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Assessing the value of data for prediction policies: The case of antibiotic prescribing

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  • Huang, Shan
  • Ribers, Michael Allan
  • Ullrich, Hannes

Abstract

We quantify the value of data for the prediction policy problem of reducing antibiotic prescribing to curb antibiotic resistance. Using varying combinations of administrative data, we evaluate machine learning predictions for diagnosing bacterial urinary tract infections and the outcomes of prescription rules based on these predictions. Simple patient demographics improve prediction quality substantially but larger reductions in prescribing can be achieved by making use of rich health data. Our results suggest decreasing returns to data for prediction quality and increasing returns for policy outcomes. Hence, data needs for prediction policy problems must be assessed based on the policy objective and not only on prediction quality.

Suggested Citation

  • Huang, Shan & Ribers, Michael Allan & Ullrich, Hannes, 2022. "Assessing the value of data for prediction policies: The case of antibiotic prescribing," Economics Letters, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:ecolet:v:213:y:2022:i:c:s0165176522000490
    DOI: 10.1016/j.econlet.2022.110360
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    References listed on IDEAS

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    6. Justine S. Hastings & Mark Howison & Sarah E. Inman, 2020. "Predicting high-risk opioid prescriptions before they are given," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(4), pages 1917-1923, January.
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    Cited by:

    1. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.

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