Battling antibiotic resistance: can machine learning improve prescribing?

Michael Allan Ribers and Hannes Ullrich

No 7654, CESifo Working Paper Series from CESifo

Abstract: Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic 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, 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)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-hea and nep-pay
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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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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7654

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