Can AI make better decisions than an ER doctor?

Dive into the intersection of economics and healthcare with our latest podcast episode. How much can AI systems enhance high-stakes medical decision-making? In this episode, we explore the implications of a research paper titled “Diagnosing Physician Error: A Machine Learning Approach to Low Value Health Care” by Sendhil Mullainathan and Ziad Obermeyer.

The paper argues that physicians often make predictable and costly errors in deciding who to test for heart attacks. The authors claim that incorporating machine learning could significantly improve the efficiency and outcome of such tests, reducing the cost per life year saved while maintaining or improving standards of care. We discuss the challenges and limitations of implementing AI in healthcare, the potential biases doctors may have, and the broader systemic issues in medical technology adoption.

Sponsored by the Digital Business Institute at Boston University’s Questrom School of Business. Big thanks to Ching-Ting “Karina” Yang for her help editing the episode.

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🔗Links to the paper for this episode’s discussion:

(Full Paper) Diagnosing Physician Error

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