Smart Deprescribing Algorithms as a Strategy to Optimize Medication Safety and Reduce Drug–Drug Interactions

Abstract

Clinically significant drug–drug interactions (DDIs) remain a major source of preventable harm in modern pharmacotherapy, particularly in patients exposed to polypharmacy and complex medication regimens. Traditional rule-based clinical decision support systems have contributed to improved medication safety but are frequently limited by alert fatigue, inadequate prioritization, and limited support for actionable clinical decisions. In recent years, deprescribing has emerged as a structured, patient-centered strategy to reduce medication burden and optimize therapeutic outcomes. This review examines the convergence of deprescribing principles with algorithm-driven decision support, a concept referred to as smart deprescribing, aimed at proactively reducing clinically significant DDIs. Through an integrative analysis of international evidence, this study synthesizes findings on rule-based systems, machine learning, and deep learning approaches, highlighting their respective strengths, limitations, and roles within deprescribing-oriented frameworks. The results indicate that algorithm-assisted deprescribing can improve prioritization of high-risk interactions, reduce low-value alerts, and support medication optimization when grounded in robust clinical frameworks. The review further explores implementation pathways across diverse healthcare contexts, including middle-income settings, emphasizing the importance of scalability, interpretability, and workflow integration. Overall, smart deprescribing represents a promising evolution in medication safety that complements clinical judgment while supporting rational prescribing and medical education.

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