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Assessing the Value of Data for Prediction Policies: The Case of Antibiotic Prescribing

Aufsätze referiert extern - Web of Science

Shan Huang, Michael Allan Ribers, Hannes Ullrich

In: Economics Letters 213 (2022), 110360, 4 S.

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.

Michael Allan Ribers

Research Associate in the Firms and Markets Department

Shan Huang

Ph.D. Student in the Firms and Markets Department

Hannes Ullrich

Research Associate in the Firms and Markets Department



Keywords: Value of data, Antibiotic prescribing, Prediction policy problem, Machine learning, Administrative data
DOI:
https://doi.org/10.1016/j.econlet.2022.110360

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