Authors: Mareike Buhl¹ ²; Lena Schell-Majoor¹; Antje Heinrich³; Charlotte Vercammen⁴ ³ ⁵; Birger Kollmeier¹; Antje Wulff¹
¹Carl von Ossietzky Universität Oldenburg; ²Institut de l’Audition and IHU reConnect; ³University of Manchester; ⁴Sonova AG; ⁵KU Leuven
Background: Data standards provide a means to harmonize data formats, to enhance structured documentation and, thus, combining and reusing data. In audiology, data from clinical routine, research studies or hearing device data logging yield a large potential to be exploited with artificial intelligence, but differences in audiological tests, data formats, or quality hinder data exchange. Accessible, interoperable big audiological data would enable precise analysis and prediction, advancing research and patient care.
Method: Three main data standardization approaches were identified by Vercammen et al. (2026), from which two are open and community-based: Open Electronic Health Record (openEHR) and the Observational Medical Outcomes Partnership (OMOP). These are widely used in different medical fields but differ in purpose and concept. An EFAS working group is currently striving to establish these standards within audiology.
Results: In openEHR, a data model for categorical loudness scaling is published, one for speech intelligibility is under community review, and modelling of the audiogram is ongoing. In OMOP, data models for audiogram data already exist, and their utility for combining data from different sources in the context of hearing aid fitting is currently being explored in a proof-of-concept study.
Conclusion: Community-based data standardization has led to significant progress in both open data standards. As both approaches rely on extensive metadata, future convergence remains possible through transformations between standards. The audiological community is invited to participate by providing expertise and reviewing these emerging data models to strengthen global interoperability.
