ArticleAmerican journal of audiology2018
Big Data for Sound Policies: Toward Evidence-Informed Hearing Health Policies.
Article in American journal of audiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Greater agreement is required to harness the potential of health intelligence: a critical interpretive synthesis.Journal of clinical epidemiology · 2023Review
- Knowledge management tools and mechanisms for evidence-informed decision-making in the WHO European Region: a scoping review.Health research policy and systems · 2023Article
- Can Haptic Stimulation Enhance Music Perception in Hearing-Impaired Listeners?Frontiers in neuroscience · 2021Review
- Public health policy-making for hearing loss: stakeholders' evaluation of a novel eHealth tool.Health research policy and systems · 2020Article
- Fully Synthetic Longitudinal Real-World Data From Hearing Aid Wearers for Public Health Policy Modeling.Frontiers in neuroscience · 2019Article
- Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing.Ear and hearingArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeThe 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.
methodThe 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.
resultsThe 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.
conclusionIn 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.