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Abstract
Background The escalating threat of AMR demands a paradigm shift in antimicrobial prescribing practices. The application programming interface (API) is conceived as an advanced system, integrating artificial intelligence and machine learning, to optimize clinical decision-making in the context of antimicrobial therapy. This study outlines the development and evaluation of the software, emphasizing its potential impact on antimicrobial stewardship. Methods The API was meticulously constructed in two phases. In the initial phase, an algorithm leveraging decision flow, developed by a collaboration of information technology experts, infectious disease and microbiology specialists, was designed. This algorithm accounts for a comprehensive array of variables influencing antimicrobial treatment outcomes. Subsequently, a Machine Learning model was employed to assess the probability of success for each available antimicrobial drug. The second phase involved a rigorous evaluation through ten hypothetically described clinical cases, assessed independently by five infectious disease specialists (IDP team) in a double-blinded study. Results generated were then compared with the antimicrobial prescriptions made by the IDP team. Results Utilizing the World Health Organization's AWaRe classification system as a benchmark, the API demonstrated a 50% prescription at both the Access and Watch categories, with a 0% allocation in the Reserve category. In comparison, the IDP team exhibited an 11.9% prescription in the Access, 73.9% at Watch, and 14.5% at Reserve category. Conclusion Despite potential disparities between expert opinions and the software, the proposed system, characterized by its conservative nature, holds promise in refining and validating clinical decisions. Moreover, the implementation of the API has the potential to mitigate selective pressure that contributes to antimicrobial resistance, thus fortifying antimicrobial stewardship practices.
Published Version
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