Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/226823 
Year of Publication: 
2020
Series/Report no.: 
DIW Discussion Papers No. 1911
Publisher: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Abstract: 
Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply our framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians' skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show the combination of machine learning predictions with physician diagnostic skill achieves a 25.4 percent reduction in prescribing.
Subjects: 
prediction policy
expert decision-making
machine learning
antibiotic prescribing
JEL: 
C10
C55
I11
I18
Q28
Document Type: 
Working Paper

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