Data-Driven Business Process Improvement
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Abstract
This research presents an analytical method to improve organizational workflow efficiency by utilizing data from the organization's information system, which was recorded as event logs from a hospital's outpatient department. Through the application of Process Mining techniques using the Disco tool and Fuzzy Miner algorithm, we created a process model for efficiency analysis. The research results demonstrated the effectiveness of the proposed method in analyzing outpatient service processes involving 12,836 patients, which revealed 4,293 distinct process variants. This diversity reflects the complexity of medical service delivery. Through frequency and time analysis, our research demonstrates how organizations can optimize resource allocation, establish SLAs, and develop effective staff training plans. The study confirms that Process Mining techniques provide accurate and effective means for improving work processes through the analysis of existing organizational data.
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