Drug-drug interactions (DDIs) pose significant risks to patients, ranging from adverse effects to fatal outcomes. Preventing these issues depends on providing caregivers with timely information on DDIs and offering viable alternative options. Currently, there is a gap in the formal specifications of systems designed to alert caregivers about potential DDIs. This gap hinders the development of further support, such as algorithms that can recommend alternative drugs.

This study adopts the Design Science approach, defining a formal knowledge graph to capture DDIs. Then, algorithms are defined to identify drug interactions and suggest alternative medications with less severe consequences. As a proof of concept, we implemented our approach using Neo4j and Python, transforming data from the Swedish DDIs database.

The implementation was applied to real care session data in the healthcare region of Stockholm for a randomly selected day, focusing on instances where caregivers prescribed drugs with severe DDIs. Validation occurred through expert interviews, discussing the correctness and utility of the approach. Results indicate that our graph-based model effectively supports the development of systems that alert caregivers to potential DDIs and recommend alternative drugs with reduced interactions.

To the best of our knowledge, this paper introduces the first graph-based model serving as a blueprint for developing DDI systems. This model enables systems to i) warn caregivers about the presence of DDIs in prescribed drugs and ii) assess the availability of alternative drugs with less severe interactions, providing recommendations.