Challenges faced by Siemens Healthineers in managing MRI scanner data
Since 2007, Siemens Healthineers has collected data from its MRI scanners over the Siemens Remote Service connection. Hundreds of log files feed three essential jobs: helping service teams stay ahead of scanner problems, monitoring field stability and meeting post-market surveillance requirements. Two decades on, the MRI install base alone generates billions of event log lines and roughly 100 TB of data each month. Every new scanner and software upgrade adds more.
Designed in 2007 with scalability in mind, XMART served the business for two decades and grew into one of the largest SQL deployments Microsoft had seen. But the economics and reach of an on-premises design eventually ran into three limits: cost, data sharing and access.
Cost challenges of scaling MRI data management
As volumes climbed, some Analysis Services cubes had to be taken offline because the data outgrew them. Scaling also meant provisioning servers years ahead of demand, sized for where the data would be rather than where it was. The approach kept working, but its cost curve continued to rise.
Data sharing and access bottlenecks across teams
Each business line ran its own server silo, so moving data to another team meant exporting it, encrypting it and physically transferring large files. Because the history dates back to 2007, requests for older records incurred significant computational overhead. The same data was often delivered and reworked two or three times. “If someone needs historical data, it could take weeks or months to get this data,” said Georg Görtler, Product Manager for XMART at Siemens Healthineers.
More than 1,000 people across sales, service and headquarters functions relied on Qlik Sense dashboards, and most worked in Excel rather than SQL. Any questions the dashboards did not already answer were routed to a central team. At the same time, hospitals increasingly sought operational insight through teamplay to schedule patients and get more from their scanners. “There has always been a bottleneck in a central team, where users wait, and prioritization has to happen,” said Michael Kelm, Cloud Data Platform Lead at Siemens Healthineers.
