Ebook: pHealth 2018
Smart mobile systems, such as microsystems, smart textiles, smart implants, and sensor-controlled medical devices, together with innovative sensor and actuator techniques and related networks, have become important enablers for telemedicine and a new generation of health services. Social media and gamification have added even more knowledge to pHealth as an ecosystem.
This book presents the proceedings of pHealth 2018. Held in Gjøvik, Norway, in June 2018, this is the 15th in a series of scientific conferences which have brought together expertise from medical, technological, political, administrative, and social domains, and even from philosophy or linguistics. Initiated in 2003 as part of a European project, the scope of these conferences now encompasses technological and biomedical facilities, legal, ethical, social, and organizational requirements and impacts, as well as necessary basic research for enabling future-proof care paradigms. The conferences thereby combine medical services with public health, preventive medicine, social and elderly care, wellness and personal fitness to establish participatory, predictive, personalized, preventive, and effective care settings.
The book includes 1 of the 2 keynotes presented at the conference, 4 invited talks, 16 oral presentations, and 7 short poster presentations. All submissions were carefully and critically reviewed by at least two independent experts, and this selective review process resulted in a full papers rejection rate of 50%.
pHealth 2018 is the 15th Conference 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.
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 patients 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 2018 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, 2016 in Heraklion, and 2017 in Eindhoven. 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 provided 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 2018, borrowing from good experiences of former events, establishes national and European satellite workshops, so completing the more theoretical consideration of the majority of the papers by organizational and practical experiences.
The Norwegian University of Science and Technology (NTNU) in Gjøvik, Norway, Escio AS, HL7 Norway, and HL7 International, 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 2018 Conference.
This proceedings volume covers 1 of the 2 keynotes presented to the conference, 4 invited talks, 16 oral presentations, and 7 short poster presentations from almost 100 authors, coming from 20 countries from all around the world. 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 50%, 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 2018 Conference and the publication of the pHealth 2018 Proceedings at IOS Press would not have been possible without the supporters and sponsors Health Level 7 International (HL7 International), HL7 Norway, Escio AS, the European Federation for Medical Informatics (EFMI), the Center for Cyber and Information Security (CCIS) at NTNU, the Ministry of Health and Care Services of Norway, the IKTPLUSS Initiative of the Research Council of Norway, and the U.S. Embassy Oslo.
The editors are also grateful to the Members of the international Scientific Program Committee, but especially the dedicated efforts of the Local Organizing Committee members and their supporters for carefully and smoothly preparing and operating of the conference. They thank all team members from NTNU such as Urszula Nowostawska and Vivek Agrawal for their dedication to the organization and realization of the conference.
Bernd Blobel, Bian Yang
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 for 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.
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, present identified needs of elderly population and propose potential solutions. 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.
A pHealth ecosystem is a community of service users and providers. It is also a dynamic socio-technical system. One of its main goals is to help users to maintain their personal health status. Another goal is to give economic benefit to stakeholders which use personal health information existing in the ecosystem. In pHealth ecosystems, a huge amount of health related data is collected and used by service providers such as data extracted from the regulated health record and information related to personal characteristics, genetics, lifestyle and environment. In pHealth ecosystems, there are different kinds of service providers such as regulated health care service providers, unregulated health service providers, ICT service providers, researchers and industrial organizations. This fact together with the multidimensional personal health data used raises serious privacy concerns.
Privacy is a necessary enabler for successful pHealth, but it is also an elastic concept without any universally agreed definition. Regardless of what kind of privacy model is used in dynamic socio-technical systems, it is difficult for a service user to know the privacy level of services in real life situations. As 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 is used for calculation of service providers' specific initial privacy value. This value is then used as an estimate for the initial trust score. In this solution, total trust score is a combination of recommended trust, proposed trust and initial trust. Initial privacy level is a weighted arithmetic mean of knowledge and user selected weights. The total trust score for any service provider in the ecosystem can be calculated deploying either a beta trust model or the Fuzzy trust calculation method. The prosed solution is easy to use and to understand, and it can be also automated. It is possible to develop a computer application that calculates a situation-specific trust score, and to make it freely available on the Internet.
In the biomedical domain, there exist a number of common data models (CDM) that have experienced wide uptake. However, none of these has emerged 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 those models. These efforts raise the question of how to appropriately represent the semantics of data, and, furthermore, 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. Organizing and managing the development of numerous orthogonal domain-specific ontologies would benefit from using an Architecture Reference Model, that helps keeping the relationships consistent within each domain and ensure that appropriate inter-domain relationships are defined. This paper 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.
