Background The increasing prevalence of polypharmacy has raised concerns about drug-drug interactions (DDIs) and their impact on patient safety. Database-based DDI detection often suffers from insufficient patient background information and missing data, limiting the accuracy and applicability of DDI assessments. A novel model is needed to overcome these limitations and provide a more comprehensive evaluation of DDIs to enhance patient safety in the context of multiple medication use. Objectives This study aims to develop and validate a novel model for evaluating both the beneficial and detrimental effects of DDIs on patient safety. The model is designed to address challenges associated with insufficient patient background information and missing data in database studies while providing a comprehensive assessment of DDIs using statistical inference and hypothesis tests. Methods To address the challenges of insufficient patient background information and missing data often encountered in database studies, the proposed model incorporates an overlap parameter. This parameter represents the degree of commonality in patient profiles susceptible to adverse events from individual drug administrations. The magnitude of DDIs is presented in a 2×2 contingency table constructed by the occurrence or non-occurrence of specific adverse events in observed value and expected value estimated from the model. This tabular format facilitates the assessment of DDIs using statistical inference and hypothesis tests. Results Simulations under various settings confirmed that significance levels for statistical hypothesis tests were strictly observed. Furthermore, applications to real-world databases demonstrated that the proposed model effectively identifies both positive and negative DDIs. Conclusions This research provides healthcare professionals with a robust and practical tool for enhanced DDI detection and management. The presentation of findings in a familiar 2×2 contingency table format improves the accessibility of our results, facilitating straightforward interpretation. The proposed model has the potential to promote a safer healthcare environment for patients on multiple medications, ultimately enhancing patient safety and treatment efficacy.