Human Factors in AI-Driven Antimicrobial Stewardship: Clinician Decision-Making, Automation Bias, and Patient Safety Risks

Abstract

This research explores how human behavior and psychology impact the effectiveness of artificial intelligence within hospital programs designed to manage antibiotic use. While these digital tools aim to combat antimicrobial resistance, their success often depends on how doctors interpret and trust the technology's suggestions. The study identifies significant obstacles such as alert fatigue and automation bias, which occur when clinicians either ignore warnings or follow computer guidance too blindly. Findings suggest that making AI logic more transparent and improving the way alerts are delivered can foster better professional engagement. Ultimately, the authors argue that human-centered design is essential to ensure these technological advancements actually lead to safer prescribing habits and better patient recovery. To achieve long-term success, medical systems must prioritize the interaction between clinicians and software during both the development and implementation phases.

Keywords: Antimicrobial Stewardship, Artificial Intelligence, Clinical Decision Support Systems, Human Factors, Automation Bias, Patient Safety

DOI: 10.54941/ahfe1007464