Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing

Michael Allan Ribers () and Hannes Ullrich
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Michael Allan Ribers: University of Copenhagen

Quantitative Marketing and Economics (QME), 2024, vol. 22, issue 4, No 3, 445-483

Abstract: Abstract 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 to physicians, where they possess private diagnostic information, effectively utilizes the complementarity between algorithmic and human decisions. Combining physician and algorithmic decisions can achieve a reduction in inefficient overprescribing of antibiotics by 20.3 percent.

Keywords: Human-machine complementarity; Machine learning; Antibiotic resistance; Antibiotic prescribing (search for similar items in EconPapers)
JEL-codes: C53 D83 I18 I19 L2 M15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11129-024-09284-1

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