eHealth Domain Research Articles

Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management. The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels. The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks. Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management. Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar.

Individual mobile data in institutional EHRs: The emerging role of information from the Internet of Medical Things (IoMT)

The use of wearable sensors is revolutionizing the monitoring of patients’ vital signs and diseases. In the domains of healthcare and eHealth, the Internet of Things (IoT) and its subset, the Internet of Medical Things (IoMT), along with embedded devices, offer potential for new opportunities and effective solutions. Particularly, their application within the context of Smart Homes exhibits promise. In this regard, the plan to integrate the formal Electronic Health Record (EHR) that is a component of Croatia’s Central Health Information System with personal health data collected via wireless sensors, such as personal trackers, has enormous potential because the implementation of this approach would yield enhanced decision-making processes and advancements in healthcare that are tailored to individual needs.

Critical success factors of startups in the e-health domain

ObjectiveThough health-tech startups are increasingly bridging the affordability, accessibility, and quality gaps in healthcare through innovative solutions, only some sustain and become successful. Knowledge about the critical success factors (CSFs) is limited and shall be helpful for the stakeholders of health-tech startups. The present paper attempts to fill the gap. MethodsWe collected data through in-depth personal interviews with founders and other important stakeholders in India. The data were analyzed using Braun and Clarke's thematic analysis process. We also collected structured inputs from the interviewee to classify the identified CSFs. The Service-Technology-Organization-Finance (STOF) framework was used to guide the data collection and analysis. ResultsThe thematic analysis of the interview transcripts revealed eighteen CSFs and five themes: actor's knowledge and communication process, service value and effectiveness, robust technological infrastructure, revenue generation ability, and regulation management capacity. ConclusionThe delineated CSFs will guide the startups to understand various stakeholders' needs, market demands, regulatory compliance, and policy requisites. The present paper is the first of its kind to extensively examine the CSFs and adds to the knowledge about the health-tech startups' success. In light of the findings, the paper modifies the STOF framework and shares practical implications and future research directions. Public interest summaryHealth-tech startups are increasingly transforming the healthcare industry by leveraging technologies to improve patient outcomes, enhance the patient experience, and deliver value to all stakeholders. There is a need to identify the critical success factors to address the high mortality among these firms. The present research identifies the factors under five broad themes: actor's knowledge and communication process, service value and effectiveness, robust technological infrastructure, revenue generation ability, and regulation management capacity. The findings will help entrepreneurs, policymakers, and other stakeholders in the health-tech startup ecosystem.

DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs

Advances in computing resources have enabled the processing of vast amounts of data. However, identifying trends in such data remains challenging for humans, especially in fields like medicine and social networks. These challenges make it difficult to process, analyze, and visualize the data. In this context, graph summarization has emerged as an effective framework aiming to facilitate the identification of structure and meaning in data. The problem of graph summarization has been studied in the literature and many approaches for static contexts are proposed to summarize the graph. These approaches provide a compressed version of the graph that removes many details while retaining its essential structure. However, they are computationally prohibitive and do not scale to large graphs in terms of both structure and content. Additionally, there is no framework providing summarization of mixed sources with the goal of creating a dynamic, syntactic, and semantic data summary. In this paper, our key contribution is focused on modeling data graphs, summarizing data from multiple sources using a schema-driven approach, and visualizing the graph summary version according to the needs of each user. We demonstrate this approach through a case study on the use of the E-health domain.

Latent class analysis of eHealth behaviors among adults with epilepsy.

The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy. The 2013, 2015, and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a health care provider via email, and used chat groups to learn about health topics. Multivariate logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis was performed to identify underlying patterns of eHealth activity. Survey participants were classified into three discrete classes: (1) frequent, (2) infrequent, and (3) nonusers of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use. There were 1770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a health care provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to nonusers. A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

Experiences of intercultural teaching activities in the field of eHealth

Abstract The funded research Project HealthCONNECT implements measure to promote the term eHealth in teaching and means to ease the exchange of lecturers and students to perceive different views of this topic. This report introduces the activities that have been implemented, stresses challenges that were observed and derives suggestions how to establish a co-operation between different institutes of higher education. This includes the mutual participation and inclusion of lecturers in teaching and evaluation activities; spanning from assessing students’ projects till chairing master examinations. The co-operative organization and hosting of eHealth Summer Schools is another mean to share knowledge in the domain of eHealth with students and colleagues abroad. Such activities require a stable co-operation between the involved institutions that only can be build upon a trustworthy relationship between the lecturers as they are paving the way.

