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Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

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2 Papers

Key Factors in Achieving Intersectoral Interoperability: A Scoping Review

Topic: Interoperability and Data Integration

Authors: Eveline Prochaska ¹ , Franziska Bathelt ² , Michelé Zoch ¹ , Antonia Ewald ² and Elisa Henke ¹

Affiliations: ¹ Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany , ² Thiem-Research GmbH, Cottbus, Germany

Digital Object Identifier: 10.5220/0013373500003911

Proceedings of HEALTHINF 2025, ISBN: 978-989-758-731-3

Quick Abstract

Intersectoral interoperability is a fundamental basis for effective collaboration and seamless information exchange across various sectors of the healthcare system. This paper presents a scoping review to examine the current state of research into intersectoral interoperability, focusing on the technical, syntactic, semantic, and organizational levels. Key factors identified include the adoption of international standards for data formats, terminologies, and communication protocols, as well as the establishment of trusted governance structures and compliance with ethical and legal requirements. Syntactic interoperability was most frequently addressed, followed by technical and semantic aspects, with organizational factors also playing a significant role.

Towards AI-Based Kinematic Data Analysis in Hand Function Assessment: An Exploratory Approach

Topic: Health Monitoring Devices

Authors: Eveline Prochaska and Martin Sedlmayr

Affiliation: Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

Digital Object Identifier: 10.5220/0013376200003911

Proceedings of BIODEVICES 2025, ISBN: 978-989-758-731-3

Quick Abstract

Neurological diseases, such as multiple sclerosis (MS), significantly affect hand function, impacting patients' independence and quality of life. The Nine Hole Peg Test (NHPT) is a standardized tool widely used to assess upper limb motor function. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) in the analysis of kinematic data obtained from a digitized NHPT prototype. The digital NHPT captures detailed motion data, including timestamps for each action, movement patterns, and filling sequences, enabling advanced analyses of motor and cognitive processes. AI-driven methods, such as clustering, anomaly detection, and pattern recognition, provide innovative ways to evaluate fine motor skills, detect subtle anomalies, and monitor disease progression. The combination of enhanced data collection and AI-based analytics offers a comprehensive and objective approach to understanding hand function, supporting individualized therapy development, and improving clinical diagnostics. This integration represents a significant advancement in the evaluation and management of neurological diseases.