Battling antibiotic resistance: Can machine learning improve rescribing?

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: DIW Discussion Papers ; No. 1803

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Large Data Sets: Modeling and Analysis ; Analysis of Health Care Markets ; Health: Government Policy; Regulation; Public Health ; Public Policy ; Technological Change: Government Policy ; Renewable Resources and Conservation: Government Policy
• Subject: antibiotic prescribing ; prediction policy ; machine learning ; expert decision-making

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ribers, Michael ; Ullrich, Hannes
• Event: Veröffentlichung
• (who): Deutsches Institut für Wirtschaftsforschung (DIW)
• (where): Berlin
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/196835
• Last update: 10.03.2025, 11:44 AM CET