Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Integrating digital Health services : the role of the government and the challenge of cost allocation

eHealth, mHealth and eCare services are growing in numbers at a fast pace. This is mainly driven by technology and the societal challenges of an aging and more chronically burdened population while pressure on both human and financial resources increases. Though the adoption of these digital health services is challenging and experience difficulties. This work focusses on the main barriers that cause a 'gap' in the value network. Via case research following barriers are identified: 1) low willingness to pay, 2) unbalanced cost/benefit ratios of the actors or unfair cost allocation and 3) negative impacted business models. Furthermore the several roles of the government within the value network of digital health services are discussed and reflections and guidelines for digital health service developers are foreseen

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Ambivalence in digital health: co-designing an mHealth platform for HIV care

In reaction to polarised views on the benefits or drawbacks of digital health, the notion of ‘ambivalence’ has recently been proposed as a means to grasp the nuances and complexities at play when digital technologies are embedded within practices of care. This article responds to this proposal by demonstrating how ambivalence can work as a reflexive approach to evaluate the potential implications of digital health. We first outline current theoretical advances in sociology and organisation science and define ambivalence as a relational and multidimensional concept that can increase reflexivity within innovation processes. We then introduce our empirical case and highlight how we engaged with the HIV community to facilitate a co-design space where 97 patients (across five European clinical sites: Antwerp, Barcelona, Brighton, Lisbon, Zagreb) were encouraged to lay out their approaches, imaginations and anticipations towards a prospective mHealth platform for HIV care. Our analysis shows how patients navigated ambivalence within three dimensions of digital health: quantification, connectivity and instantaneity. We provide examples of how potential tensions arising through remote access to quantified data, new connections with care providers or instant health alerts were distinctly approached alongside embodied conditions (e.g. undetectable viral load) and embedded socio-material environments (such as stigma or unemployment). We conclude that ambivalence can counterbalance fatalistic and optimistic accounts of technology and can support social scientists in taking-up their critical role within the configuration of digital health interventions

Exploring people’s candidacy for mobile health–supported HIV testing and care services in rural Kwazulu-Natal, South Africa: qualitative study

BACKGROUND: The use of mobile communication technologies (mHealth: mobile health) in chronic disease management has grown significantly over the years. mHealth interventions have the potential to decentralize access to health care and make it convenient, particularly in resource-constrained settings. It is against this backdrop that we aimed to codevelop (with potential users) a new generation of mobile phone-connected HIV diagnostic tests and Web-based clinical care pathways needed for optimal delivery of decentralized HIV testing, prevention, and care in low- and middle-income countries. OBJECTIVE: The aim of this study was to understand ways in which an mHealth intervention could be developed to overcome barriers to existing HIV testing and care services and promote HIV self-testing and linkage to prevention and care in a poor, HIV hyperendemic community in rural KwaZulu-Natal, South Africa. METHODS: A total of 54 in-depth interviews and 9 focus group discussions were conducted with potential users (including health care providers) in 2 different communities. Theoretically informed by the candidacy framework, themes were identified from the interview transcripts, manually coded, and thematically analyzed. RESULTS: Participants reported barriers, such as fear of HIV identity, stigma, long waiting hours, clinic space, and health care workers' attitudes, as major impediments to effective uptake of HIV testing and care services. People continued to reassess their candidacy for HIV testing and care services on the basis of their experiences and how they or others were treated within the health systems. Despite the few concerns raised about new technology, mobile phone-linked HIV testing was broadly acceptable to potential users (particularly men and young people) and providers because of its privacy (individual control of HIV testing over health provider-initiated testing), convenience (individual time and place of choice for HIV testing versus clinic-based testing), and time saving. CONCLUSIONS: Mobile phone-connected HIV testing and Web-based clinical care and prevention pathways have the potential to support access to HIV prevention and care, particularly for young people and men. Although mHealth provides a way for individuals to test their candidacy for HIV services, the barriers that can make the service unattractive at the clinic level will also need to be addressed if potential demand is to turn into actual demand

Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care

The health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resource-limited settings, and where AI/ML can provide the most benefit.Comment: Accepted Paper at ICLR 2020 Workshop on Practical ML for Developing Countrie

Telehealth and Mobile Health Applied To IntegratedBehavioral Care: OpportunitiesFor Progress In New Hampshire

This paper is an accompanying document to a webinar delivered on May 16, 2017, for the New Hampshire Citizens Health Initiative (Initiative). As integrated behavioral health efforts in New Hampshire gain traction, clinicians, administrators, payers, and policy makers are looking for additional efficiencies in delivering high quality healthcare. Telehealth and mobile health (mHealth) have the opportunity to help achieve this while delivering a robust, empowered patient experience. The promise of video-based technology was first made in 1964 as Bell Telephone shared its Picturephone® with the world. This was the first device with audio and video delivered in an integrated technology platform. Fast-forward to today with Skype, FaceTime, and webinar tools being ubiquitous in our personal and business lives, but often slow to be adopted in the delivery of medicine. Combining technology-savvy consumers with New Hampshire’s high rate of electronic health record (EHR) technology adoption, a fairly robust telecommunications infrastructure, and a predominately rural setting, there is strong foundation for telehealth and mHealth expansion in New Hampshire’s integrated health continuum

MOSAIC roadmap for mobile collaborative work related to health and wellbeing.

The objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments. For that purpose MOSAIC develops visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. One of the application domains where MOSAIC is active is health and wellbeing. This paper builds on another paper submitted to this same conference, which presents and discusses health care and wellbeing specific scenarios. The aim is to present an early form of a roadmap for validation