Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

Michael A. Ribers and Hannes Ullrich

No 1803, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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 (search for similar items in EconPapers)
JEL-codes: C10 C55 I11 I18 L38 O38 Q28 (search for similar items in EconPapers)
Pages: 40 p.
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur and nep-hea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Working Paper: Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing? (2019)
Working Paper: Battling antibiotic resistance: can machine learning improve prescribing? (2019)
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