Abstract
Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally, A ∗ algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments.
Highlights
Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work
To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways
A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments
Summary
A tremendous amount of data is stored in hospital information systems. We can extract a lot of medical events from them. Definition 4 (alignment) Given a set A including activities, a Petri net N, and a trace σ, the alignment between σ and A, namely, c ∈ (A≫ × T≫)∗ is a moving sequence (where ≫ denotes no movement and A≫ A ∪ {≫}) which has the following rules:. According to Definition 4, alignment is a sequence of movements, and movement reflects the correlation between the activities and transitions For log movement, it means that an activity cannot be executed in the model; for model movement, it means that a transition is not observed in the trace; for synchronous movement, it means that the activity can correspond to the transition; for illegal movement, it will not occur in actual business process, so it will be ignored in this paper. (4) e value of lc ((a, t)) is +∞, when (a, t) is illegal movement
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
