Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

Transforming Ontology Web Language Elements into Common Terminology Service 2 Terminology Resources.

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

The Representational Challenge for Designing and Managing 5P Medicine Ecosystems.

Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental and behavioral context. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. For enabling communication and cooperation between actors from different domains using different methodologies, languages and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystems and all its components in structure, function and relationships in the necessary detail ranging from elementary particles up to the universe. That way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business view of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture.

Knowledge representation and management enabling intelligent interoperability - principles and standards.

Based on the paradigm changes for health, health services and underlying technologies as well as the need for at best comprehensive and increasingly automated interoperability, the paper addresses the challenge of knowledge representation and management for medical decision support. After introducing related definitions, a system-theoretical, architecture-centric approach to decision support systems (DSSs) and appropriate ways for representing them using systems of ontologies is given. Finally, existing and emerging knowledge representation and management standards are presented. The paper focuses on the knowledge representation and management part of DSSs, excluding the reasoning part from consideration.

Interoperability evaluation case study: an Obstetrics-Gynecology Department and related Information Systems.

The paper presents the steps and metrics for evaluating the interoperability of an Obstetrics-Gynecology Department Information System applied on Bega Clinic Timisoara regarding its readiness for interoperability in relation with similar systems. The developed OGD IS was modeled starting from the Generic Component Model and sends information to other medical units using the HL7 Clinical Document Architecture and Continuity of Care Document standards. The data for evaluation are real, collected between 2009 and 2010 from Bega Clinic Timisoara. The results were relatively good for the investigated data and structure.

Selected Papers from the pHealth 2021 Conference, Genoa, Italy, 8-10 November 2021.

This Special Issue of the Journal of Personalized Medicine presents extended versions of selected contributions to pHealth 2021, the 18th International Conference on Wearable Micro and Nano Technologies for Personalized Health, held on 8-10 November 2021 in Genoa, Italy [...].

Future pHealth Ecosystem-Holistic View on Privacy and Trust.

Modern pHealth is an emerging approach to collecting and using personal health information (PHI) for personalized healthcare and personalized health management. For its products and services, it deploys advanced technologies such as sensors, actuators, computers, mobile phones, etc. Researchers have shown that today's networked information systems, such as pHealth ecosystems, miss appropriate privacy solutions, and trust is only an illusion. In the future, the situation will be even more challenging because pHealth ecosystems will be highly distributed, dynamic, increasingly autonomous, and multi-stakeholder, with the ability to monitor the person's regular life, movements, emotions, and health-related behavior in real time. In this paper, the authors demonstrate that privacy and trust in ecosystems are system-level problems that need a holistic, system-focused solution. To make future pHealth ethically acceptable, privacy-enabled, and trustworthy, the authors have developed a conceptual five-level privacy and trust model as well as a formula that describes the impact of privacy and trust factors on the level of privacy and trust. Furthermore, the authors have analyzed privacy and trust challenges and possible solutions at each level of the model. Based on the analysis performed, a proposal for future ethically acceptable, trustworthy, and privacy-enabled pHealth is developed. The solution combines privacy as personal property and trust as legally binding fiducial duty approaches and uses a blockchain-based smart contract agreement to store people's privacy and trust requirements and service providers' promises.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

Designing and Managing Advanced, Intelligent and Ethical Health and Social Care Ecosystems.

The ongoing transformation of health systems around the world aims at personalized, preventive, predictive, participative precision medicine, supported by technology. It considers individual health status, conditions, and genetic and genomic dispositions in personal, social, occupational, environmental and behavioral contexts. In this way, it transforms health and social care from art to science by fully understanding the pathology of diseases and turning health and social care from reactive to proactive. The challenge is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e., of multidisciplinary and dynamic systems in variable context. This enables mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc., as philosophy or cognitive sciences do. The approach requires the deployment of advanced technologies including autonomous systems and artificial intelligence. This poses important ethical and governance challenges. This paper describes the aforementioned transformation of health and social care ecosystems as well as the related challenges and solutions, resulting in a sophisticated, formal reference architecture. This reference architecture provides a system-theoretical, architecture-centric, ontology-based, policy-driven model and framework for designing and managing intelligent and ethical ecosystems in general and health ecosystems in particular.

Principles and Standards for Designing and Managing Integrable and Interoperable Transformed Health Ecosystems.

