Medication Administration Safety and Error Prevention System Using Intelligent Nursing Support Technology

Abstract

Medication Administration Errors (MAEs) represent a persistent global threat to patient safety, contributing significantly to patient morbidity, prolonged hospitalisations, and substantial financial strain on healthcare infrastructure. In the context of an accelerating global population ageing trend, the complexity of managing polypharmacy in home-based elderly care environments has reached an unprecedented peak, rendering traditional manual clinical workflows and human verification protocols increasingly insufficient. This comprehensive review synthesises contemporary literature from 2020 to 2026 regarding the design, architectural deployment, operational efficacy, and clinical outcomes of medication administration safety and error prevention systems leveraging intelligent nursing support technologies. A systematic appraisal of high-impact literature across databases including PubMed, IEEE Xplore, ScienceDirect, and MDPI was conducted, focusing on the convergence of the Internet of Things (IoT), wearable sensors, Artificial Intelligence (AI), Machine Learning (ML), smart home environments, and mobile health (mHealth) frameworks. The findings demonstrate that integrated closed-loop medication management infrastructures substantially reduce MAEs by automating patient identification, prescription label parsing via computer vision, and real-time physiological response tracking. Furthermore, machine learning models provide predictive clinical decision support that alerts nursing professionals to potential adverse drug events before administration. However, broad translation from controlled pilot settings to ubiquitous clinical practice faces critical challenges regarding systemic interoperability, data security, and ethical issues surrounding patient autonomy. Ultimately, intelligent nursing support technologies represent a paradigm shift in healthcare delivery, transforming medication safety from a reactive, human-dependent verification process into a proactive, data-driven, and preventive system. Future research must target scalable, secure, and human-centred frameworks to fully harmonise machine intelligence with professional nursing practice.