Arbeitspapier
Battling antibiotic resistance: Can machine learning improve rescribing?
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: DIW Discussion Papers ; No. 1803
- Klassifikation
-
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
- Thema
-
antibiotic prescribing
prediction policy
machine learning
expert decision-making
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Ribers, Michael
Ullrich, Hannes
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsches Institut für Wirtschaftsforschung (DIW)
- (wo)
-
Berlin
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Ribers, Michael
- Ullrich, Hannes
- Deutsches Institut für Wirtschaftsforschung (DIW)
Entstanden
- 2019
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