Column (in German): ZEW News 9/2009.

Column (in German): DIW Wochenbericht 14/2014.

Working Papers

Human decision-making varies due to differences in information and incentives. This constitutes a substantial challenge for evaluating how machine learning predictions can improve decision outcomes. We tackle this challenge in the context of the major health policy problem of improving efficiency in antibiotic prescribing, the leading cause of antibiotic resistance. We incorporate machine learning predictions on large-scale administrative data into a treatment choice model featuring heterogeneity in patients’ disease risk, physician payoffs, and diagnostic skill. We find substantial variation in the skill to diagnose bacterial urinary tract infections and in how physicians trade off the antibiotic resistance externality against curative benefits. Counterfactual policy evaluation shows that providing predictions to physicians increases efficiency but does not reduce antibiotic use. To reduce prescribing, by close to 10 percent, physicians must be incentivized. Our results highlight the importance of the potential misalignment of decision-makers’ and social planners’ objectives in considering prediction policies, while accentuating the complementarity of machine learning and human expertise.

  • Physician effects in antibiotic prescribing: evidence from physician exits (with Shan Huang) – DIW Discussion Paper Nr. 1958 (download pdf). Twitter thread.

Human antibiotic consumption is considered the main driver of antibiotic resistance. Reducing human antibiotic consumption without compromising health care quality poses one of the most important global health policy challenges. A crucial condition for designing effective policies is to identify who drives antibiotic treatment decisions, physicians or patient demand. We measure the causal effect of physician practice style on antibiotic intake and health outcomes exploiting variation in patient-physician relations due to physician exits in general practice in Denmark. We estimate that physician practice style accounts for 53 to 56 percent of between-clinic differences in all antibiotic consumption, and for 74 to 81 percent in the consumption of second-line antibiotic drugs. We find little evidence that low prescribing styles adversely affect health outcomes measured as preventable hospitalizations due to infections. Our findings suggest that policies to curb antibiotic resistance are most effective when aimed at improving physician decision-making, in particular when they target high prescribers. High prescribing practice styles are positively associated with physician age and negatively with staff size and the availability of diagnostic tools, suggesting that improvements in the quality of diagnostic information is an important path to improved decisions.

Column (in German): DIW Wochenbericht 13-14/2021.

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. Focusing 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.

Columns: DIW Weekly Report 19/2019 (in English), DIW Wochenbericht 19/2019 and Oekonomenstimme (in German).

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and microbiological laboratory data from Denmark to predict diagnostic test outcomes for urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and policy implementation when patient distributions vary over time. The proposed policies delay antibiotic prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, this result is likely to be a lower bound of what can be achieved elsewhere.

We analyze the effects of a hypothetical payment card fee regulation on bank profits, consumer welfare, and merchant welfare. We model consumers’ and merchants’ bank choices for debit card services, cardholders’ demand for card usage (conditional on bank choice), and how banks account for these in setting card fees to their customers. To estimate the model, we use bank-level data and survey data from the Norwegian debit card scheme, BankAxept. We conduct counterfactual exercises to analyze the effects of interchange fee regulations in the debit card scheme.

Former title: Considerations on partially identified regression models, Centre for European Economic Research Discussion Paper 2012-024, BETA (Bureau d’Economie Théorique et Appliquée) Working Paper No. 2012-07, Online Appendix: Download

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