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

26 Pages Posted: 9 Apr 2021

See all articles by Shan Huang

Shan Huang

German Institute for Economic Research (DIW Berlin)

Michael A. Ribers

University of Copenhagen

Hannes Ullrich

University of Copenhagen - Department of Economics; German Institute for Economic Research (DIW Berlin) - Innovation, Management, Service

Date Written: March 2021

Abstract

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.

Keywords: Prediction policy, data combination, machine learning, antibiotic prescribing

JEL Classification: C10,C55,I11,I18,Q28

Suggested Citation

Huang, Shan and Ribers, Michael Allen and Ullrich, Hannes, The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing (March 2021). DIW Berlin Discussion Paper No. 1939, Available at SSRN: https://ssrn.com/abstract=3821419 or http://dx.doi.org/10.2139/ssrn.3821419

Shan Huang

German Institute for Economic Research (DIW Berlin)

Mohrenstraße 58
Berlin, 10117
Germany

Michael Allen Ribers

University of Copenhagen ( email )

Nørregade 10
Copenhagen, København DK-1165
Denmark

Hannes Ullrich (Contact Author)

University of Copenhagen - Department of Economics ( email )

Øster Farimagsgade 5, Bygn 26
Copenhagen, 1353
Denmark

German Institute for Economic Research (DIW Berlin) - Innovation, Management, Service ( email )

Mohrenstraße 58
Berlin, 10117
Germany
+493089789521 (Phone)

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