With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient’s health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

In recent years, healthcare is evolving to meet the needs of a growing and ageing population. To support better and more reliable care, a comprehensive and up-to-date Personal Health Record (PHR) is essential. Ideally, the PHR should contain all health-related information about an individual and be available for sharing with healthcare institutions. However, due to interoperability issues of the medical and fitness devices, most of the times, the PHR only contains the same information as the patient Electronic Health Record (EHR). This results in lack of health-related information (e.g., physical activity, working patterns) essential to address medical conditions, support prescriptions, and treatment follow-up. This paper introduces the B-Health IoT Box, a fog IoT computing framework for eHealth interoperability and data collection that enables seamless, secure integration of health and contextual data into interoperable health records. The system was deployed in real-world settings involving over 4500 users, successfully collecting and transmitting more than 1.5 million datasets. The validation shown that data was collected, harmonized, and properly stored in different eHealth platforms, enriching data from personal EHR with mobile and wearable sensors data. The solution supports real-time and near real-time data collection, fast prototyping, and secure cloud integration, offering a modular, standards-compliant gateway for digital health ecosystems. The health and health-related data is available in FHIR format enabling interoperable eHealth ecosystems, and better equality of access to health and care services.

Globally, educational institutes are trying to adapt modernized and effective approaches and tools to their education systems to improve the quality of their performance and achievements. However, identifying, designing, and/or developing promising mechanisms and tools that can impact class activities and the development of students’ outputs are critical success factors. Given that, the contribution of this work is to propose a methodology that can guide and usher educational institutes step by step through the implementation of a personalized package of training Toolkits in Smart Labs. In this study, the package of Toolkits refers to a set of needed tools, resources, and materials that, with integration into a Smart Lab can, on the one hand, empower teachers and instructors in designing and developing personalized training disciplines and module courses and, on the other hand, may support students (in different ways) in developing their skills. To demonstrate the applicability and usefulness of the proposed methodology, a model was first developed, representing the potential Toolkits for training and skill development. The model was then tested by instantiating a particular box that integrates some hardware to be able to connect sensors to actuators, with an eye toward implementing this system mainly in the health domain. In a real scenario, the box was used in an engineering program and its associated Smart Lab to develop students’ skills and capabilities in the areas of the Internet of Things (IoT) and Artificial Intelligence (AI). The main outcome of this work is a methodology supported by a model able to represent Smart Lab assets in order to facilitate training programs through training Toolkits.