In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.

Hospitals often evoke negative feelings due to their antiseptic architecture and personal memories. Biophilic hospital design can reduce stress and enhance health and wellness for patients and caregivers, creating sustainable therapeutic environments. This research paper explores these environments and presents case studies demonstrating the positive outcomes of biophilic design on health and wellbeing. This paper systematically reviews and critically assesses literature using secondary data from peer-reviewed journals and reliable sources on sustainable design from 2010–2023. The results show that biophilic design in hospitals reduces hospitalization time, patient mortality, pain levels, and stress for healthcare providers. It alleviates anxiety, improves experiences for patients, families, and staff, reduces patient harm, and supports faster recovery. Overall, it positively influences the psychological and physiological responses of patients and staff. Future research should analyze the impact of individual biophilic design elements separately and explore implementation challenges. It should also quantify benefits such as reduced staff absenteeism and increased productivity. This study fills the gap of the limitation of holistic studies on biophilic design’s impact on patient care and service delivery in hospital settings.

Background: This review investigates the relationship between artificial intelligence (AI) use and the role of nurses in patient care. AI exists in health care for clinical decision support, disease management, patient engagement, and operational improvement and will continue to grow in popularity, especially in the nursing field. Objective: We aim to examine whether AI integration into nursing practice may have led to a change in the role of nurses in patient care. Methods: To compile pertinent data on AI and nursing and their relationship, we conducted a thorough systematic review literature analysis using secondary data sources, including academic literature from the Scopus database, industry reports, and government publications. A total of 401 resources were reviewed, and 53 sources were ultimately included in the paper, comprising 50 peer-reviewed journal articles, 1 conference proceeding, and 2 reports. To categorize and find patterns in the data, we used thematic analysis to categorize the systematic literature review findings into 3 primary themes and 9 secondary themes. To demonstrate whether a role change existed or was forecasted to exist, case studies of AI applications and examples were also relied on. Results: The research shows that all health care practitioners will be impacted by the revolutionary technology known as AI. Nurses should be at the forefront of this technology and be empowered throughout the implementation process of any of its tools that may accelerate innovation, improve decision-making, automate and speed up processes, and save overall costs in nursing practice. Conclusions: This study adds to the existing body of knowledge about the applications of AI in nursing and its consequences in changing the role of nurses in patient care. To further investigate the connection between AI and the role of nurses in patient care, future studies can use quantitative techniques based on recruiting nurses who have been involved in AI tool deployment—whether from a design aspect or operational use—and gathering empirical data for that purpose.

The purpose of this study is to investigate the complexities of inventory management in the healthcare industry for improving efficiency and resilience in healthcare supply chains. A Systematic Literature review using Scopus database and PRISMA approach was performed. Driven by innovation and technology, new developments are changing the face of inventory management. Predictive analytics and stock optimization are made possible by the increasing deployment of AI and machine learning. Autonomous Mobile Robots and RFID technology are being used more for quick identification and data collection, while Internet-of-Things devices are used for real-time tracking. Blockchain technology to guarantee supply chain traceability and transparency is also being investigated. Another is automation through robots, which lowers human error and increases warehouse operation efficiency. Finally, the incorporation of cloud-based systems such as SMART logistics and E-logistics platforms enables distant access to inventory information, encouraging adaptability and cooperation across various sites.

Program management is an important strategy for organizing and managing multiple interdependent construction projects to achieve strategic goals. However, when change orders occur, they can have a serious impact on the quality, time, cost of projects and, ultimately, affect the construction program. Furthermore, when change orders are caused particularly by legislative changes, such as environmental laws, taxes, tolls, safety codes, transportation, design or building codes, their impacts are unavoidable, yet can be managed through mitigation strategies. The existing literature only reports the implications of change orders on the project level and reports legislative changes as one of the contributing factors to change orders, but does not consider the implications on a program level. This study aims to close this knowledge gap by assessing the implications of change orders caused by legislative changes on program management in the construction industry during the construction phase, and explore what the possible mitigation strategies to manage change orders caused by legislative changes are. The objectives of the study include identifying the implications of change orders on construction projects in the UAE through a literature review using peer-reviewed journals and reliable industry sources. Additionally, we investigate the implications of change orders caused by legislative changes on construction programs through interviewing subject matter experts, evaluating the importance of the reported impacts, with possible mitigation strategies, through a structured questionnaire and Relative Importance Index (RII) and, finally, proposing a set of recommendations for key industry stakeholders. A mixed methods approach is adopted in this qualitative study, and the participants include clients, contractors and consultants from the construction industry, with a defined scope covering the construction stage only. The outcomes of the study can guide program managers, decision-makers and practitioners in the construction industry to successfully deliver all projects by directing proper resources to accommodate legislative changes.