Medication management is a complex process and is taken into account of daily activities. Moreover, participation in daily activities could define the wellbeing. On the other hand, the medication management process for visually impaired individuals is more difficult. Nowadays, the technologies like mHealth and RFID, have caused a significant progress in both areas of medication management systems and visually impaired Independent Living. Therefore the aim of this work was to develop an assistive medication management system for visually impaired people in order to improve the medication adherence among them. The development process started by requirements extraction according to goal directed design methodology introduced by Cooper. Then the system, called MedVision was developed, consisting of an android mobile application, RFID device and a medication box with vibration motors and it is developed for Iranian visually impaired individuals in Persian language. At the final step of this study, a functional assessment was performed in order to improve the system even more in next prototypes.
Objectives: This study was conducted to develop an android based patient decision aid (PDA) as a self-care instrument for patients after kidney transplant and its usability evaluation.
Methods: In this study, the systematic development process of Android-based self-care application for patients after kidney transplant based on Ottawa standard was included: scoping, assemble steering group, analysis of requirements, designing, develop of a prototype and system evaluation. The PDA is a self-triage system that will help early identification of risk symptoms in patients, and help manage them. System recommendations for risk signs are: Refer to the nearest hospital or healthcare center without delay, refer to the doctor and tell your doctor in the next visit. To identify patient care needs, a semi-structured interview with members of steering group, including patients and clinical experts, was conducted by the researchers. A prototype of the decision aid was made according to identified needs in the previous step. Finally, in order to evaluate its usability rate by using the System Usability Scale (SUS) questionnaire, it was used by exerts and patients.
Results: This study identified information needs, risk signs and steps that patients need to make appropriate decisions about them. The main capabilities of the decision aid are features such as reminders for appointment/test, time of taking medication, registration of symptoms, weight, blood pressure, body temperature, advising to patient in case of signs of risk, weight, blood pressure, body temperature and test results which were reported in the diagram. The mean score of system's usability evaluated by medical informatics specialists, clinicians, and patients were 88.33, 95, and 91.
Conclusions: PDAs was usable and desirable from the point of view of medical informatics specialists, clinicians and patients.
The demography is changing towards older people, and the challenge to provide an appropriate care is well known. Sensor systems, combined with IT solutions are recognized as one of the major tools to handle this situation. Embedded Sensor Systems for Health (ESS-H) is a research profile at Mälardalen University in Sweden, focusing on embedded sensor systems for health technology applications. The research addresses several important issues: to provide sensor systems for health monitoring at home, to provide sensor systems for health monitoring at work, to provide safe and secure infrastructure and software testing methods for physiological data management. The user perspective is important in order to solve real problems and to develop systems that are easy and intuitive to use. One of the overall aims is to enable health trend monitoring in home environments, thus being able to detect early deterioration of a patient. Sensor systems, signal processing algorithms, and decision support algorithms have been developed. Work on development of safe and secure infrastructure and software testing methods are important for an embedded sensor system aimed for health monitoring, both in home and in work applications. Patient data must be sent and received in a safe and secure manner, also fulfilling the integrity criteria.
High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multi-parameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP.
This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.
Problem: The number of older adults is growing worldwide. This has a social and economic impact in all countries because of the increased number of older adults affected by chronic diseases, health emergencies, and disabilities, representing at the end high cost for the health system. To face this problem, the Ambient Assisted Living (AAL) domain has emerged. Its main objective is to extend the time that older adults can live independently in their homes. AAL is supported by different fields and technologies, being Human Activity Recognition (HAR), control of vital signs and location tracking the three of most interest during the last years.
Objective: To perform a systematic review about Human Activity Recognition (HAR) approaches supported on Indoor Localization (IL) and vice versa, describing the methods they have used, the accuracy they have obtained and whether they have been directed towards the AAL domain or not.
Methods: A systematic review of six databases was carried out (ACM, IEEE Xplore, PubMed, Science Direct and Springer).
Results: 27 papers were found. They were categorised into three groups according their approach: paper focus on 1. HAR, 2. IL, 3. HAR and IL. A detailed analysis of the following factors was performed: type of methods and technologies used for HAR, IL and data fusion, as well as the precision obtained for them.
Conclusions: This systematic review shows that the relationship between HAR and IL has been very little studied, therefore providing insights of its potential mutual support to provide AAL solutions.
Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based Not Only SQL (NO-SQL) model can provide an accessible, extensive and robust coding data management framework while maintaining certain flexibility. This paper focus on the design and implementation of a big data-coding warehouse, it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented, analyzed a big data-coding warehouse via the Mongodb database, and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model.
