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.

The adaptive nature of artificial intelligence (AI), with its ability to improve performance through continuous learning, offers substantial benefits across various sectors. However, current regulatory frameworks are not intended to accommodate this adaptive nature, and they have prolonged approval timelines, sometimes exceeding one year for some AI-enabled devices. This creates significant challenges for manufacturers who must deal with lengthy waits and submit multiple approval requests for AI-enabled device software functions as they are updated. In response, regulatory agencies like the US Food and Drug Administration (FDA) have introduced guidelines to better support the approval process for continuously evolving AI technologies. This article explores the FDA’s concept of predetermined change control plans and how they can streamline regulatory oversight by reducing the need for repeated approvals, while ensuring safety and compliance. This can help reduce the burden for regulatory bodies and decrease waiting times for approval decisions, therefore fostering innovation, increasing market uptake, and exploiting the benefits of artificial intelligence and machine learning technologies.

This narrative review explores how specialised clinical competencies and artificial intelligence (AI) technologies converge in the context of perioperative care, with a focus on their combined potential to improve patient safety. Considering the growing complexity of surgical care and rising demands on healthcare professionals, the study aims to understand how human expertise and digital tools can complement each other in this high-stakes environment. Methods: A narrative review methodology was adopted to integrate insights from diverse sources, including empirical studies, policy documents, and expert analyses published over the last decade. Findings reveal that AI can support clinical decision-making, streamline workflows, and enable earlier identification of complications across all perioperative phases. These technologies enhance, rather than replace, the roles of nurses, anesthetists, and surgeons. However, their effective use depends on critical factors such as digital literacy, interdisciplinary collaboration, and ethical awareness. Issues related to data privacy, algorithmic bias, and unequal access to technology highlight the need for thoughtful, inclusive implementation. The future of perioperative care will likely depend on hybrid models where human judgment and AI-based tools are integrated in ways that uphold safety, equity, and person-centred values.

This research explores the benefits and challenges associated with Gen-AI tools for business improvement from the perspective of business players and academicians. Data comprises of interviews with 7 academicians and 10 European industry players. Several benefits were recognized from the use of Gen-AI in business context, mainly resolution of complex situations and operational improvement of processes. At the optimization level, industry players perceived Gen-AI with more potential than academicians. Advantages of Gen-AI were identified as strongly aligned with data quality. Both academicians and industry players highlight difficulty of identification, collection, and update of relevant data, together with the needed parsimony in decision-making about Gen-AI-produced results. This study corroborates the current awareness of scarcity in trained people to explore the full potential of Gen-AI and highlights the resistance towards its adoption by top management, especially in SMEs, which due to their scale may lack structured data and act more risk averse.

The increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers.