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by Prof. Dr. habil. Bernd Blobel, FACMI, FACHI, FHL7, FEFMI, MIAHSI
Complex ecosystems like the pHealth one combine different domains represented by a huge variety of different actors (human beings, organizations, devices, applications, components) belonging to different policy domains, coming from different disciplines, deploying different methodologies, terminologies, and ontologies, offering different levels of knowledge, skills, and experiences, acting in different scenarios and accommodating different business cases to meet the intended business objectives.
For correctly modeling such systems, a system-oriented, architecture-centric, ontology-based, policy-driven approach is inevitable, thereby following established Good Modeling Best Practices. However, most of the existing standards, specifications and tools for describing, representing, implementing and managing health (information) systems reflect the advancement of information and communication technology (ICT) represented by different evolutionary levels of data modeling.
The paper presents a methodology to integrating, adopting and advancing models, standards, specifications as well as implemented systems and components on the way towards the aforementioned ultimate approach, so meeting the challenge we face when transforming health systems towards ubiquitous, personalized, predictive, preventive, participative, and cognitive health and social care.
by Helge Blindheim, Senior Advisor, the Norwegian Directorate of Health
The strategic direction for the vision of a digitalized and collaborating health service is described in the national eHealth strategy and action plan, owned by the Norwegian Directorate of eHealth. The patient centric focus is clearer than ever before. There are possibilities to explore and pit-falls to avoid when the different branches of the public health service strive to adopt methods for personalized health. This presentation seek to illustrate the strategic direction and the operational dilemmas.
by Mirko De Maldè, COO at Lynkeus, Italy
The EU-funded project MyHealtMyData aims to implement a new system for allowing patients and citizens to get direct access and control over their personal health data. The envisioned system, that builds on a blockchain architecture and is powered by smart contracts for managing consent and access rights/permission, allow citizens to access their data safely, through a mobile application.The MyHealthMyData platform allow citizens to gather together their personal health data from different sources (healthcare providers, wearable devices, mobile health applications) and have them available in one place. The system also facilitates data sharing, making available anonymisation/pseudonymisation/encryption tools for safely share data with external stakeholders. The same tools are also provided to healthcare providers, to allow them to share the wealth of health data collected for clinical practice securely and in compliance with the GDPR, thus enabling a more easy data flow for the benefit of patients, providers, biomedical research institutions and industries.
by Brad Holschuh, University of Minnesota, USA, Department of Design, Housing, and Apparel
Wearable technology holds great promise for personalized healthcare. Ubiquitously worn devices have the potential to collect continuous, remotely-accessible data on patient health status and activity level. However, patient compliance represents a significant barrier to the sustained adoption of wearable technologies for these applications: if these devices require additional effort to don/use (e.g., charging and syncing of separate hardgoods, such as wristbands or watches) patient compliance drops significantly. A potential solution is to integrate these technologies directly into the everyday garments that the patient would already wear. Such an approach minimizes the burden on the user, as they are not asked to perform any additional steps beyond the already-daily task of getting dressed in typical clothing. In this talk, I will present technologies and manufacturing methods developed at the University of Minnesota for garment-integrated sensing and actuation of the wearer, with specific focus on personalized healthcare and tele-medicine/tele-rehabilitation applications. These technologies are low profile, perceptually "invisible", and mass-manufacturable using existing equipment that is widespread in the apparel industry, making them extremely appealing for near-term deployment in wearable personalized healthcare scenarios. I will also discuss known challenges to garment-integrated sensing and actuation, and comment on future research trends in this space.
