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Riska Wahyuningtyas,Eighty Mardiyan Kurniawati,Budi Utomo,Gatut Hardianto,Hari Paraton,Tri Hastono,Djoko Kuswanto 한국보건의료인국가시험원 2022 보건의료교육평가 Vol.19 No.-
Purpose: Obstetric anal sphincter injury is one of the most common complications during delivery. Simulation models with manikinscan be used as an effective medical learning method to improve students’ abilities before encountering patients. The present studyaimed to describe the development of an anal sphincter injury model and to assess residents’ satisfaction and self-confidence after a perineal repair workshop with an anal sphincter injury simulator in Indonesia. Methods: This was a cross-sectional study with evaluation of outcomes before and after the workshop. We created a silicone-latex simulation anal sphincter injury model. Then, we validated this simulation and used it as a simulation model for the workshop. We askedresidents about their satisfaction with repairing anal sphincter injuries using a simulation model and residents’ self-confidence whenpracticing anal sphincter injury repair. Results: All residents felt the simulation-based workshop was valuable (100%). Most of the scores for the similarity of the simulationmodel were good (about 8 out of maximum 10). The self-assessment of confidence was measured before and after the workshop. Overall self-confidence increased significantly after the workshop in identifying the external sphincter ani (EAS) (P=0.031), suturing theanal mucosa (P=0.001), suturing the internal sphincter ani (P=0.001), suturing the EAS (P<0.001), and evaluating the sphincter anitone (P=0.016). Conclusion: The anal sphincter injury simulator improved residents’ self-confidence in identifying the EAS, suturing the anal mucosa,suturing the internal sphincter ani, suturing the EAS, and evaluating sphincter ani tone.
The study of a full cycle semi-automated business process re-engineering
Sanghwa Lee(이상화),Riska A. Sutrisnowati,Seokrae Won(원석래),Jong Seong Woo(우종성),Hyerim Bae(배혜림) 한국컴퓨터정보학회 2018 韓國컴퓨터情報學會論文誌 Vol.23 No.11
This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.