This research project is a cooperation of several industrial partners (KARL STORZ, smartIT, HBT) and research institutes (Leipzig University, University of Hohenheim, University Hospital Heidelberg) for investigating innovative approaches to improve business processes in surgical environments in hospitals.
The surgery area is the heart of each hospital but also the most expensive part, e.g. each operating room costs 1000-3000€ per hour. As in the whole healthcare area there is an ever increasing cost pressure and every gain in efficiency has to be utilized. Additionally, in the highly dynamic and complex operating room area with manifold physicians, staffs and resources many tasks are not supported with information technology. Further the integration on an information or communication level is quite poor.
Instead of isolated processes and applications in the operating room area with a wide range of unused data sources our solution approach is to proivde efficient surgical processes by smart data services. The research goal is to develop a data-driven way to link and optimize processes and tasks in the operating room area. Besides existing data sources, e.g. hospital information system, a main focus is to integrate data of medical devices, because they allow to get real-time insights into running processes and tasks. Hence a Smart Data Platform is developed, which connects all related data sources and additionally provide solutions for accessing raw data as well as analysed information. Based on this information preceding and following processes can be integrated. Further the monitoring and management of the operating room area can be improved and some surgical tasks can be automated.
The ScaDS Dresden/Leipzig competence centre supported InnOPlan with expertise in big data architectures and consultancy for using the Apache Hadoop ecosystem. Thanks to this a big data framework as a part of the smart data platform was developed. This framework first allows scalable processing of large medical device data in real-time as well as for getting insights in live and historic data by big data analytics.
ScaDS project participant
- Norman Spangenberg