoya, MD
  • Benjamin Goldstein
  • Christina Silcox
  • Lee Tiedrich
  • Nrupen Bhavsar
  • Siobahn Day Grady
  • Tomi Akinyemiju
  • Michael Pencina
  • AMA PRA Category 1 Credit(s)
  • ANCC
  • IACET CEU

Available Credit

  • 2.50 AMA PRA Category 1 Credit(s)
  • 2.50 Attendance
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