by Mathias Brochhausen, University of Arkansas of Medical Sciences, Division of Biomedical Informatics, Little Rock, AR, USA
by Bernd Blobel, University of Regensburg, Medical Faculty, Regensburg, Germany
In the biomedical domain there exist a number of common data models (CDM) that have experienced wide uptake. However, none of these has emerge as the common model. Recently, the demand for integrating and analyzing increasingly large data sets in clinical and translational research has led to numerous efforts to harmonize existing CDMs and integrate data curated based on these. These efforts raise the question of how to appropriately represent the semantics of data, and, in fact, they highlight the fact that quite often different groups have greatly different definitions of ‘semantics’. The question of how to formally assure that mappings between CDMs are correct is often overlooked. The answer to these challenges lies in using axiomatically-rich ontologies that allow verifying that terms refer to the same set of entities using automatic inference. This verification is only possible by building ontologies that represent the content of the scientific disciplines in accordance with the reality of the domain of the disciplines. Creating this kind of ontologies does require an Architecture Reference Model that clarifies the relations between the domain and the representations of the domain. In this Invited Speech we will explore how a strong logical representation of the scientific domain does not only foster harmonization of CDMs, but also informs and facilitates the transition from data over information to knowledge and back.
Invited Speaker: Mathias Brochhausen, PhD, MA, Assistant Professor University of Arkansas of Medical Sciences Little Rock, AR
by Lenka Lhotská, Jaromír Doležal, Jindřich Adolf, Jiří Potůček, Miroslav Křížek, Baha Chbani Czech Technical University in Prague, Czech Republic
The rapid emergence and proliferation of connected medical devices and their application in healthcare are already part of the Healthcare Internet of Things (IoT) – as this area started to be named. Their true impact on patient care and other aspects of healthcare remains to be seen and is highly dependent on the quality and relevancy of the data acquired. There is also the trend of application of IoT in telemedicine and home care environment. Currently many research groups focus on design and development of various solutions that can assist elderly and handicapped people in their home environment. However, many of these solutions are sophisticated and require advanced users that are able to control the device, handle error states and exceptions. They are frequently using expensive technologies that are good for laboratory environment but they are not affordable for many elderly or handicapped persons. In the paper we will analyze the current situation, developed solutions, their advantages and disadvantages. We will explain how the methodology of user centred design can help. On a case study of efficient home solution of a personalized and assistive system we will show possibilities of technologically simple solutions using off-the-shelf devices and elements.
by Asbjørn HOVSTØ, Hafenstrom, Svolvær, Norway and Yajuan Guanb, Aalborg University, Aalborg, Denmark
Lack of interoperability is considered as the most important barrier to achieve the global integration of Internet-of-Things (IoT) ecosystems across borders of different disciplines, vendors and standards. Indeed, the current IoT landscape consists of a large set of isolated islands that do not constitute a real Internet, preventing the exploitation of the huge potential expected by Information and communications technology (ICT) visionaries and unfolding business opportunities facilitated by digitalization and automation. The VICINITY project is building and demonstrating a platform linking various ecosystems providing “interoperability-as-a-service” for infrastructures in the IoT. The approach is bottom-up, decentralized, user-centric and standards-based without relying on a single standard.
by Pekka Ruotsalainen, School of Information Sciences, University of Tampere, Finland
pHealth eco-system is a community of service users and providers. It is also dynamic socio-technical system. One of its main goals is to help users to and maintain their personal health status. Another goal is to give economic benefit for stakeholders which use personal health information existing in the eco-system. In pHealth eco-system huge amount of health related data is collected and used by service providers such as data got from the regulated health record and information related personal characters, genetics, lifestyle and environment. In pHealth eco-system there are different kind of service providers such as health care service providers, unregulated health service providers, ICT service providers, researchers and industrial organizations. This together with multidimensional personal health data used had raised serious privacy concerns. Privacy is necessary enabler for successful pHealth but it is also elastic concept without any universally agreed definition. Regardless of what kind of privacy model used, in dynamic socio-technical systems it is difficult for a service user to know privacy level of services in real life situations. Because privacy and trust are interrelated concepts, the authors have developed a hybrid solution where knowledge got from regulatory privacy requirements and publicly available privacy related documents are used for calculation of service providers’ specific initial privacy value. This value is then used as an estimate for initial trust score. In this solution total trust score is a combination of recommended trust, proposed trust and initial trust. Initial privacy level is weighted arithmetic mean of knowledge and user selected weights. The total trust score for any service provider in the eco-system can be calculated using either beta trust model or Fuzzy trust calculation method. The prosed solution is easy to use and understand and it can be also automated. It is possible to develop a computer application that calculated trust score in situation, and also make it freely available in the net.