Home // International Journal On Advances in Life Sciences, volume 12, numbers 1 and 2, 2020 // View article
Authors:
Oliver Klar
Rasim Atakan Poyraz
Gerd Schneider
Oliver Heinze
Keywords: clinical artificial intelligence; artificial intelligence in healthcare; medical device monitoring; real-time data stream processing; predictive maintenance; Apache Kafka; Apache Flink; Elasticsearch; Kibana.
Abstract:
The rise of Artificial Intelligence (AI) is ubiquitous. In healthcare it is seen as a key technology supporting clinicians in their daily routine. The PART research project (Predictive Analytics of Robustness Testing) aims to develop an AI driven, vendor independent monitoring system, which has the focus on system monitoring, profitability analysis, and predictive maintenance of networked medical devices in a clinical environment. However, before working on AI driven monitoring solutions at Heidelberg University Hospital, we experienced a variety of difficulties according to networked medical devices, data acquisition, standards and protocols, and device interfaces, which must be addressed first. This paper stresses those difficulties and presents a monitoring system of networked medical devices from one operating theater at Heidelberg University Hospital. Continuous data streams of laparoscopic devices out of the surgery room are ingested into the system and analyzed in real-time. The results are stored in an on-premises data store and visualized according to profitability analysis and system monitoring in a dashboard. Further, an outlook is giving including the transformation of the presented monitoring system into the Medical Data Integration Center (MeDIC) of the Heidelberg University Hospital in the future and the connection of more surgery theaters.
Pages: 1 to 9
Copyright: Copyright (c) to authors, 2020. Used with permission.
Publication date: June 30, 2020
Published in: journal
ISSN: 1942-2660