• Center on Health Equity and Access
  • Clinical
  • Health Care Cost
  • Health Care Delivery
  • Insurance
  • Policy
  • Technology
  • Value-Based Care

The Challenges of Identifying and Mitigating Racial Bias in Predictive Models

News
Podcast

On this episode of Managed Care Cast, we interview the lead author of a paper in the Health IT issue of The American Journal of Managed Care.

In recent years, predictive models in medicine have become increasingly popular what with the continued integration of artificial intelligence and data technology into health care. However, these models can carry the risk of bias depending on which individuals make up the data sets.

The close relationship between health care and technology also raises a myriad of questions when it comes to regulation, accountability, and model implementation.

In this month’s Health Information Technology special issue of The American Journal of Managed Care®, Paige Nong, a PhD candidate in public health at the University of Michigan, and colleagues present research on facilitating informed decision-making and communicating equity issues when integrating predictive models into care.

On this episode of Managed Care Cast, Nong outlines how the researchers carried out their study, the ethical challenges of combining computer science with health, and next steps for combatting bias in predictive models.

Listen above or through one of these podcast services:

iTunes
TuneIn
Stitcher
Spotify


Related Videos
Beau Raymond, MD
Judith Alberto, MHA, RPh, BCOP, director of clinical initiatives, Community Oncology Alliance
Yuqian Liu, PharmD
Jenny Craven, PharmaD, BCPS
Kimberly Westrich, MA
Mila Felder, MD, FACEP, emergency physician and vice president for Well-Being for All Teammates, Advocate Health
Sarah Bajorek, PhD, BCACP, MBA.
Pat Van Burkleo
dr monica li
Related Content
© 2024 MJH Life Sciences
AJMC®
All rights reserved.