The discovery, analysis and comparison of the patient discharge process - A case study.
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Introduction The quality of business processes is highly dependent on the suitability and efficiency of their execution. Healthcare emphasizes this dependency as the processes within the domain are concerned with activities that aim to diagnose, treat and prevent diseases in order to improve the quality of life for its patients. Process mining aims to discover healthcare processes with a data-driven approach. A common trend among the literature is that the basis of its analysis is an event log that is concerned with hospital-wide patient process that spans multiple departments or only a single department/patient group. The comparison of multiple departments concerned with the same care process is unexplored in current literature. Objectives This thesis aims to discover, analyse and compare the patient discharge process across three different hospital departments with the use of process mining. Methods This thesis is a case study using a generalized framework for process mining projects in practise called the generalized PM methodology. The six phases of the generalized PM methodology have been divided into an 8-stage process involving: (1) Planning, (2) Data collection, (3) Log exploration, (4) Log pre-processing, (5) Process model discovery, (6) Model evaluation, (7) Model analysis and comparison, (8) Process improvement. Additional interviews with domain experts were conducted for several stages to fine tune the output for these stages. Results The results of this thesis give insight in what process mining metrics can be utilized to compare an unstructured care process across several hospital departments and show what type of activities are most prone to showing interdepartmental discrepancies based on the results of the case study.