Ebook: pHealth 2017

The pHealth 2017 Conference is the 14th in a series of scientific events bringing together expertise from medical, technological, political, administrative, and social domains, and even from philosophy or linguistics. It opens a new chapter in the success story of the series of international conferences on wearable or implantable micro and nano technologies for personalized medicine by presenting 3 keynotes, 3 invited talks, 16 oral presentations, and 8 short poster presentations from more than 120 authors, coming from 20 countries from all around the world.

Starting in 2003 as a Dissemination Activity in the framework of a European Project on Wearable Micro and Nano Technologies for Personalized Health with personal health management systems, pHealth conferences have evolved to truly interdisciplinary and global events. Meanwhile, pHealth comprehensively represented in the conference series also covers technological and biomedical facilities, legal, ethical, social, and organizational requirements and impacts as well as necessary basic research for enabling the future proof care paradigms. Thereby, it combines medical services with public health, prevention, social and elderly care, wellness and personal fitness to establish participatory, predictive, personalized, preventive, and effective care settings. By this way, it has attracted scientists, developers, and practitioners from various technologies, medical and health disciplines, legal affairs, politics, and administration from all over the world. The conference brought together health services vendor and provider institutions, payer organizations, governmental departments, academic institutions, professional bodies, but also patient and citizens representatives.

Smart mobile systems such as microsystems, smart textiles, smart implants, sensor-controlled medical devices, and innovative sensor and actuator principles and techniques as well as related body, local and wide area networks up to Cloud services have become important enablers for telemedicine and ubiquitous pervasive health as the next generation health services. Social media and gamification have added even further knowledge to pHealth as an eco-system.

OECD has defined four basic areas to be managed in the new care model: address the big data challenges; foster meaningful innovation; understand and address the potential new risks; and support concerted effort to un-silo communities for a virtual care future. The multilateral benefits of pHealth technologies for all stakeholder communities including patients, citizens, health professionals, politicians, healthcare establishments, and companies from the biomedical technology, pharmaceutical, and telecommunications domain gives enormous potential, not only for medical quality improvement and industrial competitiveness, but also for managing health care cost.

The pHealth 2017 Conference thankfully benefits from the experience and the lessons learned from the organizing committees of previous pHealth events, particularly 2009 in Oslo, 2010 in Berlin, 2011 in Lyon, 2012 in Porto, 2013 in Tallinn, 2014 in Vienna, 2015 in Västerås, and 2016 in Heraklion. The 2009 conference brought up the interesting idea of having special sessions, focusing on a particular topic, and being organized by a mentor/moderator. The Berlin event in 2010 initiated workshops on particular topics prior to the official kick-off of the conference. Lyon in 2011 initiated the launch of so-called dynamic demonstrations allowing the participants to dynamically show software and hardware solutions on the fly without needing a booth. Implementing pre-conference events, the pHealth 2012 in Porto gave attendees a platform for presenting and discussing recent developments and provocative ideas that helped to animate the sessions. Highlight of pHealth 2013 in Tallinn was the special session on European projects' success stories, but also presentations on the newest paradigm changes and challenges coming up with Big Data, Analytics, Translational and Nano Medicine, etc. Vienna in 2014 focused on lessons learned from international and national R&D activities and practical solutions, and especially from the new EU Framework Program for Research and Innovation, Horizon 2020. Beside reports about technology transfer support and building ecosystems and value chains to ensure better time to market and higher impact of knowledge-based technologies, the acceptability of solutions, especially considering security and privacy aspects have been presented and deeply discussed. pHealth 2015 in Västerås addressed mobile technologies, knowledge-driven applications and computer-assisted decision support, but also apps designed to support elderly as well as chronic patients in their daily and possibly independent living. Furthermore, fundamental scientific and methodological challenges of adaptive, autonomous, and intelligent pHealth approaches, the new role of patients as consumers and active party with growing autonomy and related responsibilities, but also requirements and solutions for mHealth in low- and medium income countries have been considered. The pHealth2016 conference aimed at the integration of biology and medical data, the deployment mobile technologies through the development of micro-nano-bio smart systems, the emphasis on personalized health, virtual care, precision medicine, big bio-data management and analytics. The pHealth 2017 event in Eindhoven provides an inventory of the former conferences by summarizing requirements and solutions for pHealth systems, highlighting the importance of trust, and newly focuses on behavioral aspects in designing and using pHealth systems. A specific aspect addressed is the need for flexible, adaptive and knowledge-based systems as well as decision intelligence. pHealth 2017 aims at sharing experiences and developing visions for future developments. The pHealth 2017 presentations are complemented by demonstrations of practical artifacts and solutions.

