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Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

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  • Michael A. Ribers
  • Hannes Ullrich

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.

Suggested Citation

  • Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1803
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    References listed on IDEAS

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    1. Daniel Bennett & Che-Lun Hung & Tsai-Ling Lauderdale, 2015. "Health Care Competition and Antibiotic Use in Taiwan," Journal of Industrial Economics, Wiley Blackwell, vol. 63(2), pages 371-393, June.
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    Cited by:

    1. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
    2. MARTENS Bertin, 2021. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre (Seville site).
    3. Jason Abaluck & Leila Agha & David C. Chan Jr & Daniel Singer & Diana Zhu, 2020. "Fixing Misallocation with Guidelines: Awareness vs. Adherence," NBER Working Papers 27467, National Bureau of Economic Research, Inc.
    4. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Antibiotic prescribing; prediction policy; machine learning; expert decision-making;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • L38 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Public Policy
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy

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