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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.

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Metadaten
Document Type:Working Paper
Language:English
Author(s):Hannes Ullrich, Michael Allan Ribers
Parent Title (English):Berlin School of Economics Discussion Papers
Hertie Collections (Serial Number):Berlin School of Economics Discussion Papers (27)
Publication year:2023
Publishing Institution:Hertie School
Number pages:52
Related URL:https://berlinschoolofeconomics.de/insights/bse-discussion-papers
DOI:https://doi.org/10.48462/opus4-5111
Release Date:2023/11/13
Tag:Economics
Edition:No. 27
Hertie School Research:BerlinSchoolOfEcon_Discussion_Papers
Licence of document (German):Creative Commons - CC BY - 4.0 International
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