The Technical University Eindhoven (TU/e), Cognicum bv. HL7 the Netherlands, and Results 4 Care bv, but – following a long-term tradition – also the Working Groups “Electronic Health Records (EHR)”, “Personal Portable Devices (PPD)” and “Security, Safety and Ethics (SSE)” of the European Federation for Medical Informatics (EFMI) have been actively involved in the preparation and realization of the pHealth 2017 Conference.

This proceedings volume covers 1 Keynote, 2 Invited Papers as well as 16 Full Papers selected from more than 45 submissions provided by about 150 authors from 23 countries from Europe, North and South America, and Asia, the first time also recognizing contributions from China, South Korea and India. Furthermore, it contains 8 Short Presentations (Poster Presentations). All submissions have been carefully and critically reviewed by at least two independent experts from other than the authors' home countries, and additionally by at least one member of the Scientific Program Committee. The performed highly selective review process resulted in a full papers rejection rate of 60%, by that way guaranteeing a high scientific level of the accepted and finally published papers. The editors are indebted to the acknowledged and highly experienced reviewers for having essentially contributed to the quality of the conference and the book at hand.

Both the pHealth 2017 Conference and the publication of the pHealth 2017 proceedings at IOS Press would not have been possible without the supporters and sponsors Health Level 7 International (HL7 International), HL7 the Netherlands, the Dutch medical informatics association VMBI, and the European Federation for Medical Informatics (EFMI).

The editors are also grateful to the dedicated efforts of the Local Organizing Committee members and their supporters for carefully and smoothly preparing and operating the conference. They especially thank all team members from the TU/e, prof. Ward Cottaar and Karlijn Hillekens from the clinical informatics department, and Patrick van der Schaaf from Cognicum for their dedication to the organization and realization of the conference.

Bernd Blobel, William Goossen

(Editors)

Organizational, methodological and technological paradigm changes enable a precise, personalized, predictive, preventive and participative approach to health and social services supported by multiple actors from different domains at diverse level of knowledge and skills. Interoperability has to advance beyond Information and Communication Technologies (ICT) concerns, including the real world business domains and their processes, but also the individual context of all actors involved. The paper introduces and compares personalized health definitions, summarizes requirements and principles for pHealth systems, and considers intelligent interoperability. It addresses knowledge representation and harmonization, decision intelligence, and usability as crucial issues in pHealth. On this basis, a system-theoretical, ontology-based, policy-driven reference architecture model for open and intelligent pHealth ecosystems and its transformation into an appropriate ICT design and implementation is proposed.

Trust is a social code and glue between persons and organizations in any business domain including health. pHealth is a complex concept that is built around health service providers, individuals and artefacts such as sensors, mobile devices, networks, computers, and software applications. It has many stakeholders such as organizations, persons, patients, customers, and tele-operators. pHealth services are increasingly offered in insecure information space, and used over organizational, geographical and jurisdictional borders. This all means that trust is an essential requirement for successful pHealth services. To make pHealth a successful business, organizations offering pHealth services should establish inter-organizational trust and trusted relationship between their customers. Before starting to use services, the pHealth user should have a possibility to define how much it trusts on the service provider and on the surrounding information infrastructure. The authors' analysis show that trust models used in today's health care and e-commerce are insufficient for networked pHealth. Calculated trust as proposed by the authors is stronger than the predefined dispositional trust model currently used in health care, other's recommendations used in e-commerce and risk assessment. Until now, caused by the lack of business incentive, lack of regulatory and political pressure, pHealth providers have not demonstrated meaningful interest in moving from the current unsatisfactory situation to trust calculation by making information necessary for this methodology available. To make pHealth successful, a combination of legal, political, organizational, technological and educational efforts is needed to initiate the paradigm change and start the era of trust-based pHealth services.

