Publikationen des Projekts: Antibiotic Resistance: Socio-Economic Determinants and the Role of Information and Salience in Treatment Choice (ABRSEIST)

Forschungsdatengesetz: Fakten stärken Vertrauen in Politik und Wissenschaft

Das Ende der Ampelkoalition hat auch dafür gesorgt, dass das eigentlich geplante Forschungsdatengesetz vorerst auf Eis liegt. Es ist von zentraler Bedeutung, um den Zugang zu Forschungsdaten zu verbessern, wissenschaftliche Erkenntnisse zu fördern und eine evidenzbasierte Politikgestaltung zu ermöglichen. Internationale Erfahrungen zeigen, dass eine bessere Datenverfügbarkeit die Zahl hochwertiger ...

Corona-Tests sind zu selektiv, um auf tatsächliche Infektionszahlen zu schließen

Spätestens seit Ende März ist die Corona-Krise endgültig in Deutschland angekommen. Unklar ist aber bis heute, inwieweit die offizielle Fallzahl die tatsächliche Entwicklung der Epidemie widerspiegelt. Nutzen und Kosten einer möglichen Lockerung der einschränkenden Maßnahmen können allerdings nur dann sinnvoll betrachtet werden, wenn die Zahl der Erkrankten und die aktuelle Infektionsgeschwindigkeit ...

Physician Effects in Antibiotic Prescribing: Evidence from Physician Exits

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 ...

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

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 ...

Machine Predictions and Human Decisions with Variation in Payoffs and Skills

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 ...

Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

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 ...

Provider Effects in Antibiotic Prescribing: Evidence from Physician Exits

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 ...

Complementarities between Algorithmic and Human Decision-making: The Case of Antibiotic Prescribing

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 ...

Assessing the Value of Data for Prediction Policies: The Case of Antibiotic Prescribing

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 ...

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