Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

Discussion Papers 1803, 40 S.

Michael A. Ribers, Hannes Ullrich

2019

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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.

Michael Ribers

Wissenschaftlicher Mitarbeiter in der Abteilung Unternehmen und Märkte

Hannes Ullrich

Wissenschaftlicher Mitarbeiter in der Abteilung Unternehmen und Märkte



JEL-Classification: C10;C55;I11;I18;L38;O38;Q28
Keywords: Antibiotic prescribing; prediction policy; machine learning; expert decision-making
Frei zugängliche Version: (econstor)
http://hdl.handle.net/10419/196835