Personal portable devices have already gained their position in health services. However, mobile technologies and Internet of Things open new areas of applications. The possibility to collect many data types continuously over long time intervals brings various questions that must be answered in the design process. We also discuss briefly the role of the user. We illustrate the complexity of the field by a case study of diabetes management.

The aim of this case study has been to investigate to what extent user centered design (UCD) methodologies have been applied, how the process and outcomes were perceived by project team members, and what were potential barriers towards meeting end user needs. The case studied was the European Union Framework 7 integrated project d-LIVER (2011-2015), which aimed at developing an integrated care system for chronic liver disease patient management. d-LIVER is an example of a public funded, international, multidisciplinary, collaborative research project where development starts from a low technology readiness level, but where research is motivated by societal needs for better health care solutions. Awareness of central end user needs are therefore crucial. 14 project participants were interviewed. To meet societal and end user needs represent a prominent motivation factor for participants. The project organization with only clinical partners interacting with end users was accepted as a fact of life and not as a project pain point. A summary of observations and recommendations for good practice is given.

Correctly counting entities is a requirement for analytics tools to function appropriately. The Observational Medical Outcomes Partnership's (OMOP) Common Data Model (CDM) specifications were examined to assess the extent to which counting in OMOP CDM compatible data repositories would work as expected. To that end, constructs (tables, fields and attributes) defined in the OMOP CDM as well as cardinality constraints and other business rules found in its documentation and related literature were compared to the types of entities and axioms proposed in realism-based ontologies. It was found that not only the model itself, but also a proposed standard algorithm for computing condition eras may lead to erroneous counting of several sorts of entities.

Using communication standards is normally combined with the expectation that interfaces implementing them are interoperable. This paper examines why this is not the case and what must be done to overcome this problem. It ends with a conclusion and recommendations for future developments.

The present paper deals with intellectual system for determination and evaluation of personalized patient's level of adherence. The most popular nowadays techniques for adherence evaluation are interview and questionnaires. We propose a new automated approach based on the comparison of direct patient's data with personalized treatment plan using fuzzy logic and standard ISO 13606. On the current step, we process only manually submitted data. For the next step we are going to introduce sensor devices for the purposes of personalized patients' adherence determination. The results can be useful for medical organizations with long-term patients.

Although mobile devices become more and more common in clinicians' hands, transforming them into an institutional tool to access electronic health record information at the patient's bedside still raises many questions. One of these questions is the provenance of mobile devices when these are deployed at an institutional level. Some advocate the use of personal devices, known as BYOD, for its lower cost, others favor the use of institutional devices which allow a standardization of the development, deployment and support processes. The financial disadvantage of institutional devices could be reduced by sharing devices between several care-providers. The problem with this solution is the authentication management. Indeed, smartphones are defined for individual use and do not efficiently manage multiple identities on a single device. In this article we present the outcome of a Delphi study aiming at identifying an authentication strategy that combines security and acceptable usability in order to share a pool of devices in a medical ward.

Introduction: The Dutch perinatal registry required a new architecture due to the large variability of the submitted data from midwives and hospitals. The purpose of this article is to describe the healthcare information architecture for the Dutch perinatal registry.

Methods: requirements analysis, design, development and testing.

Results: The architecture is depicted for its components and preliminary test results.

Conclusion: The data entry and storage work well, the Data Marts are under preparation.

Background: Large Scale Electronic Health Record (EHR) systems tend to contain a significant higher amount of patient data compared to traditional local Electronic Medical Record (EMR) silos. This demands the requirement of providing a user context and role specific summary of the patient record. Security and Access Control systems are a crucial part of modern EHR systems controlling legal policies and patient consents. Goal of this paper is to elaborate a concept enhancing an already existing, commonly based on IHE XDS and XACML based, EHR systems with summarization functionalities.

Methods: Using literature review and analysis of technical background of standards and currently running eHealth projects the state of the art is compiled. Based on this knowledge a workflow concept is derived and verified using a prototype based on an EHR product.

