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The Challenges of Identifying and Mitigating Racial Bias in Predictive Models
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.
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Subjective and Objective Impacts of Ambulatory AI Scribes
January 8th 2026Although the vast majority of physicians using an artificial intelligence (AI) scribe perceived a reduction in documentation time, those with the most actual time savings had higher relative baseline levels of documentation time.
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