Case-Based Reasoning Diagnostic System for Antenatal Research Database

E-health is an exciting area of research with immense potential for benefit of mankind. The research in e-health domain is of such a nature that it provides an opportunity for researchers from different fields to work together for collective research benefit. Antenatal care is a critical issue for the health of the mother and new born. There are multiple complications which can arise during the pregnancy. The availability of all the relevant data for effective diagnosis and treatment is very important for every case of antenatal care. In this paper, we have proposed an efficient Intelligent Electronic Medical Record system for antenatal care and used Case-Based Reasoning to predict diagnosis of the patient in noisy environment. The experimental results obtained have confirmed the soundness of our approach. These results have also shown that performance of e-health can be enhanced when system development is a collective effort of various stakeholders including medical professionals, software developers and researchers.

A quantitative study exploring the acceptance of the eHealth model for mental wellness among digital workers

eHealth makes use of information and communication technologies (ICT) to improve health. In the digital age, the use of eHealth applications and other health-related applications has gained popularity,particularlyduring theCOVID-19 pandemic. As a result of the pandemic, many uncertainties havearisen, causing stress andaffecting the mental health of many skilled workers in the digital industry, particularly in the ICT, computing, and creative media industries.eHealthapplicationshavethe potential to benefit people's health. As a prerequisite for effective implementation of eHealth for mental wellness (EHMW), this paper examines the acceptance of EHMW amongdigital workersin Malaysia. The objectives of this research are two-fold:1) To explore the acceptance of EHMW amongdigital workersin a localPremier Digital Tech Institution(PDTI), and 2)To explore how these talents' demographic profiles, mental health literacy and workplace wellness influence their acceptance of EHMW. Methods:This research surveyed41digital workerswho played vital roles in providing digital skills at a tertiary education level. Results:Mostrespondentsagreed that eHealthwas appropriate for managing mental wellness. Among the three eHealth domainsfor managing mental wellness, the acceptance level is the highest for the application domain of "interacting for health",with malerespondentsmore likely to accept the use ofEHMW. Conclusions:This small-scale survey could not fully examine theacceptance ofeHealth anditsusage patterns for mental wellness amongdigital workersin Malaysia. Future research will target moredigital workersin Malaysia.This research addresses the research gap ontheeHealth perspectives ofdigital workerson their acceptance,and the potential influence of demographic profiles, mental health literacy, and workplace wellness on EHMW's acceptance of digital health tools/platforms to promote their mental wellness.

Mobile Health (m-Health) in Retrospect: The Known Unknowns.

For nearly two decades, mobile health or (m-Health) was hailed as the most innovative and enabling area for the digital transformation of healthcare globally. However, this profound vision became a fleeting view since the inception and domination of smart phones, and the reorientation of the concept towards the exclusivity of global smart phone application markets and services. The global consumerization of m-Health in numerous disciplines of healthcare, fitness and wellness areas is unprecedented. However, this divergence between ‘mobile health capitalism’ and the ‘science of mobile health’ led to the creation of the ‘m-Health schism’. This schism was sustained by the continued domination of the former on the expense of the latter. This also led to increased global m-Health inequality and divide between the much-perceived health and patient benefits and the markets of m-Health. This divergence was more evident in low and middle income (LMIC) countries compared to the developed world. This powerful yet misguided evolution of the m-Health was driven essentially by complex factors. These are presented in this paper as the ‘known unknowns’ or ‘the obvious but sanctioned facts’ of m-Health. These issues had surreptitiously contributed to this reorientation and the widening schism of m-Health. The collateral damage of this process was the increased shift towards understanding ‘digital health’ as a conjecture term associated with mobile health. However, to date, no clear or scientific views are discussed or analyzed on the actual differences and correlation aspects between digital and mobile health. This particular ‘known unknown’ is presented in detail in order to provide a rapprochement framework of this correlation and valid presentations between the two areas. The framework correlates digital health with the other standard ICT for the healthcare domains of telemedicine, telehealth and e-health. These are also increasingly used in conjunction with digital health, without clear distinctions between these terms and digital health. These critical issues have become timelier and more important to discuss and present, particularly after the world has been caught off guard by the COVID-19 pandemic. The much hyped and the profiteering digital health solutions developed in response of this pandemic provided a modest impact, and the benefits were mostly inadequate in mitigating the massive health, human, and economic impact of this pandemic. This largely commercial reorientation of mobile health was unable not only to predict the severity of the pandemic, but also unable to provide adequate digital tools or effective pre-emptive digital epidemiological shielding and guarding mechanisms against this devastating pandemic. There are many lessons to be learnt from the COVID-19 pandemic from the mobile and digital health perspectives, and lessons must be learnt from the past and to address the critical aspects discussed in this paper for better understanding of mobile health and effective tackling of future global healthcare challenges.

