Machine learning and physician prescribing: a path to reduced antibiotic use

Inefficient human decisions are driven by biases and limited information. Health care is one leading example where machine learning is hoped to deliver efficiency gains. Antibiotic resistance constitutes a major challenge to health care systems due to human antibiotic overuse. We investigate how a policy leveraging the strengths of a machine learning algorithm and physicians can provide new opportunities to reduce antibiotic use. We focus on urinary tract infections in primary care, a leading cause for antibiotic use, where physicians often prescribe prior to attaining diagnostic certainty. Symptom assessment and rapid testing provide diagnostic information with limited accuracy, while laboratory testing can diagnose bacterial infections with considerable delay. Linking Danish administrative and laboratory data, we optimize policy rules which base initial prescription decisions on machine learning predictions and delegate decisions to physicians where these benefit most from private information at the point-of-care. The policy shows a potential to reduce antibiotic prescribing by 8.1 percent and overprescribing by 20.3 percent without assigning fewer prescriptions to patients with bacterial infections. We find human-algorithm complementarity is essential to achieve efficiency gains.