Authors:
R. S. Mans
1
;
M. H. Schonenberg
1
;
M. Song
1
;
W. M. P. van der Aalst
1
and
P. J. M. Bakker
2
Affiliations:
1
Eindhoven University of Technology, Netherlands
;
2
Academic Medical Center, University of Amsterdam, Netherlands
Keyword(s):
Process mining, healthcare processes.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Enterprise Information Systems
;
Expert Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Medical and Nursing Informatics
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospitals information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights th
at facilitate the improvement of existing careflows.
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