Results: Using the IHE Profiles XDR and On-Demand a standard based connection to the EHR product is established. The data is extracted and stored with a link to the document reference it was contained in. With a standard based IHE ITI-18 transaction the query is filtered by the ACS of the domain. Using the resulting information a patient summary document can be compiled including only data which was gathered from documents the user has access to.

A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

Background: Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs.

Objective: To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors.

Results: The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors.

Conclusions: A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

Background: Among the factors that outline the health of populations, person's lifestyle is the more important one. This work focuses on the caracterization and prevention of sedentary lifestyles. A sedentary behavior is defined as “any waking behavior characterized by an energy expenditure of 1.5 METs (Metabolic Equivalent) or less while in a sitting or reclining posture”.

Objective: To propose a method for sedentary behaviors classification using a smartphone and Bluetooth beacons considering different types of classification models: personal, hybrid or impersonal.

Results: Following the CRISP-DM methodology, a method based on a two-layer approach for the classification of sedentary behaviors is proposed. Using data collected from a smartphones' accelerometer, gyroscope and barometer; the first layer classifies between performing a sedentary behavior and not. The second layer of the method classifies the specific sedentary activity performed using only the smartphone's accelerometer and barometer data, but adding indoor location data, using Bluetooth Low Energy (BLE) beacons. To improve the precision of the classification, both layers implemented the Random Forest algorithm and the personal model.

Conclusions: This study presents the first available method for the automatic classification of specific sedentary behaviors. The layered classification approach has the potential to improve processing, memory and energy consumption of mobile devices and wearables used.

Online communities have been an integral part of tobacco cessation programs. They are rich in content, and offer insights into factors affecting an individual's behavior change efforts. We used word representation techniques to infer implicit meaning embedded in messages exchanged in a health-related online community. Our analysis of peer interactions revealed that individuals factor in safety, glamour, expense, and media projection when choosing a form of nicotine intake. When choosing pharmacotherapy techniques, individuals focus on brands, dosage, and side effects associated with each form (e.g. gums, patches). Our analysis sheds light on factors embedded in peer interactions, which might lead to opinion formation based on peer influence and knowledge dissemination in these social platforms. Such understanding enables design of high-engagement behavior change technologies, through personalization of content delivery by factoring in individual-level beliefs, behavioral state, and community-level influences.

The study explores how mobile safety alarms can be utilized for ambient assisted living (AAL), and provides elderly safety, autonomy, independence and mobility. The aim is to generate knowledge on usage, usability and technical requirements to harvest the potential benefits of using wearable and mobile technologies in care for safe and active living. The study is based on real life pilots in three Norwegian municipalities with 71 users, their caregivers and relatives. Pilot users wore the mobile safety alarm while performing their daily activities – indoor and outdoor. The study shows increased safety to users, their relatives and caregivers and increased activity and mobility indicating improved social and physical health. Further development of wearables and mobile technologies is requested to meet user needs, and the inclusion of relatives imposes new challenges in terms of privacy. Mobile safety alarms represent a huge potential for efficiency and innovation in integrated care, but new tools are required for efficient collaboration and operation for large-scale implementations.

Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user's environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobile recommender system for the promotion of healthy habits. The system is adapted to the user's needs, his/her health information, interests, time, location and lifestyles. In this paper, the CARS computational architecture and the user and context models of health promotion are presented, which were used to implement and test a prototype recommender system.

Diabetes self-management is a crucial element for all people with diabetes and those at risk for developing the disease. Diabetic patients should be empowered to increase their self-management skills in order to prevent or delay the complications of diabetes. This work presents the proposal and first development stages of a smartphone application focused on the empowerment of the patients with diabetes. The concept of this interventional tool is based on the personalization of the user experience from an adaptive and dynamic perspective. The segmentation of the population and the dynamical treatment of user profiles among the different experience levels is the main challenge of the implementation. The self-management assistant and remote treatment for diabetes aims to develop a platform to integrate a series of innovative models and tools rigorously tested and supported by the research literature in diabetes together the use of a proved engine to manage workflows for healthcare.