Deterministic and stochastic interventions in reducing drug-drug interactions in inappropriate prescribing: A systematic review
Abstract
Background: Drug-drug interactions (DDIs) remain a major contributor to preventable patient harm, particularly in the context of polypharmacy. Over the past two decades, interventions to mitigate inappropriate prescribing have evolved from deterministic, rule-based clinical decision support toward increasingly complex data-driven and stochastic models. However, the extent to which these methodological advances translate into improved clinical safety remains unclear.
Methods: We conducted a systematic review in accordance with PRISMA 2020 guidelines, guided by the SPIDER framework. PubMed, Scopus, ScienceDirect, and IEEE Xplore were searched from inception to October 2025 for primary studies evaluating computational or clinical decision support interventions aimed at reducing DDIs or inappropriate prescribing. Eligible studies included deterministic rule-based systems, ontological frameworks, and artificial intelligence-driven predictive models. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool, extended with artificial intelligence-specific considerations (PROBAST+AI). Due to heterogeneity in study designs and outcome measures, findings were synthesized narratively.
Results: Ten studies met the inclusion criteria. Earlier interventions predominantly employed deterministic approaches focused on workflow optimization, alert management, and policy enforcement, demonstrating modest improvements in prescribing processes but inconsistent links to…
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