gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Data Recording System for Anesthesiology, Patient Monitor and Surgical Devices in Operating Rooms

Meeting Abstract

  • Tamer Abdulbaki Alshirbaji - Hochschule Furtwangen, Villingen-Schwenningen, Germany
  • Nour Aldeen Jalal - Hochschule Furtwangen, Villingen-Schwenningen, Germany
  • Lars Mündermann - Karl Storz SE & Co. KG, Tuttlingen, Germany
  • Knut Möller - Hochschule Furtwangen, Villingen-Schwenningen, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 92

doi: 10.3205/19gmds174, urn:nbn:de:0183-19gmds1743

Veröffentlicht: 6. September 2019

© 2019 Abdulbaki Alshirbaji et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Integrated operating rooms typically connect medical devices providing the clinical user a complete control over environment, device setting and digital management of intervention-related data. Consequently, the opportunity to analyze and present data from different perspectives and with different objectives has arisen. The available integrated ORs are so far designed as closed systems, thus connecting co-existing systems from different manufactures e.g. anesthesia machines and surgical devices is demanding. The purpose of this project is to facilitate data collection from anesthesiology, patient monitoring and surgical devices. The main goal is to explore if and how surgical actions affect the physiological situation of patients and how to employ this knowledge for assisting the surgical team and improve patient safety. The study is performed on laparoscopic procedures, and the data are going to be recorded at the Schwarzwald-Baar Klinikum (SBK) in Villingen-Schwenningen (Germany). Therefore, this part of the project focuses on the overall architecture for collecting data in the operating theater at the SBK. In this work, (i) the system architecture (i.e. hardware components), (ii) software architecture and (iii) required protocols for synchronous recording of data in the OR are described. The proposed framework allows to synchronously record all data available in the OR and subsequently to analyze “big data” obtained with machine learning approaches.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.