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The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing

Discussion Papers 1939, 23 S.

Shan Huang, Michael Allan Ribers, Hannes Ullrich


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Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the availability of a variety of data combinations for tackling the policy problem of curbing antibiotic resistance, where the reduction of inefficient antibiotic use requires improved diagnostic prediction. Fousing on antibiotic prescribing for suspected urinary tract infections in primary care in Denmark, we link individual-level administrative data with microbiological laboratory test outcomes to train a machine learning algorithm predicting bacterial test results. For various data combinations, we assess out of sample prediction quality and efficiency improvements due to prediction-based prescription policies. The largest gains in prediction quality can be achieved using simple characteristics such as patient age and gender or patients’ health care data. However, additional patient background data lead to further incremental policy improvements even though gains in prediction quality are small. Our findings suggest that evaluating prediction quality against the ground truth only may not be sufficient to quantify the potential for policy improvements.

Michael Allan Ribers

Research Associate in the Firms and Markets Department

Shan Huang

Ph.D. Student in the Graduate Center

Hannes Ullrich

Research Associate in the Firms and Markets Department

JEL-Classification: C10;C55;I11;I18;Q28
Keywords: Prediction policy, data combination, machine learning, antibiotic prescribing
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