A quantitative study exploring the acceptance of the eHealth model for mental wellness among digital workers

Background: eHealth makes use of information and communication technologies (ICT) to improve health. In the digital age, the use of eHealth applications and other health-related applications has gained popularity, particularly during the COVID-19 pandemic. As a result of the pandemic, many uncertainties have arisen, causing stress and affecting the mental health of many skilled workers in the digital industry, particularly in the ICT, computing, and creative media industries. eHealth applications have the potential to benefit people's health. As a prerequisite for effective implementation of eHealth for mental wellness (EHMW), this paper examines the acceptance of EHMW among digital workers in Malaysia. The objectives of this research are two-fold: 1) To explore the acceptance of EHMW among digital workers in a local Premier Digital Tech Institution (PDTI), and 2) To explore how these talents' demographic profiles, mental health literacy and workplace wellness influence their acceptance of EHMW. Methods: This research surveyed 41 digital workers who played vital roles in providing digital skills at a tertiary education level. Results: Most respondents agreed that eHealth was appropriate for managing mental wellness. Among the three eHealth domains for managing mental wellness, the acceptance level is the highest for the application domain of "interacting for health", with male respondents more likely to accept the use of EHMW. Conclusions: This small-scale survey could not fully examine the acceptance of eHealth and its usage patterns for mental wellness among digital workers in Malaysia. Future research will target more digital workers in Malaysia. This research addresses the research gap on the eHealth perspectives of digital workers on their acceptance, and the potential influence of demographic profiles, mental health literacy, and workplace wellness on EHMW's acceptance of digital health tools/platforms to promote their mental wellness.

Improving usability benchmarking for the eHealth domain: The development of the eHealth UsaBility Benchmarking instrument (HUBBI).

BackgroundCurrently, most usability benchmarking tools used within the eHealth domain are based on re-classifications of old usability frameworks or generic usability surveys. This makes them outdated and not well suited for the eHealth domain. Recently, a new ontology of usability factors was developed for the eHealth domain. It consists of eight categories: Basic System Performance (BSP), Task-Technology Fit (TTF), Accessibility (ACC), Interface Design (ID), Navigation & Structure (NS), Information & Terminology (IT), Guidance & Support (GS) and Satisfaction (SAT).ObjectiveThe goal of this study is to develop a new usability benchmarking tool for eHealth, the eHealth UsaBility Benchmarking Instrument (HUBBI), that is based on a new ontology of usability factors for eHealth.MethodsFirst, a large item pool was generated containing 66 items. Then, an online usability test was conducted, using the case study of a Dutch website for general health advice. Participants had to perform three tasks on the website, after which they completed the HUBBI. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), we identified the items that assess each factor best and that, together, make up the HUBBI.ResultsA total of 148 persons participated. Our selection of items resulted in a shortened version of the HUBBI, containing 18 items. The category Accessibility is not included in the final version, due to the wide range of eHealth services and their heterogeneous populations. This creates a constantly different role of Accessibility, which is a problem for a uniform benchmarking tool.,ConclusionsThe HUBBI is a new and comprehensive usability benchmarking tool for the eHealth domain. It assesses usability on seven domains (BSP, TTF, ID, NS, IT, GS, SAT) in which a score per domain is generated. This can help eHealth developers to quickly determine which areas of the eHealth system’s usability need to be optimized.

Security and Privacy Service Level Agreement composition for Internet of Things systems on top of standard controls

The growing markets of Cloud services and IoT platforms have dramatically raised system flexibility and deployment options. However, increasing complexity and dependency on third-party providers make it difficult to assess the security and privacy levels that distributed systems can offer to their users. In the last years, machine-readable Service Level Agreements (SLAs) have been studied as an optimal method for copying with security and privacy policies. Still, the computation of the SLAs of applications distributed in diverse infrastructures remains a challenging task. This paper presents a methodology to compose security SLAs (SecSLAs) and privacy SLAs (PLAs) of Cloud-based IoT applications on top of standard controls. The composition considers individual components’ SLAs and the control delegation relationships between the components with respect to different types of controls (common, system-specific or hybrid controls). Furthermore, we propose a technique to calculate the Service Level Objectives (SLO) of the controls declared in the composite SLA based on the SLOs granted by individual components. Finally, the paper presents the validation of the methodology carried out to create the SecSLAs and PLAs of a real multiCloud-based IoT application in the eHealth domain.

