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Correlation vs. Causation: Stanford's Dr. Sherri Rose on Informing Health Policy

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Manage episode 315864857 series 2833637
Content provided by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Sherri Rose, Ph.D. is an Associate Professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research. She speaks with us about detangling causation from correlation, measuring the effectiveness of different interventions, and influencing real-world health policy.

Pranav and Adriel first give an overview on risk adjustment, where medicine, health policy, and financial incentives interact in complex ways, before the interview, which starts at 12:04. If you like what you hear, let a friend know, subscribe wherever you get your podcasts, and connect with us on Twitter @AIHealthPodcast.

  continue reading

41 episodes

Artwork
iconShare
 
Manage episode 315864857 series 2833637
Content provided by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, Marc Robbins, Pranav Rajpurkar, Adriel Saporta, Oishi Banerjee, and Marc Robbins or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Sherri Rose, Ph.D. is an Associate Professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research. She speaks with us about detangling causation from correlation, measuring the effectiveness of different interventions, and influencing real-world health policy.

Pranav and Adriel first give an overview on risk adjustment, where medicine, health policy, and financial incentives interact in complex ways, before the interview, which starts at 12:04. If you like what you hear, let a friend know, subscribe wherever you get your podcasts, and connect with us on Twitter @AIHealthPodcast.

  continue reading

41 episodes

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