Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Smart Healthcare solutions in China and Europe, an international business perspective

The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China. Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely “Which societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?”, “What are the business model innovation for tech companies in China for the smart health industry?”, “What are the facilitators and hurdles for implementing telehealth solutions”, “Are elderly users willing to use telehealth solutions in China?”. Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China. The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data. Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patients’ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research. There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible

The worldwide impact of telemedicine during COVID-19: current evidence and recommendations for the future.

During the COVID-19 pandemic, telemedicine has emerged worldwide as an indispensable resource to improve the surveillance of patients, curb the spread of disease, facilitate timely identification and management of ill people, but, most importantly, guarantee the continuity of care of frail patients with multiple chronic diseases. Although during COVID-19 telemedicine has thrived, and its adoption has moved forward in many countries, important gaps still remain. Major issues to be addressed to enable large scale implementation of telemedicine include: (1) establishing adequate policies to legislate telemedicine, license healthcare operators, protect patients' privacy, and implement reimbursement plans; (2) creating and disseminating practical guidelines for the routine clinical use of telemedicine in different contexts; (3) increasing in the level of integration of telemedicine with traditional healthcare services; (4) improving healthcare professionals' and patients' awareness of and willingness to use telemedicine; and (5) overcoming inequalities among countries and population subgroups due to technological, infrastructural, and economic barriers. If all these requirements are met in the near future, remote management of patients will become an indispensable resource for the healthcare systems worldwide and will ultimately improve the management of patients and the quality of care

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Well-being and -ageing with chronical disease: the BV2 project

International audienceThe BV2 project aims to propose a monitoring system for wellbeing but also well-aging working on the prevention, detection and monitoring using a System of the Systems (SoS) approach. The project partner already uses the IoT technologies and the BV2 platform will combine the different developed systems. The main originality of the project consist s in the development of a virtual platform by combining the existing system

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinic

Swift trust and behavioral change: facilitating factors of crowdsourcing in chronic disease prevention

Behind Internet usage habits there is a common vocabulary: trust. In order to promote preventive medicine, Internet medical care has been trying to cultivate user habits and behavior change, but whoever increases trust can go further. The Internet has accelerated the pace of work and life and generalized the temporary involvement of individuals and teams. In many organizations, there is usually no time to develop trust among team members or between the team and customers in traditional ways such as mutual familiarity, experience sharing, mutual disclosure, and verification of commitments. These new situations have led to the study of a new form of trust: "swift trust". According to Hurd et al. (2017), "swift trust" focuses on expecting that a person has the necessary attributes to be relied upon. In the "swift trust" theory, a group or individual assumes the existence of trust initially, and later verifies and adjusts trust beliefs accordingly. Faced with the problem of the rapid spread of chronic diseases and the high proportion of medical expenses needed to combat them and that have posed challenges to the national finances in China, this thesis focuses on studying the factors that may facilitate the establishment of "swift trust" in the Internet based chronic disease crowdsourcing model. Grounded on the idea that trust affects behavior and speed affects efficiency, we have reviewed extant literature and, with the help of ROST Content Mining (ROST-CM) text mining software, we dug millions of Internet data and conducted in-depth research on the "swift trust" problem. Results, later verified through two ongoing healthcare projects showed that "profession" followed by "platform", "dissemination" and "propensity" are the most critical factors that affect the establishment of swift trust. These results may be of interest to professionals, organizations and government decision makers in need of establishing and winning trust, and particularly "swift trust", as an essential ingredient in the sharing economy.Existe uma palavra comum por detrás de todos os hábitos de utilização da Internet: confiança. Com o objetivo de promover a medicina preventiva, alguns cuidados médicos prestados através da Internet têm vindo a procurar motivar os utilizadores para uma mudança de hábitos e comportamentos, mas apenas quem conseguir ganhar a confiança poderá ir mais longe. A Internet acelerou o ritmo da vida e do trabalho e generalizou a participação temporária de indivíduos e grupos. Em muitas organizações, não há tempo suficiente para se criar confiança entre os membros de um grupo ou entre grupos e indivíduos através de formas tradicionais como a convivência e o conhecimento mútuos, a partilha de experiências ou a verificação do cumprimento de compromissos. Esta situação levou ao estudo de uma nova forma de confiança: "a confiança imediata". Hurd et al. (2017) afirmam que este conceito se refere à expetativa de que uma determinada pessoa reúna os atributos necessários para ser confiável. Segundo a teoria que estuda a "confiança imediata", um grupo ou indivíduo assume desde logo a presença de confiança e reserva para mais tarde a confirmação da sua existência. Considerando os desafios colocados pelo rápido desenvolvimento de doenças crónicas num país tão populoso como a China e a necessidade de as combater, esta tese estuda os fatores que poderão facilitar a construção de "confiança imediata" no modelo de colaboração aberta através da Internet com vista à prevenção destas doenças. Partindo do princípio de que a confiança afeta os comportamentos e de que a rapidez afeta a eficiência procedeu-se à revisão de literatura sobre o tema e, com a ajuda do "software" de mineração de texto ROST-CM (ROST Content Mining) foram recolhidos e tratados milhões de dados extraídos da Internet. Os resultados foram depois confrontados com a prática de dois projetos na área da saúde e revelaram que a "profissão" seguida da "plataforma", "disseminação" e "propensão" são os fatores que mais contribuem para a formação de "confiança imediata". Os resultados obtidos poderão ser de interesse para profissionais, organizações e decisores governamentais que necessitam de construir e manter confiança e, em particular "confiança imediata", enquanto ingrediente essencial na economia de partilha