Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing

We analyze how machine learning predictions may improve antibiotic prescribing in the context of the global health policy challenge of increasing antibiotic resistance. Estimating a binary antibiotic treatment choice model, we find variation in the skill to diagnose bacterial urinary tract infections and in how general practitioners trade off the expected cost of resistance against antibiotic curative benefits. In counterfactual analyses we find that providing machine learning predictions of bacterial infections to physicians increases prescribing efficiency. However, to achieve the policy objective of reducing antibiotic prescribing, physicians must also be incentivized. Our results highlight the potential misalignment of social and heterogeneous individual objectives in utilizing machine learning for prediction policy problems.