Somnolink – Vernetzte Schlafdaten und Entscheidungshilfen entlang des Patient/-innenpfades für eine bessere Versorgung bei obstruktiver Schlafapnoe
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
- Department of Neurology
- Institute for Medical Informatics and Biometry
- University of Göttingen
- University of Duisburg-Essen
- University of Regensburg
- Heidelberg University
- Charité – Universitätsmedizin Berlin
- Evangelical Hospital Göttingen-Weende
Abstract
Obstructive sleep apnea (OSA) – characterized by interruptions in the nocturnal respiratory flow – is a widespread condition in Germany with an estimated 26 million people affected. For optimal treatment, early diagnosis and usually permanent therapeutic measures are necessary. Medical data science, i.e.; making health data available and making use of them – for example with artificial intelligence (AI) methods – can contribute in a variety of ways to better research into OSA and the care of affected persons. The new legal framework conditions such as the German Health Data Utilization Act as well as technological innovations such as the electronic patient record and sensor-based health monitoring offer new opportunities to accompany and involve OSA patients along the entire patient pathway. In the Somnolink project, these possibilities are being investigated and put into practice.
| Translated title of the contribution | Somnolink – connected sleep data and decision support along the patient journey to improve obstructive sleep apnea healthcare |
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Details
| Original language | German |
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| Pages (from-to) | 21-27 |
| Number of pages | 7 |
| Journal | Atemwegs- und Lungenkrankheiten |
| Volume | 51 |
| Issue number | 1 |
| Publication status | Published - Jan 2025 |
| Peer-reviewed | Yes |
External IDs
| ORCID | /0000-0003-2126-290X/work/201623803 |
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| ORCID | /0000-0002-9888-8460/work/201624905 |
Keywords
ASJC Scopus subject areas
Keywords
- artificial intelligence, decision support, digitalization, health record, individualized medicine, medical data science, obstructive sleep apnea, patient participation, sleep lab, wearables