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Discovering and Improving the Processes of an Iranian Psychiatric Hospital Using Process Mining
Roshan, Mohammad Amin | 2023
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 56684 (51)
- University: Sharif University of Technology, International Campus, Kish Island
- Department: Science and Engineering
- Advisor(s): Hassan Nayebi, Erfan
- Abstract:
- Providing quality hospital services depends on the efficient and correct implementation of processes. Therapeutic care processes are a set of activities that are carried out with the aim of diagnosing, treating and preventing any disease in order to improve and promote the patient's health. The purpose of this study is to use process mining techniques to discover and improve healthcare processes. The case study of this research is a psychiatric hospital in Shiraz. The approach implemented in this research consists of three main stages including data pre-processing, model discovery phase, and analysis phase. Three algorithms including Heuristic Miner, Inductive Miner, and ILP Miner were used to discover the process of admission to discharge of the disabled. In this research, ProM plugins were used to apply process mining techniques while extracting and cleaning Event Logs, Discovering Process Models, applying compliance checks, applying performance or organizational analysis. This research suggests the use of abstraction techniques and trace clustering to simplify the unstructured form of the model. Then, evaluating the quality criteria among the output models to select the best process model that describes the path of care for all groups of people with disabilities. The evaluation was done based on the criteria of appropriateness, accuracy, generalization and simplicity. Based on the comparison of the four determined criteria, the best discovered process model of the Heuristic Miner algorithm was determined. In this research, using organizational and performance analysis, insights can be extracted from care flows and event reporting
- Keywords:
- Process Mining ; Clustering System ; Event Detection ; Process Improvement ; Clustering ; Event Log ; ProM Plugins
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