The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing
Michael Allan Ribers and
No 1939, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research
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 (search for similar items in EconPapers)
JEL-codes: C10 C55 I11 I18 Q28 (search for similar items in EconPapers)
Pages: 23 p.
New Economics Papers: this item is included in nep-big, nep-cmp and nep-hea
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Persistent link: https://EconPapers.repec.org/RePEc:diw:diwwpp:dp1939
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