Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

42 Pages Posted: 22 May 2019

See all articles by Michael A. Ribers

Michael A. Ribers

German Institute for Economic Research (DIW Berlin)

Hannes Ullrich

University of Copenhagen - Department of Economics; German Institute for Economic Research (DIW Berlin) - Innovation, Management, Service

Multiple version iconThere are 2 versions of this paper

Date Written: May 2019

Abstract

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and time-variant patient distributions for policy implementation. The proposed policies delay prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, targeting a 30 percent reduction in prescribing by 2020, this result is likely to be a lower bound of what can be achieved elsewhere.

Keywords: antibiotic prescribing; prediction policy; machine learning; expert decision-making

JEL Classification: C10, C55, I11, I18, L38, O38, Q28

Suggested Citation

Ribers, Michael Allen and Ullrich, Hannes, Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing? (May 2019). DIW Berlin Discussion Paper No. 1803 (2019). Available at SSRN: https://ssrn.com/abstract=3392196 or http://dx.doi.org/10.2139/ssrn.3392196

Michael Allen Ribers

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Hannes Ullrich (Contact Author)

University of Copenhagen - Department of Economics ( email )

Øster Farimagsgade 5, Bygn 26
Copenhagen, 1353
Denmark

German Institute for Economic Research (DIW Berlin) - Innovation, Management, Service ( email )

Mohrenstraße 58
Berlin, 10117
Germany
+493089789521 (Phone)

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