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• W4225150511 abstract "The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved by pre-processing or data cleaning techniques, leading to inaccurate results. We take an innovative Process Mining (PM) approach, termed Interactive Process Discovery (IPD), which combines domain knowledge with available data. This approach can overcome the limitations of noisy and incomplete event logs by putting “humans in the loop”, leading to improved business process modelling. This is particularly valuable in healthcare, where physicians have a tacit domain knowledge not available in the event log, and, thus, difficult to elicit. We conducted a two-step approach based on a controlled experiment and a case study in an Italian hospital. At each step, we compared IPD with traditional PM techniques to assess the extent to which domain knowledge helps to improve the accuracy of process models. The case study tests the effectiveness of IPD to uncover knowledge-intensive processes extracted from noisy real-life event logs. The evaluation has been carried out by exploiting a real dataset of an Italian hospital, involving the medical staff. IPD can produce an accurate process model that is fully compliant with the clinical guidelines by addressing data quality issues. Accurate and reliable process models can support healthcare organizations in detecting process-related issues and in taking decisions related to capacity planning and process re-design." @default.
• W4225150511 created "2022-05-01" @default.
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• W4225150511 date "2022-06-01" @default.
• W4225150511 modified "2023-09-30" @default.
• W4225150511 title "How Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare" @default.
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• W4225150511 doi "https://doi.org/10.1016/j.jbi.2022.104083" @default.
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