AJMC: Dr Ravi Parikh Offers Solutions to Barriers During Shift From Fee-for-Service to APMs

By In the News

From AJMC: Ravi B. Parikh, MD, MPP, assistant professor of medical ethics and health policy, assistant professor of medicine, University of Pennsylvania, discusses the partnership between University of Pennsylvania and Tennessee Oncology, as well as the barriers commerical payers and practice partners face in the shift to alternative payment models (APMs) in oncology. Parikh and his co-authors published an article in the March issue of The American Journal of Managed Care®, “Oncology Alternative Payment Models: Lessons From Commercial Insurance.” Listen to the episode at AJMC.

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Knowledge at Wharton: How to Build a Better Relationship at the Bargaining Table

By In the News

From Knowledge at Wharton: In a recent interview with The Wall Street Journal, powerhouse entertainment executive Shonda Rhimes said, “Never enter a negotiation you’re not willing to walk away from. If you walk in thinking, ‘I can’t walk away,’ then … you’ve already lost.” This all-or-nothing approach has become the standard for what’s considered to be success in negotiations, but it doesn’t have to be. Maurice Schweitzer, a Wharton professor of operations, information, and decisions, has written a paper with Einav Hart, management professor at George Mason University, that answers the question: When can negotiators get the best deal by not squeezing their counterpart? Schweitzer joined Knowledge at…

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Data Skeptic: Haywire Algorithms

By In the News

From Data Skeptic: Today, we are joined by Ravi Parikh, a Medical Oncologist and an Assistant Professor at the University of Pennsylvania. He runs a lab that develops and implements machine learning predictive models in clinical care. Ravi discusses his research on how the pandemic has toppled the performance of machine learning models in the medical field. The medical researcher kicked off by establishing how he worked with other specialists such as behavioral scientists, implementation scientists, and end-users to turn medical data into actionable inferences. He emphasized the need for more humans, particularly medical practitioners when building machine learning models in…

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