The importance of decision support systems is highly acknowledged as a key strategy to improve medical safety and quality of care. A strong interoperability between the hospital Electronic Health Record (EHR) and the Clinical Decision Support System (CDSS) is the key to reach a most reliable decision support, that could aid in better diagnosis, reduce medication errors and improve practitioner performances. Interoperability is granted by the use of standards for data representation and for system intercommunication. A CDSS to support HAART (Highly active antiretroviral therapy) prescription in HIV (Human Immunodeficiency Virus) naive patients was developed within the Ligurian HIV Network. GLIF3 (GuideLine Interchange Format) standard was used to represent clinical guidelines in machine interpretable format. HSSP (Healthcare Services Specification Project) DSS (Decision Support System) standard was used to develop the CDSS web service that evaluates patient's data according to the rules emerging from the GLIF representation of the guidelines. Patient's data are extracted from hospital EHR, formatted into standard vMR (virtual Medical Record) documents and sent to the DSS web service to be evaluated. The results are displayed by a client application in an intuitive way to guide physician's decisions.
In some healthcare systems, it is common that patients address laboratory test centers directly without a doctor's referral. Russia is one of such countries with about 28% of the patients going directly to the laboratory test center for diagnostics. This leads to a situation when patients are not supported by healthcare professionals in the interpretation of test results. Interpretation of test results is a resource-consuming task that will delay the results and increase costs of each test. However, this can be done by computer-based decision support systems that have proved to solve such tasks efficiently. So, the design and implementation of a decision support system that would generate reports for the patients who referred to a test center without a doctor's referral can increase motivation and support patients to make better informed decisions. The goal of this study is to implement a decision support system for patients. We developed a clinical decision support system for the patients that solves a classification problem by relating a vector of test results to a set of diagnoses and find a set of recommendations associated with every diagnosis from this set.
Heart failure (HF) affects at least 26 million people worldwide and is considered a global pandemic. Almost 1 out of 4 hospitalised patients are re-hospitalised for HF within the 30-day post-discharge period. This can be avoided if changes to the hemodynamics of the HF patient's body are early detected. Bioimpedance Spectroscopy (BIS) is a method that allows the measurement and analysis of multi-frequency body complex impedance and can be used to detect changes to the HF patient's hemodynamics. This paper presents the calibration and validation process of a low-cost portable BIS system, as well as, the study of the best Cole parameter estimation methods. The BIS system was calibrated using a three reference circuit method and a Resistance-Capacitance-Inductance (RCL) meter as calibration system. After calibration, BIS impedance measurements of validation circuits presented an average phase error of 0.76 degrees and an average magnitude relative error of 0.6% when compared with standard values. Regarding Cole parameters estimation, using the Impedance model and the Non-Linear Least Squares method for curve fitting, the relative errors were below 4% when compared with the expected values.
Cancer research has a great importance across the world. It is responsible for data analysis of cancer incidence by region, ethnicity, gender, age, social and economic issues, and contributes to the assessment of population health necessities. Nowadays, Brazil has a complex cancer care scenario. There are nearly 600.000 new cancer cases each year. Cancer surveillance in Brazil is carried out by a network of cancer registries which together feed the data warehouse of cancer incidence across the country. The analysis of cancer data warehouses using Data Mining techniques may discover hidden relations among patients' data, cancer treatment, and disease surveillance. This paper presents the development of a Big Data project with the purpose to analyze the cancer incidence and mortality patterns within the different regions and population groups in Brazil.
A key problem in physical rehabilitation treatments is patient motivation since those treatments involve slow, repetitive, and often painful movements. Consequently, little progress may be achieved after a session, leading to longer or even uncompleted treatments. In this paper, PlayTherapy a platform to assist physical rehabilitation treatments is described. PlayTherapy is composed of two main components: (i) a rehabilitation digital exergame, consisting of a set of movement based and interactive mini-games; (ii) an information management system that keeps patient personal progress. Both components were developed in collaboration with a group of physiotherapists. Additionally, a User Experience (UX) evaluation, involving a group of physiotherapists and patients, is presented. This evaluation showed that the inclusion of PlayTherapy in physical rehabilitation treatments may increase patient motivation.
The widespread adoption of smartphones creates an enormous potential to improve healthcare services. Numerous apps, sensors, and devices are developed for health self-management purposes. However, adoption rates remain low and long-term user engagement is a major issue. The goal of this study is to identify major motivational factors that can facilitate prolonged use of mobile health systems. To this end, we conducted 16 interviews with representatives of various cultural backgrounds, disease history, age, and gender. Participants' experiences indicated that existing systems were unable to answer their self-management needs properly. People with a disease history favored learning from data, as well as from others via social media integration. People without chronic disease felt more reserved about social media integration. In conclusion, systems that collect and share personal data should have a clear opt-in or opt-out option to motivate usage. Additionally, researchers and mobile health system developers could achieve long-term adoption by giving clear answers to privacy and trust issues, while offering people strong added value according to their individual needs.