Proactive workflow modeling by stochastic processes with application to healthcare operation and management

Advances in real-time location system (RTLS) solutions have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and discovering useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm for workflow modeling by the knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by the integrated analysis of indoor location traces in the hospital environment. In comparison with conventional workflow modeling based on passive workflow logs, one salient feature of our approach is that it can proactively unravel the workflow patterns hidden in the location traces, by automatically constructing the workflow states and estimating parameters describing the transition patterns of moving objects. Specifically, to determine a meaningful granularity for the model, the workflow states are first constructed as regions associated with specific healthcare activities. Then, we transform the original indoor location traces to the sequences of workflow states and model the workflow transition patterns by finite state machines. Furthermore, we leverage the correlations in the location traces between related types of medical devices to reinforce the modeling performance and enable more applications. The results show that the proposed framework can not only model the workflow patterns effectively, but also have managerial applications in workflow monitoring, auditing, and inspection of workflow compliance, which are critical in the healthcare industry.

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