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Am J Audiol. 2018 Nov 19;27(3S):493-502. doi: 10.1044/2018_AJA-IMIA3-18-0003.

Big Data for Sound Policies: Toward Evidence-Informed Hearing Health Policies.

Author information

1
Eriksholm Research Centre, Oticon A/S, Denmark.
2
Biomedical Engineering Laboratory, National Technical University of Athens, Greece.
3
Pazardzhik Regional Administration, Bulgaria.
4
Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
5
Institute of Public Health for the Osijek Baranya County, Croatia.
6
City, University of London, United Kingdom.
7
Athens Medical Group, Greece.
8
Oticon Medical, Denmark.
9
Department of Behavioural Sciences and Learning, Linköping University, Sweden.

Abstract

Purpose:

The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies.

Method:

The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes.

Results:

The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care.

Conclusion:

In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.

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