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

Hannes Ullrich and Michael Allan Ribers

No 27, Berlin School of Economics Discussion Papers from Berlin School of Economics

Abstract: 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.

Pages: 52 pages
Date: 2023-11-13
New Economics Papers: this item is included in nep-big, nep-dcm and nep-hea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://opus4.kobv.de/opus4-hsog/files/5111/BSE_DP_0027.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bdp:dpaper:0027

DOI: 10.48462/opus4-5111

Access Statistics for this paper

More papers in Berlin School of Economics Discussion Papers from Berlin School of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christian Reiter ().