Deviation Detection in Clinical Pathways Based on Business Alignment

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, $\text{A}\ast$ 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.