Somnolink – a national initiative to improve data sharing for better healthcare and research in obstructive sleep apnea
Year: 2025
Type: article
Abstract: Introduction: Obstructive sleep apnea (OSA) has a high prevalence and is associated with several severe health conditions. However, it might remain undetected and even when diagnosed, patients show relatively low adherence to the standard therapy. Methods: To optimize OSA diagnosis and therapy, the project Somnolink addresses relevant improvement targets along the patient path by leveraging existing and upcoming medical informatics methods, ranging from health data integration to ML-based clinical decision support. We identified main improvement targets, the relevant data types, data items and data exchange methods, and analysed to which extent health standards and solutions exist already. Results: The different targets require partly similar, partly diverging data types, from questionnaires to multidimensional biosignal recordings. Conclusion: Sleep medicine is a multidisciplinary specialty and healthcare standards typically address only parts of the full data spectrum, including technical standards for biosignal recordings. However, most of the relevant data items and measures are covered by one of the semantic standards, allowing for semantic interoperability.
DOI: 10.3205/mibe000280
Authors: Krefting, Dagmar, Arzt, Michael, Brandt, Moritz, Goldammer, Miriam, Ehrlich, Franz, Maurer, Joachim T., Penzel, Thomas, Sehr, Tony, Rodenbeck, Andrea, Zaschke, Philip, Schöbel, Christoph
Institutions: Universitätsmedizin Göttingen, Campus-Institut Data Science (CIDAS), University of Göttingen, University Hospital Regensburg, University Hospital Carl Gustav Carus, Technische Universität Dresden, Heidelberg University, University Medical Centre Mannheim, Charité - Universitätsmedizin Berlin, Evangelisches Krankenhaus Göttingen-Weende, Stiftung Universitätsmedizin Essen
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7080565292
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