Publications of the Project: Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice (ABRSEIST)
Improving physicians’ prescription practices is a primary strategy for countering the rise in resistance to antibiotics. This would prevent physicians from incorrectly prescribing antibiotics, one of the main causes of antibiotic resistance. The increasing availability of medical data and methods of machine learning provide an opportunity to generate instant diagnoses. In the present study, the example ...
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 ...
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 ...
2021| Shan Huang, Michael Allan Ribers, Hannes Ullrich
Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions ...
Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections. Based on predictions, we develop policies to improve ...
In the fight against antibiotic resistance, reducing antibiotic consumption while preserving healthcare quality presents a critical health policy challenge. We investigate the role of practice styles in patients’ antibiotic intake using exogenous variation in patient-physician assignment. Practice style heterogeneity explains 49% of the differences in overall antibiotic use and 83% of the differences ...
In:
Journal of Human Resources
(2026), im Ersch. [online first: 2024-05-08]
| Shan Huang, Hannes Ullrich
Artificial Intelligence has the potential to improve human decisions in complex environments, but its effectiveness can remain limited if humans hold context-specific private information. Using the empirical example of antibiotic prescribing for urinary tract infections, we show that full automation of prescribing fails to improve on physician decisions. Instead, optimally delegating a share of decisions ...
In:
Quantitative Marketing and Economics
22 (2024), S. 445–483
| Michael Allan Ribers, Hannes Ullrich
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 ...
In:
Economics Letters
213 (2022), 110360, 4 S.
| Shan Huang, Michael Allan Ribers, Hannes Ullrich