Mastering the Art of Cookbook Medicine: Machine Learning, Randomized Trials, and Misallocation
, , , ,
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.
You may purchase this paper on-line in .pdf format from SSRN.com ($5) for electronic delivery.
Document Object Identifier (DOI): 10.3386/w27467