Background: The digital age, with digital sensors, the Internet of Things (IoT), and big data tools, has opened new opportunities for improving the delivery of health care services, with remote monitoring systems playing a crucial role and improving access to patients. The versatility of these systems has been demonstrated during the current COVID-19 pandemic. Health remote monitoring systems (HRMS) present various advantages such as the reduction in patient load at hospitals and health centers. Patients that would most benefit from HRMS are those with chronic diseases, older adults, and patients that experience less severe symptoms recovering from SARS-CoV-2 viral infection. Objective: This paper aimed to perform a systematic review of the literature of HRMS in primary health care (PHC) settings, identifying the current status of the digitalization of health processes, remote data acquisition, and interactions between health care personnel and patients. Methods: A systematic literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify articles that explored interventions with HRMS in patients with chronic diseases in the PHC setting. Results: The literature review yielded 123 publications, 18 of which met the predefined inclusion criteria. The selected articles highlighted that sensors and wearables are already being used in multiple scenarios related to chronic disease management at the PHC level. The studies focused mostly on patients with diabetes (9/26, 35%) and cardiovascular diseases (7/26, 27%). During the evaluation of the implementation of these interventions, the major difficulty that stood out was the integration of information into already existing systems in the PHC infrastructure and in changing working processes of PHC professionals (83%). Conclusions: The PHC context integrates multidisciplinary teams and patients with often complex, chronic pathologies. Despite the theoretical framework, objective identification of problems, and involvement of stakeholders in the design and implementation processes, these interventions mostly fail to scale up. Despite the inherent limitations of conducting a systematic literature review, the small number of studies in the PHC context is a relevant limitation. This study aimed to demonstrate the importance of matching technological development to the working PHC processes in interventions regarding the use of sensors and wearables for remote monitoring as a source of information for chronic disease management, so that information with clinical value is not lost along the way.

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

The management and exchange of electronic health records (EHRs) remain critical challenges in healthcare, with fragmented systems, varied standards, and security concerns hindering seamless interoperability. These challenges compromise patient care and operational efficiency. This paper proposes a novel solution to address these issues by leveraging distributed ledger technology (DLT), including blockchain, to enhance data security, integrity, and transparency in healthcare systems. The decentralized and immutable nature of DLT enables more efficient and secure information exchange across platforms, improving decision-making and coordination of care. This paper outlines a strategic implementation approach, detailing timelines, resource requirements, and stakeholder involvement while addressing crucial privacy and security concerns like encryption and access control. In addition, it explores standards and protocols necessary for achieving interoperability, offering case studies that demonstrate the framework’s effectiveness. This work contributes by introducing a DLT-based solution to the persistent issue of EHR interoperability, providing a novel pathway to secure and efficient health data exchanges. It also identifies the standards and protocols essential for integrating DLT with existing health information systems, thereby facilitating a smoother transition toward enhanced interoperability.

The increasing digitalisation of healthcare has created a pressing need for health professionals to develop robust digital skills. This paper explores the imperative of equipping health professionals with the necessary digital proficiency to enhance their daily workflows and improve patient care. The expanding use of digital technologies, including electronic health records, telehealth, and artificial intelligence, has transformed the healthcare landscape. However, the adoption of these technologies has been hindered by barriers, such as a lack of interoperability and hesitancy among healthcare providers. To address these challenges, this paper argues that digital skill development must be a core component of healthcare education and professional training. Medical schools and healthcare organisations must prioritise the integration of digital health curricula and continuous learning opportunities to ensure that the next generation of healthcare providers is well-equipped to navigate the digital healthcare ecosystem. Additionally, this paper highlights the importance of fostering a culture of digital innovation and collaboration within healthcare settings. By empowering health professionals to actively participate in the development and testing of new digital health applications, the industry can unlock the full potential of digital technologies to enhance daily workflows and improve patient outcomes.

Background: Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different health care settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVDs), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI’s capacity to analyze complex datasets and uncover critical patterns. Objective: This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of CVDs, identifying areas for further research to enhance patient outcomes and support early interventions. Methods: This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field. Results: Through the methodology used, 64 documents were retrieved, of which 40 documents met the inclusion criteria. The reviewed papers demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in health care supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVDs. Conclusions: The study concludes that AI and ML techniques can improve the accuracy of CVD classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and noninvasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in health care.

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.