Conformance checking in healthcare based on partially ordered event data

There is a continuous pressure to make healthcare processes more efficient and effective without sacrificing quality. Conformance checking can be used to improve processes by analyzing event data and directly relating observed behavior and modeled behavior. Conformance checking provides diagnostics that go far beyond measuring traditional key performance indicators. However, current conformance checking techniques focus on a rather simplistic setting where executions of process instances are sequential and homogeneous whereas healthcare processes are known to be dynamic, complex, and ad-hoc. In healthcare process instances of patients often follow a unique path through the process with one-of-a-kind deviations. Moreover, timestamps are often rather coarse (the date is known, but not the time) resulting in an unreliable ordering of events. As current techniques are unable to handle concurrent events, and the obtained sequential alignments are unable to provide structural information about deviations, the diagnostics provided are often insufficient and misleading. This paper presents a novel approach using partially ordered traces and partially ordered alignments which aims to incorporate unreliability and concurrency in the input while providing diagnostics about deviations that take causalities into account. The approach has been implemented in ProM and was evaluated using event data from a Dutch hospital.