A quantitative study exploringtheacceptance of theeHealthmodel formentalwellness amongdigital workers.

eHealth makes use of information and communication technologies (ICT) to improve health. In the digital age, the use of eHealth applications and other health-related applications has gained popularity,particularlyduring theCOVID-19 pandemic. As a result of the pandemic, many uncertainties havearisen, causing stress andaffecting the mental health of many skilled workers in the digital industry, particularly in the ICT, computing, and creative media industries.eHealthapplicationshavethe potential to benefit people's health. As a prerequisite for effective implementation of eHealth for mental wellness (EHMW), this paper examines the acceptance of EHMW amongdigital workersin Malaysia. The objectives of this research are two-fold:1) To explore the acceptance of EHMW amongdigital workersin a localPremier Digital Tech Institution(PDTI), and 2)To explore how these talents' demographic profiles, mental health literacy and workplace wellness influence their acceptance of EHMW. Methods:This research surveyed41digital workerswho played vital roles in providing digital skills at a tertiary education level. Results:Mostrespondentsagreed that eHealthwas appropriate for managing mental wellness. Among the three eHealth domainsfor managing mental wellness, the acceptance level is the highest for the application domain of "interacting for health",with malerespondentsmore likely to accept the use ofEHMW. Conclusions:This small-scale survey could not fully examine theacceptance ofeHealth anditsusage patterns for mental wellness amongdigital workersin Malaysia. Future research will target moredigital workersin Malaysia.This research addresses the research gap ontheeHealth perspectives ofdigital workerson their acceptance,and the potential influence of demographic profiles, mental health literacy, and workplace wellness on EHMW's acceptance of digital health tools/platforms to promote their mental wellness.

The WOOL Dialogue Platform: Enabling Interdisciplinary User-Friendly Development of Dialogue for Conversational Agents

Health coaching applications can include (embodied) conversational agents as coaches. The development of these agents requires an interdisciplinary cooperation between eHealth application developers, interaction designers and domain experts. Therefore, proper dialogue authoring tools and tools to integrate these dialogues in a conversational agent system are essential in the process of creating successful agent-based applications. However, we found no existing open source, easy-to-use authoring tools that support multidisciplinary agent development. To that end, we developed the WOOL Dialogue Platform. The WOOL Dialogue Platform provides the eHealth and conversational agent communities with an open source platform, consisting of a set of easy to use tools that facilitate virtual agent development. The platform consists of a dialogue definition language, an editor, application development libraries and a web service. To illustrate the platform’s possibilities and use in practice, we describe two use cases from EU Horizon 2020 research projects. The WOOL Dialogue Platform is an ‘easy to use, and powerful if needed’ platform for the development of conversational agent applications that is seeing a slow but steady increase in uptake in the eHealth community. Developed to support dialogue authoring for embodied conversational agents in the health coaching domain, this platform’s strong points are its ease of use and ability to let domain experts and agents technology experts work together by providing all parties with tools that support their work effectively.

Application of Federated Learning Approaches for Time-Series Classification in eHealth Domain

Contemporary Machine Learning approaches (e.g., Deep Learning) need huge volumes of data to build accurate and robust statistical models. Nowadays, such data are often collected by distinct and geographically distributed entities and successively transmitted to and stored by centralized nodes that implement the learning process.However, this approach exposes data to security and privacy issues during the transmitting process.In order to demonstrate the benefits which Federate learning can bring concerning these issues, this paper proposes an application of the Federated Learning approach to training a time series (TS)-based model for the early identification of the level of risk associated with patients with hypertension in a federated healthcare environment.The results demonstrate the goodness of the solution in comparison with traditional centralized approaches. The solution achieves good results; indeed, the accuracy is over 0.90%. The goodness of the model is also confirmed by the values of recall and precision, equal to 82% and 96%, respectively

A data-owner centric privacy model with blockchain and adapted attribute-based encryption for internet-of-things and cloud environment

Advances in internet of things (IoT) and cloud computing technologies have led to the emergence of new applications such as in e-health domain bringing convenience for both physicians and patients. However, the development of these new technologies makes users' privacy vulnerable. The threats on private data may arise from service providers themselves voluntarily or by inadvertence. As a result, the data owner would like to ensure that the collected data are securely stored and accessed only by authorised users. In this paper, we propose a novel data-owner centric privacy model in IoT/cloud environment. Our model combines two promising paradigms for data privacy, which are attribute-based encryption (ABE) and blockchain, to strengthen the data-owner privacy protection. We propose a new scheme of ABE that is, in one hand, suitable to resource-constrained devices by externalising the computing capabilities, thanks to fog computing paradigm and, in the other hand, combined with a blockchain-based protocol to overcome a single point of trust and to enhance data-owner access control.

