Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

Public health policy for management of hearing impairments based on big data analytics: EVOTION at Genesis

The holistic management of hearing loss (HL) requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; protection from noise; and socioeconomic inclusion of HL patients. However, currently the evidential basis for forming such policies is limited. Holistic HL management policies require the analysis of heterogeneous data, including Hearing Aid (HA) usage, noise episodes, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data. To utilise these data in forming holistic HL management policies, EVOTION, a new European research and innovation project, aims to develop an integrated platform supporting: (a) the analysis of related datasets to enable the identification of causal and other effects amongst them using various forms of big data analytics, (b) policy decision making focusing on the selection of effective interventions related to the holistic management of HL, based on the outcomes of (a) and the formulation of related public health policies, and (c) the specification and monitoring of such policies in a sustainable manner. In this paper, we describe the EVOTION approach

Fully Synthetic Longitudinal Real-World Data From Hearing Aid Wearers for Public Health Policy Modeling

Here, we share the first outcome of EVOTION (www.h2020evotion.eu) in the form of a data-set to inspire, encourage, and motivate a data-driven analytical approach to evidence-based healthcare policy modeling using real-world longitudinal data. The data-set includes information relating to patterns of real-world hearing aid usage and sound environment exposure. Undoubtedly, many such data-sources will be available for researchers and policy-makers in the future, and the data-set presented here can act as a first step of building and testing potential statistical model

Public health policy-making for hearing loss: stakeholders' evaluation of a novel eHealth tool

Background: Hearing loss (HL) affects 466 million people of all ages worldwide, with a rapidly increasing prevalence, and therefore requires appropriate public health policies. Multi-disciplinary approaches that make use of eHealth services can build the evidence to influence public policy. The European Union-funded project EVOTION developed a platform that is fed with real-time data from hearing aids, a smartphone, and additional clinical data and makes public health policy recommendations based on hypothetical public health policy-making models, a big data engine and decision support system. The present study aimed to evaluate this platform as a new tool to support policy-making for HL. / Methods: A total of 23 key stakeholders in the United Kingdom, Croatia, Bulgaria and Poland evaluated the platform according to the Strengths, Weaknesses, Opportunities and Threats methodology. / Results: There was consensus that the platform, with its advanced technology as well as the amount and variety of data that it can collect, has huge potential to inform commissioning decisions, public health regulations and affect healthcare as a whole. To achieve this, several limitations and external risks need to be addressed and mitigated. Differences between countries highlighted that the EVOTION tool should be used and managed according to local constraints to maximise success. / Conclusion: Overall, the EVOTION platform can equip HL policy-makers with a novel data-driven tool that can support public health policy-making for HL in the future

EHealth and Its Role in Supporting Audiological Rehabilitation: Patient Perspectives on Barriers and Facilitators of Using a Personal Hearing Support System With Mobile Application as Part of the EVOTION Study

BACKGROUND: Hearing loss is a major public health challenge. Audiology services need to utilise a range of rehabilitative services and maximise innovative practice afforded by technology to actively promote personalized, participatory, preventative and predictive care if they are to cope with the social and economic burden placed on the population by the rapidly rising prevalence of hearing loss. Digital interventions and teleaudiology could be a key part of providing high quality, cost-effective, patient-centred management. There is currently very limited evidence that assesses the hearing impaired patient perspective on the acceptance and usability of this type of technology. AIM: This study aims to identify patient perceptions of the use of a hearing support system including a mobile smartphone app when used with Bluetooth-connected hearing aids across the everyday life of users, as part of the EVOTION project. METHODS: We applied a questionnaire to 564 participants in three countries across Europe and analysed the following topics: connectivity, hearing aid controls, instructional videos, audiological tests and auditory training. KEY FINDINGS: Older users were just as satisfied as younger users when operating this type of technology. Technical problems such as Bluetooth connectivity need to be minimised as this issue is highly critical for user satisfaction, engagement and uptake. A system that promotes user-controllability of hearing aids that is more accessible and easier to use is highly valued. Participants are happy to utilise monitoring tests and auditory training on a mobile phone out of the clinic but in order to have value the test battery needs to be relevant and tailored to each user, easy to understand and use. Such functions can elicit a negative as well as positive experience for each user. CONCLUSION: Older and younger adults can utilise an eHealth mobile app to complement their rehabilitation and health care. If the technology works well, is tailored to the individual and in-depth personalised guidance and support is provided, it could assist maximisation of hearing aid uptake, promotion of self-management and improving outcomes

Research investigations on the use or non-use of hearing aids in the smart cities

This study aims to explore factors influencing behavioral intention to adopt hearing aids among old adults in smart cities. It argues that trust is a moderator to influence the relationship between attitude, subjective norm and individual's behavioral intention in smart cities. This study tests hypotheses using a sample of 103 respondents from six smart cities in China. The results reveal that attitude is main factor influencing individual's behavioral intention. Subjective norm and trust are both not statistically significant at the 95% confidence interval in the model of multiple-regression. Interestingly, it finds that trust moderates the relationship between subjective norm and individual's behavioral intention. It means that the audiologists' advice can positively affect person's behavioral intention in smart cities. The findings imply that the Theory of Reasoned Action can be partially used to explain the person's behavioral intention in Chinese context. This study contributes to encourage old people to use smart hospitals to consult audiologists about hearing loss and hearing aids rehabilitation. Hence, hearing aids can improve their quality of life (QoL), which can be reflected by the improved standard of living, better access to treatments and also the positive sentiment about their life, including comfort, friendship, happiness and a closer connection to the society

The Consent Myth: Improving Choice for Patients of the Future

Consent has enjoyed a prominent position in the American privacy system since at least 1970, though historically, consent emerged from traditional notions of tort and contract. Largely because consent has an almost deferential power as a proxy for consumer choice, organizations increasingly use consent as a de facto standard for demonstrating privacy commitments. The Department of Health and Human Services and the Federal Trade Commission have integrated the concept of consent into health care, research, and general commercial activities. However, this de facto standard, while useful in some contexts, does not sufficiently promote individual patient interests within leading health technologies, including the Internet of Health Things and Artificial Intelligence. Despite consent’s prominence in United States law, this Article seeks to understand, more fully, consent’s role in modern health applications, then applies a philosophical-legal lens to clearly identify problems with consent in its current use. This Article identifies the principle issues with substituting consent for choice, the “consent myth,” a collection of five problems, then proposes principles for addressing these problems in contemporary health technologies

A Modelling Framework for Evidence-Based Public Health Policy Making

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources