NATIONAL BUREAU OF ECONOMIC RESEARCH
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Mastering the Art of Cookbook Medicine: Machine Learning, Randomized Trials, and Misallocation

Jason Abaluck, Leila Agha, David C. Chan Jr, Daniel Singer, Diana Zhu

NBER Working Paper No. 27467
Issued in July 2020
NBER Program(s):Economics of Aging, Health Care, Health Economics, Labor Studies, Public Economics, Productivity, Innovation, and Entrepreneurship

The application of machine learning (ML) to randomized controlled trials (RCTs) can quantify and improve misallocation in healthcare. We study the decision to prescribe anticoagulants for atrial fibrillation patients; anticoagulation reduces stroke risk but increases hemorrhage risk. We combine observational data on treatment choice and guideline use with ML estimates of heterogeneous treatment effects from eight RCTs. When physicians adopt a clinical guideline, treatment decisions shift towards the recommendation but adherence remains far from perfect. Improving guideline adherence would produce larger gains than informing physicians about guidelines. Adherence to an optimal rule would prevent 47% more strokes without increasing hemorrhages.

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Document Object Identifier (DOI): 10.3386/w27467

 
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