CODCA – COVID-19 ONTOLOGY FOR DATA COLLECTION AND ANALYSIS IN E-HEALTH

Abstract. Coronavirus (Covid-19) pandemic is one of the most deadly diseases that cause the death of millions around the world. Automatic collection and analysis of Covid-19 patient data will help medical practitioners in containing the virus. For this purpose, Semantic Web technologies can be utilized, which allows machine-processable data and enables data sharing, and reuse across machines. In this paper, we propose a Covid-19 ontology (named CODCA) that helps in collecting, analysing, and sharing medical information about people in the e-health domain. In particular, the proposed ontology uses information about medical history, drug history, vaccination history, and symptoms in order to analyse Covid-19 risk factors of people and their treatment plans. In this way, information about Covid-19 patients can be automatically processed and can be re-usable by other applications. We also demonstrate extensive semantic queries (i.e. SPARQL queries) to search the created metadata. Furthermore, we illustrate the usage of semantic rules (i.e. SWRL) so that treatment plans for individual patients can be inferred from the available knowledge.

Towards a Standard of Modelling Annotations in the E-Health Domain

A large number of annotation systems in e-health domain have been implemented in the literature. Several factors distinguish these systems from one another. In fact, each of these systems is based on a separate paradigm, resulting in a disorganized and unstructured vision. As part of our research, we attempted to categorize them based on the functionalities provided by each system, and we also proposed a model of annotations that integrates both the health professional and the patient in the process of annotating the medical file.

Comparison of Instruments for Measuring Health-Related Quality of Life and Their Applicability to Ehealth Applications.

Health-related Quality of Life (HRQoL) assessment has proven as a good means for assessing treatment options or impact of applications supporting the patient in adherence, monitoring and better understanding of health issues. While most of the HRQoL instruments were designed several years ago, their capability to assess the impact of ehealth application is in question. The objective of this paper is to assess HRQoL instruments including a focus on the ehealth domain. Methods: Generic and specific instruments are selected based on their widespread use. Published criteria for assessing HRQoL instruments are used for a baseline, which are amended by criteria covering both the ehealth domain and the conditions of use of instruments and structured in groups. Results: Seven instruments have been selected and assessed using the established criteria. The instruments scored differently regarding the ehealth domain, however overall rather low. Applying weighting per group allows highlighting specific aspects. Based on the assessment, further research should consider the development of a ehealth domain module as part of the specific instruments.

Usability of a Co-designed eHealth Prototype for Caregivers: Combination Study of Three Frameworks

BackgroundCo-design (or the participation of users) has shown great potential in the eHealth domain, demonstrating positive results. Nevertheless, the co-design approach cannot guarantee the usability of the system designed, and usability assessment is a complex analysis to perform, as evaluation criteria will differ depending on the usability framework (or set of criteria) used. ISO (International Organization for Standardization) on usability (ISO 9241-210), Nielsen heuristic, and Garrett element of user experience inform different yet complementary aspects of usability.ObjectiveThis study aims to assess the usability and user experience of a co-design prototype by combining 3 complementary frameworks.MethodsTo help caregivers provide care for functionally impaired older people, an eHealth tool was co-designed with caregivers, health and social service professionals, and community workers assisting caregivers. The prototype was a website that aims to support the help-seeking process for caregivers (finding resources) and allow service providers to advertise their services (offering resources). We chose an exploratory study method to assess usability in terms of each objective. The first step was to assess users’ first impressions of the website. The second was a task scenario with a think-aloud protocol. The final step was a semistructured interview. All steps were performed individually (with a moderator) in a single session. The data were analyzed using 3 frameworks.ResultsA total of 10 participants were recruited, 5 for each objective of the website. We were able to identify several usability problems, most of which were located in the information design and interface design dimensions (Garrett framework). Problems in both dimensions were mainly coded as effectiveness and efficiency (ISO framework) and error prevention and match between the system and the real world (Nielsen heuristic).ConclusionsOur study provided a novel contribution about usability analysis by combining the 3 different models to classify the problems found. This combination provided a holistic understanding of the usability improvements needed. It can also be used to analyze other eHealth products.International Registered Report Identifier (IRRID)RR2-10.2196/11634