Jul 2026· Journal of Nursing Future Care: AI and Innovation· pp. 21-30· 0 citations
TL;DR
A systematic appraisal of high-impact literature across databases was conducted, demonstrating 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.
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.
The rapid escalation of the global aging demographic presents an unprecedented burden on contemporary healthcare infrastructure, necessitating a paradigm shift from traditional, reactive institutionalised care to proactive, home-based management strategies. This comprehensive review article evaluates the current state of the art in home-based elderly care monitoring and healthy aging promotion systems driven by integrated smart technologies. By synthesizing multi-disciplinary evidence across nursing, healthcare informatics, biomedical engineering, and digital health domains, this paper examines how the convergence of the Internet of Things (IoT), wearable physiological sensors, Artificial Intelligence (AI), Machine Learning (ML), smart home environments, mobile health (mHealth) platforms, and telemedicine networks can cooperatively support independent living.We delineate the structural architecture of these systems, mapping the transition of raw sensory telemetry from edge devices through cloud computing layers to actionable clinical decision-making interfaces. Clinical outcomes, including significantly reduced hospitalization rates, accelerated emergency response times, enhanced medication adherence, and improved quality of life indicators, are evaluated alongside the pervasive challenges that hinder widespread clinical translation. These systematic barriers include severe technical limitations in interoperability and battery longevity, ethical concerns surrounding continuous surveillance, and critical gaps in data privacy and cybersecurity infrastructure.Finally, this review outlines essential future paradigms, emphasizing user-centered co-design methodologies, edge-computing solutions to optimize data bandwidth, and the integration of next-generation generative AI frameworks capable of delivering predictive, context-aware personalized care. Ultimately, this work provides a rigorous roadmap for researchers, nursing professionals, healthcare practitioners, and engineers to collaborate on developing robust, ethically sound, and sustainable smart ambient assisted living ecosystems that safeguard autonomy and promote holistic wellness among the global aging population.
The rapid aging of the global population and the increasing prevalence of chronic diseases have rendered medication management a critical public health challenge. Poor medication adherence among elderly patients leads to compromised therapeutic outcomes, higher complication rates, and increased healthcare costs. Smart medication boxes, which integrate Internet of Things (IoT) technology, embedded systems, and cloud computing, have emerged as promising tools to address these challenges through functions including timed reminders, automated dispensing, medication logging, remote monitoring, and emergency calling. This paper presents a systematic review of technological advancements, product development, and research trends in smart medication boxes. The technical architecture encompassing hardware components and software functions is comprehensively analyzed. Representative products from both international and domestic markets are compared, highlighting differences in functional completeness, cost-effectiveness, and age-friendly design. Key challenges including system reliability, user acceptance, data security, and standardization are identified and discussed. Future development directions incorporating artificial intelligence-assisted decision-making, 5G-enabled edge computing, and policy integration are proposed. This review provides valuable insights for researchers, product developers, and policymakers engaged in smart aging and digital health technologies.
Ze Guang· Journal of Pharmaceutical an...· 0 citations
Artificial intelligence (AI) is supporting clinical decision-making processes in healthcare systems, personalizing patient care, and optimizing workflows. The aim of this narrative review is to examine the role of AI technologies in supporting physicians, nurses and physiotherapists, as well as to evaluate the barriers, ethical issues, and educational transformations associated with integration. Articles related to the topic were selected from Google Scholar, PubMed, and Scopus search databases using the keywords “Artificial intelligence, healthcare team, healthcare services, informatics,” without any restrictions on publication year. Findings show that clinical decision support systems increase diagnostic accuracy and, through personalized treatment planning, enhance treatment efficacy. In nursing, AI-supported monitoring systems improve patient safety while reducing administrative burden; in physiotherapy, robotic devices, wearable sensors, and machine learning-based movement analysis support rehabilitation. AI-based health education requires new competencies such as health, data, and human literacy. Key barriers include lack of algorithmic transparency, data privacy concerns, bias, and legal uncertainty regarding accountability. In conclusion, AI functions as “augmented intelligence” that complements rather than replaces healthcare professionals. Effective integration requires transparent infrastructures, clear legal boundaries, workforce training, and human-centered practices. Healthcare teams utilizing these AI-supported systems can maximize patient health outcomes.
Ramazan Demirer· İstanbul Gelişim Üniversites...· 0 citations
The aging of the global population has intensified the demand for innovative and sustainable healthcare strategies. Wearable technologies stand out as a promising alternative to enable real-time monitoring, early detection of clinical changes, and the promotion of autonomy and well-being among the older adults. This study presents a proposal for an integrated data platform aimed at monitoring elderly health through wearable devices. The proposed architecture comprises biometric sensors, secure wireless connectivity, intelligent data processing using machine learning algorithms, and interactive dashboards for decision-making. Through a comprehensive literature review and analysis of current applications, the platform was designed to ensure interoperability with healthcare systems, compliance with data protection laws, and usability for older adults. The results point to the potential of wearable technologies to support preventive, personalized, and population-based care models. Future challenges include issues of data integration, user acceptance, cost-effectiveness, and regulatory frameworks. The implementation of such platforms may contribute significantly to transforming the current healthcare model into a more inclusive, proactive, and data-driven system.
Lucas de, F. Gomes, Av et al.· JOURNAL OF BIOENGINEERING, T...· 0 citations
Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings. Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data were analysed thematically to obtain insights on AI interventions across the medication process. Results: AI technologies have been integrated into every stage of medication management, right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that are affected; (3) how effective these technologies are in minimising errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance, which still present major obstacles. Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance.
Jeong-ah Kim, Muhammad Ashad Kabir, Daniel R. Terry et al.· Rural and remote health· 0 citations
Background Preventable adverse drug events (ADEs) remain a major source of hospital morbidity, mortality, and healthcare costs worldwide. Clinical decision support systems (CDSS) integrated into electronic health records (EHRs) were developed to reduce unsafe prescribing, yet evidence of their real-world effectiveness remains mixed. The emergence of artificial intelligence (AI) and machine learning (ML) offers new opportunities to enhance medication safety but also introduces risks such as algorithmic bias, technology-induced error, and reduced clinician vigilance. Objectives This narrative review critically examines: (1) evidence for the effectiveness of CDSS in reducing preventable ADEs; (2) human factors influencing interactions between clinicians and AI-enabled safety tools; and (3) conceptual, methodological, and governance challenges affecting the safe implementation of digital health technologies. Methods A structured narrative review was conducted using the SANRA framework and reported in accordance with PRISMA-ScR guidance where applicable. Searches of PubMed/MEDLINE, CINAHL, Embase, Scopus, and IEEE Xplore covered literature published between January 2015 and March 2024, supplemented by seminal earlier studies. Following eligibility screening, 75 studies were included in a thematic synthesis and quality appraisal using established risk-of-bias tools. Results Five themes emerged: the transition from passive to adaptive decision support; AI's dual role as both a safety enhancer and a source of new risks; persistent alert fatigue; the often-overlooked contribution of nursing vigilance; and gaps in equity, governance, and technology-induced error research. From these findings, we propose the Clinical Safety Intelligence Loop (CSIL), a conceptual framework that positions AI within a sociotechnical system while embedding equity, governance, and continuous feedback as core components. Conclusion Achieving medication safety improvements requires moving beyond technology-focused solutions toward systems-level approaches integrating AI, clinician cognition, organizational culture, and governance. The CSIL offers a useful framework for guiding this transformation, although further empirical validation is needed.
M. Alruwaili, U. Paul-Chima, Ugwu Chinyere Nneoma· Frontiers in Digital Health· 0 citations