In value-based care, your individual decisions stop being invisible. Jeanne Cohen, who runs a health care analytics company that scores clinical decisions against the evidence, explains why the model has not delivered and what happens once payers can see which clinicians overuse imaging, underuse prevention, and drift from the guidelines. She says that visibility is already being used to steer patients, shape networks, and set prior authorization.

⏱️ Chapters:
0:00 Introduction
1:02 What happens when one clinician gets measured
2:03 Why value-based care was an incantation, not a program
3:37 In value-based care, variation becomes exposure
4:07 The infrastructure everyone underestimated
7:14 The imaging overuse that blows up a contract
7:48 Underuse is the failure nobody looks for
8:22 How much of the 25 percent waste is recoverable
8:55 How payers use your scores to steer patients
9:27 Bad numbers usually are not bad doctors
10:32 Do physicians welcome being measured now
11:34 Can value-based care survive fee for service
12:38 What she would change if she ran Medicare
14:42 Why AI will not fix affordability
16:50 Take home messages

About this episode:
Jeanne Cohen built a company that made clinical practice guidelines computable, then went a step further and started evaluating the decisions individual clinicians actually make against the evidence. Her argument is that value-based care has underperformed because the industry spent a decade on payment mechanisms and organization-level process measures while ignoring the thing that determines both cost and quality: the appropriateness of each clinical decision. She walks through the three vectors her team scores, care that is high cost, care that carries potential harm, and care that varies from the evidence without warrant, and she insists underuse matters as much as overuse. She describes an organization with heavy nuclear imaging overuse that turned out to have no echocardiogram program in place, her example of why poor numbers usually point at operations rather than at bad clinicians. She explains how provider and payer organizations are already deploying this visibility, putting appropriateness data in front of primary care physicians choosing specialists, steering patients, shaping networks, and running prior authorization. On policy, she is wary of heavy-handed regulation and would rather see public discussion connecting daily clinical activity to value-based performance, along with more quality data shared openly with patients. On AI, she expects a hybrid model with the human in the loop, predicts that judgment and discernment become the scarce clinical skills, and says flatly that AI will not fix affordability because the payment system determines that. She closes by separating what she calls the authentic complexity of medicine from the false complication layered on top of it.

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