The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual's health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author's leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones.

Multidimensional Machine Learning Model to Calculate a COVID-19 Vulnerability Index.

In Colombia, the first case of COVID-19 was confirmed on 6 March 2020. On 13 March 2023, Colombia registered 6,360,780 confirmed positive cases of COVID-19, representing 12.18% of the total population. The National Administrative Department of Statistics (DANE) in Colombia published in 2020 a COVID-19 vulnerability index, which estimates the vulnerability (per city block) of being infected with COVID-19. Unfortunately, DANE did not consider multiple factors that could increase the risk of COVID-19 (in addition to demographic and health), such as environmental and mobility data (found in the related literature). The proposed multidimensional index considers variables of different types (unemployment rate, gross domestic product, citizens' mobility, vaccination data, and climatological and spatial information) in which the incidence of COVID-19 is calculated and compared with the incidence of the COVID-19 vulnerability index provided by DANE. The collection, data preparation, modeling, and evaluation phases of the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM) were considered for constructing the index. The multidimensional index was evaluated using multiple machine learning models to calculate the incidence of COVID-19 cases in the main cities of Colombia. The results showed that the best-performing model to predict the incidence of COVID-19 in Colombia is the Extra Trees Regressor algorithm, obtaining an R-squared of 0.829. This work is the first step toward a multidimensional analysis of COVID-19 risk factors, which has the potential to support decision making in public health programs. The results are also relevant for calculating vulnerability indexes for other viral diseases, such as dengue.

A conceptual framework and principles for trusted pervasive health.

BACKGROUND: Ubiquitous computing technology, sensor networks, wireless communication and the latest developments of the Internet have enabled the rise of a new concept-pervasive health-which takes place in an open, unsecure, and highly dynamic environment (ie, in the information space). To be successful, pervasive health requires implementable principles for privacy and trustworthiness. OBJECTIVE: This research has two interconnected objectives. The first is to define pervasive health as a system and to understand its trust and privacy challenges. The second goal is to build a conceptual model for pervasive health and use it to develop principles and policies which can make pervasive health trustworthy. METHODS: In this study, a five-step system analysis method is used. Pervasive health is defined using a metaphor of digital bubbles. A conceptual framework model focused on trustworthiness and privacy is then developed for pervasive health. On that model, principles and rules for trusted information management in pervasive health are defined. RESULTS: In the first phase of this study, a new definition of pervasive health was created. Using this model, differences between pervasive health and health care are stated. Reviewed publications demonstrate that the widely used principles of predefined and static trust cannot guarantee trustworthiness and privacy in pervasive health. Instead, such an environment requires personal dynamic and context-aware policies, awareness, and transparency. A conceptual framework model focused on information processing in pervasive health is developed. Using features of pervasive health and relations from the framework model, new principles for trusted pervasive health have been developed. The principles propose that personal health data should be under control of the data subject. The person shall have the right to verify the level of trust of any system which collects or processes his or her health information. Principles require that any stakeholder or system collecting or processing health data must support transparency and shall publish its trust and privacy attributes and even its domain specific policies. CONCLUSIONS: The developed principles enable trustworthiness and guarantee privacy in pervasive health. The implementation of principles requires new infrastructural services such as trust verification and policy conflict resolution. After implementation, the accuracy and usability of principles should be analyzed.

Standards and solutions for architecture based, ontology driven and individualized pervasive health.

Based on the long-term work of scientific institutions and SDOs dedicated to system architectures, an interoperability framework is presented to help navigation through existing, emerging and even future standards for comprehensive interoperability of intelligent health and social care services. HL7 artifacts as well as work products of competing organizations are classified and semi-formally interrelated. The methodology is proven in many international standard development and health information systems implementation projects.

Telepathology interoperability - a system architectural approach.

eHealth and pHealth, instantiated, e.g., in telepathology solutions, have to meet advanced interoperability challenges. Technologically supported by pervasive computing and even autonomic computing, pHealth covers many domains, scientifically managed by specialized disciplines using their specific ontologies. Therefore, semantic interoperability has to advance from a communication protocol to knowledge coordination and sharing, deploying advanced system architectures. Based on long-term work of scientific institutions and SDOs dedicated to system architectures, an interoperability framework is presented, integrating existing, emerging and even future specifications and standards for comprehensive interoperability of health and social services. The methodology is proven in many health information systems implementation and standard developments projects.

