Data Standards in Audiology: A Mixed-Methods Exploration of Community Perspectives and Implementation Considerations
Year: 2025
Type: preprint
Abstract: Objective: This study addresses conceptual issues around data standardisation in audiology, and outlines steps toward achieving it. It reports a survey of the computational audiology community on their current understanding, needs, and preferences concerning data standards. Based on survey findings and a panel discussion, recommendations are made concerning moving forward with standardisation in audiology. Design: Mixed-methods: 1) review of existing standardisation efforts; 2) a survey of the computational audiology community; 3) expert panel discussion in a dedicated session at the 2024 Virtual Conference of Computational Audiology. Sample: Survey: 82 members of the global community; Panel discussion: five experts. Results: A prerequisite for any global audiology database are agreed data standards. Although many are familiar with the general idea, few know of existing initiatives, or have actively participated in them. Ninety percent of respondents expressed willingness to follow or contribute to standardisation efforts. The panel discussed relevant initiatives (e.g. OMOP, openEHR, Noah) and explored both challenges (around harmonisation) and opportunities (alignment with other medical fields and conversion among approaches). Conclusions: Combining conceptual discussion with stakeholder views, the study offers guidance for implementing interoperable data standards in audiology. It highlights community support, key issues to address, and suggests paths for future work.
Source: arXiv (Cornell University)
Authors: Vercammen, Charlotte, Heinrich, Antje, Lesimple, Christophe, Paglialonga, Alessia, Wasmann, Jan-Willem A., Buhl, Mareike
Institutions: University of Manchester, NIHR Manchester Biomedical Research Centre, Sonova (Switzerland), Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Centre National de la Recherche Scientifique, Inserm, Institut Pasteur, Université Paris Cité, Fondation Pour l'Audition, Institut de l'Audition
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979335174
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