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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - EVOTION (EVidenced based management of hearing impairments: Public health pÎŸlicy making based on fusing big data analytics and simulaTION.)

Teaser

Summary

Disabling hearing loss, according to the World Health Organization, affects 466 million people in the world and is predicted to rise to 900 million by 2050, unless action is taken. It is estimated that age-related and other hearing loss is the 4th most prevalent condition and 3rd leading cause of years lived with disability (YLD) in 2016. Hearing loss is a huge problem in the world, expected to become more prevalent as life expectancy rises.
Hearing loss may have a detrimental impact on life quality, and has huge socioeconomic costs, as it increases the risk of cognitive decline, mental illness and depression, and leads to social isolation, unemployment/early retirement and work discrimination. Robust public health methods are required to address hearing loss on a large scale throughout the world. Sound, evidence-based public health policy decisions are essential to develop and implement effective and cost-effective programmes.
Currently, the main intervention for hearing loss is the provision of hearing aids. However, despite accumulating evidence that hearing aids provide considerable benefits in many situations and indications that provision of hearing aids slows down cognitive decline, the treatment is considered costly. Thus, there is a need for increased integration of scientific evidence and policy making within this area of hearing health care.
EVOTION is about enabling seamless collection of big data from all actors and related to treatment of hearing loss in order to inform, support, and develop hearing health care policies. This will be achieved by developing a multi-stakeholder demonstrator platform that combines and analyses heterogeneous big data from clinical repositories and from patientsâ€™ everyday hearing aid use and clinical treatment. The enabling and combining heterogeneous streams of data and their subsequent big data analytics is expected to produce ecologically valid evidence for the formulation and validation of public health policies.
In EVOTION, a large proportion of the Big Data is provided by the patientsâ€™ self-management of their hearing aids, which offers different perspectives and insights from the same data:
â€¢ For the patients, the self-management data means that their hearing aid settings can be personalized without having to formulate their needs verbally.
â€¢ For hearing health care professionals, the same data allows them to characterize the patients with respect to different sound environments and how patients compensate for and cope within them. These insights will allow professionals to optimise the benefit of hearing loss treatments.
â€¢ For the public health policy makers, self-management data contributes to policy decision formulation, and economic analysis such as cost-effectiveness and cost-benefit analysis in which quality of care and use of clinical resources are key inputs.

Work performed

During its first 18 months period EVOTION has collected requirements, designed the architecture, reviewed security, implemented the platform components according to the architecture specifications and developed a novel specification language for the formulation of public health policy decision models (PHPDMs). The platform components enable the platform to;
- collect data from developed EVOTION hearing aids, wearable biosensor, and the EVOTION app (which serves as the collector)
- collect the retrospective and current patient data from the existing clinical repositories
- perform big data analytics, and
- perform decision support simulation.
In parallel to the implementation of the platform, EVOTION has developed a unified protocol for managing over 1200 patients across the clinical sites in Greece, the UK and Denmark. This work includes translation of questionnaires, development and translation of auditory tests, and obtaining ethics approvals with the relevant authorities. The number of patients, the quantity of real life data collected and the duration of the study by far exceeds previous studies performed at clinical sites.

The platform is illustrated in Figure 1: EVOTION platform. The current status of the EVOTION platform is that all three connections are enabled between patients, clinicians, and policy makers while the platform is powered. The joint work has so far enabled the planned use of the EVOTION platform for the clinical validation and the formulation and modeling of public health policies with the EVOTION data.

Final results

Hearing health care is particularly important from a health care and big data perspective, not only due to the substantial impact on public health, but also to the speed with which the treatment (i.e. the hearing device) can adapt to the environment, and thus to the high variability of the environments and the specific treatment over time. Other medical fields with medical devices, e.g., prostheses, insulin pumps, visual implants, etc. share these characteristics and are likely to benefit from the overall approach, technology and findings of the EVOTION project.

EVOTION has successfully initiated the largest ever research study of hearing problems with concurrent collection of dynamic data as part of the standard clinical pathway and with patients. The combination of the novel EVOTION PHPDM Language (and associated tools) together with the platformâ€™s implementation in standard clinical pathways enables EVOTION to a set a new standard for working with public hearing health policies based on big data. The socio-economic impact of hearing problems is currently undergoing intensive investigation as hearing problems become more widespread due to the increased life span, the effects of excessive noise, and increasing awareness of the size of the problem. Moreover, as various health conditions compete for public funding, studies of treatment versus non-treatment in terms of cost, and personal health consequences (emerging from recent longitudinal studies), are gaining attention. The EVOTION platform and lessons learned from it and from its development are highly relevant for all such studies in this field.

The usability and impact of the multi-stakeholder demonstrator EVOTION platform is extended by integrating requirements of patientsâ€™ and health professionalsâ€™ use of the platform, and with small add-ons such as apps or web interfaces. Thus, the EVOTION platform allows all actors to interact with data based on their specific needs, questions and from their own professional perspective. The EVOTION platform is being developed to support health care professionals and health policy makers in identifying, simulating, prioritizing, and monitoring the effectiveness of current and proposed/potential hearing loss interventions.

The multi-focal actor approach in EVOTION, e.g. that the requirements of both patients and health care professionals are included in its architecture and design, indicates that the EVOTION platform is suitable for utilizing big data to support patients and health care professionals in an integrated manner whilst also enabling public health policy makers to make better, faster and more strongly evidence-based decisions. Thus, both by means of its design and its implementation, EVOTION brings patients, health care professionals, and public health policy makers closer together in a single platform, where each collected data point will contribute information for analysis at the personal level, clinical level, and the public policy making level.