Security and privacy services in pathology for enabling trustworthy personal health.

Ubiquitous personalized health services including ePathology require comprehensive, but trusted interoperability. Contrary to regulated traditional health services with pre-defined policies, the solutions enabled by mobile, pervasive and autonomous technology have to follow dynamic policies reflecting the customers changing health services needs, expectations and wishes as well as contextual and environmental conditions. The paper introduces an advanced approach to trustworthy architecture-centric, policy-driven pHealth solutions. To some details, it also addresses security and privacy ontologies to represent the required policies.

An ontological approach to manage and harmonize document types.

Working interoperability when consuming resources of different document types requires a harmonized understanding of their structures and contents. But sometimes, a specification has not made the underlying model explicit which would enable a coherent understanding. This paper analyses the commonly used structures of different document types and proposes an ontological structure resulting in the so-called Document Type Ontology (DTO). This solution is based on the Generic Component Model (GCM) as an architectural framework and the Communication Standards Ontology (CSO) and Basic Formal Ontology (BFO) presented earlier.

Paradigm changes in health lead to paradigm changes in pathology.

For the sake of safety and quality of care as well as efficiency of care processes, health systems undergo a paradigm change towards personalized, ubiquitous, health services. This change includes preventive and predictive medicine based on advanced translational medicine. Here we introduce domain-specific, organizational, and technical paradigms, requirements and solutions for personalized, ubiquitous, care. Emphasizing the formal aspects of modeling and implementing Telehealth and personal health (pHealth) interoperability and the entailed multidisciplinary integration, and illustrate the drivers behind and benefits of personalized medicine with a specific focus on the changing trends and impact on pathology, especially emphasizing Telepathology.

Architectural approach for semantic EHR systems development based on Detailed Clinical Models.

The integrative approach to health information in general and the development of pHealth systems in particular, require an integrated approach of formally modeled system architectures. Detailed Clinical Models (DCM) is one of the most promising modeling efforts for clinical concept representation in EHR system architectures. Although the feasibility of DCM modeling methodology has been demonstrated through examples, there is no formal, generic and automatic modeling transformation technique to ensure a semantic lossless transformation of clinical concepts expressed in DCM to either clinical concept representations based on ISO 13606/openEHR Archetypes or HL7 Templates. The objective of this paper is to propose a generic model transformation method and tooling for transforming DCM Clinical Concepts into ISO/EN 13606/openEHR Archetypes or HL7 Template models. The automation of the transformation process is supported by Model Driven-Development (MDD) transformation mechanisms and tools. The availability of processes, techniques and tooling for automatic DCM transformation would enable the development of intelligent, adaptive information systems as demanded for pHealth solutions.

Adaptive intelligent systems for pHealth - an architectural approach.

Health systems around the globe, especially in developing countries, are facing the challenge of delivering effective, safe, and high quality public health and individualized health services independent of time and location, and with minimum of allocated resources (pHealth). In this context, health promotion and health education services are very important, especially in primary care settings. The objective of this paper is to describe the architecture of an adaptive intelligent system mainly developed to support education and training of citizens, but also of health professionals. The proposed architecture describes a system consisting of several agents that cooperatively interact to find and process tutoring materials to disseminate them to users (multi-agent system). A prototype is being implemented which includes medical students from the Medical Faculty at University of Cauca (Colombia). In the experimental process, the student´s learning style - detected with the Bayesian Model - is compared against the learning style obtained from a questioner (manual approach).

Architectural analysis of clinical ontologies for pHealth interoperability.

Comprehensive interoperability between eHealth/pHealth systems requires properly represented shared knowledge. Formal ontologies allow specifying the semantics of health knowledge representation in a well-defined and unambiguous manner. The objective of this paper is to formally analyze - from a system-theoretical architectural perspective - existing clinical ontologies. The paper defines important ontology requirements for semantically interoperable pHealth/eHealth systems. Then, based on those requirements, 17 criteria are defined and used for analyzing 129 clinical ontologies. Statistical results confirm that most ontologies do not meet the defined criteria. OBO foundry defines a good approach to meet all defined criteria, but it does not cover yet the clinical domain as a whole. SNOMED CT was found the more comprehensive one, despite several restrictions.