Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust

A System Model and Requirements for Transformation to Human-Centric Digital Health

Digital transformation is widely understood as a process where technology is used to modify an organization's products and services and to create new ones. It is rapidly advancing in all sectors of society. Researchers have shown that it is a multidimensional process determined by human decisions based on ideologies, ideas, beliefs, goals, and the ways in which technology is used. In health care and health, the end result of digital transformation is digital health. In this study, a detailed literature review covering 560 research articles published in major journals was performed, followed by an analysis of ideas, beliefs, and goals guiding digital transformation and their possible consequences for privacy, human rights, dignity, and autonomy in health care and health. Results of literature analyses demonstrated that from the point of view of privacy, dignity, and human rights, the current laws, regulations, and system architectures have major weaknesses. One possible model of digital health is based on the dominant ideas and goals of the business world related to the digital economy and neoliberalism, including privatization of health care services, monetization and commodification of health data, and personal responsibility for health. These ideas represent meaningful risks to human rights, privacy, dignity, and autonomy. In this paper, we present an alternative solution for digital health called human-centric digital health (HCDH). Using system thinking and system modeling methods, we developed a system model for HCDH. It uses 5 views (ideas, health data, principles, regulation, and organizational and technical innovations) to align with human rights and values and support dignity, privacy, and autonomy. To make HCDH future proof, extensions to human rights, the adoption of the principle of restricted informational ownership of health data, and the development of new duties, responsibilities, and laws are needed. Finally, we developed a system-oriented, architecture-centric, ontology-based, and policy-driven approach to represent and manage HCDH ecosystems.

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.

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.

The ethical challenges of personalized digital health

Personalized digital health systems (pHealth) bring together in sharp juxtaposition very different yet hopefully complementary moral principles in the shared objectives of optimizing health care and the health status of individual citizens while maximizing the application of robust clinical evidence through harnessing powerful and often complex modern data-handling technologies. Principles brought together include respecting the confidentiality of the patient-clinician relationship, the need for controlled information sharing in teamwork and shared care, benefitting from healthcare knowledge obtained from real-world population-level outcomes, and the recognition of different cultures and care settings. This paper outlines the clinical process as enhanced through digital health, reports on the examination of the new issues raised by the computerization of health data, outlines initiatives and policies to balance the harnessing of innovation with control of adverse effects, and emphasizes the importance of the context of use and citizen and user acceptance. The importance of addressing ethical issues throughout the life cycle of design, provision, and use of a pHealth system is explained, and a variety of situation-relevant frameworks are presented to enable a philosophy of responsible innovation, matching the best use of enabling technology with the creation of a culture and context of trustworthiness.

Reinforcing Health Data Sharing through Data Democratization

In this paper, we propose a health data sharing infrastructure which aims to empower a democratic health data sharing ecosystem. Our project, named Health Democratization (HD), aims at enabling seamless mobility of health data across trust boundaries through addressing structural and functional challenges of its underlying infrastructure with the core concept of data democratization. A programmatic design of an HD platform was elaborated, followed by an introduction of one of our critical designs—a “reverse onus” mechanism that aims to incentivize creditable data accessing behaviors. This scheme shows a promising prospect of enabling a democratic health data-sharing platform.