Mark and Carmen applied process mining to understand how Philips' MRI machines are actually used by physicians in the field. The discovered usage patterns increase the test coverage based on real-life behavior for these machines.
Philips Healthcare is a global maker of many healthcare products, among which are imaging systems such as X-Ray, CT, Fluoroscopy and Magnetic Resonance Imaging (MRI) machines. ESI is an independent research organisation for high-tech embedded systems design and engineering.
Carmen Bratosin is a research fellow at ESI and Mark Pijnenburg is a Clinical Verification Lead at Philips Healthcare. Mark and Carmen showed how process mining can be used to analyze the system usage of an MRI machine. Understanding the actual usage patterns in the field is especially interesting to improve the system requirements and to increase the test coverage based on real-life behavior for these machines.
Preparing the data for process mining is not easy, because the logging produced by the MRI machines is only available on a technical log level used for debugging. Furthermore, each physician has their own preferences regarding the machine setup for certain exams, which adds to the complexity. They started by selecting the key activities in the technical log and then aligned them with the user interface elements. Finally, they matched the logged events with the steps described by the American College of Radiology to get them on the abstraction level that a radiology expert would understand. In this way, they were able to compare the actual usage of the MRI system with the expected (and designed) behavior.
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