ODTIC-KG: A knowledge graph framework for proactive oncology drug inventory management in hospitals.

Abstract

Managing oncology drug inventories in hospitals is a critical yet complex task due to high treatment sensitivity, fluctuating patient demand, supply chain disruptions, and strict storage constraints. Traditional hospital inventory systems mainly provide descriptive monitoring and lack mechanisms for anticipatory decision support. This study proposes ODTIC (Oncology Drug Traceability and Intelligent Control), a knowledge graph-driven framework designed to enable proactive control of oncology drug inventories through semantic traceability and intelligent reasoning. The framework models relationships among oncology drugs, batches, inventory items, storage units, and hospital departments using an ontology-based knowledge graph. By combining Semantic Web Rule Language (SWRL) reasoning and SPARQL inference, the system automatically detects operational risks such as stock depletion, safety stock violations, expiration threats, and inter-department stock imbalance. When risks are identified, the framework generates semantically structured decision actions including alerts, reorder triggers, safety stock replenishment, stock redistribution, and therapeutic substitution recommendations. A case study conducted on fourteen oncology drugs distributed across multiple hospital departments demonstrates the capability of the proposed approach to detect risks and generate actionable decisions with low reasoning latency. The results highlight the potential of knowledge graph technologies to transform pharmaceutical traceability systems into proactive decision-support infrastructures.