Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?
This addresses antibiotic misuse in healthcare, particularly in primary care, and is incremental as it applies existing methods to new data with policy adaptations.
The study tackled the problem of antibiotic resistance by using machine learning to predict bacterial causes of urinary tract infections, resulting in a 7.42% reduction in antibiotic use without decreasing treatment of bacterial infections.
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.