Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Process Aware Information Systems: Bridging People and Software Through Process Technology
Process Aware Information Systems: Bridging People and Software Through Process Technology
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Conformance checking of processes based on monitoring real behavior
Information Systems
Business Process Management: Concepts, Languages, Architectures
Business Process Management: Concepts, Languages, Architectures
Towards comprehensive support for organizational mining
Decision Support Systems
Robust Process Discovery with Artificial Negative Events
The Journal of Machine Learning Research
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Investigating clinical care pathways correlated with outcomes
BPM'13 Proceedings of the 11th international conference on Business Process Management
| Hi-index | 0.00 |
Since healthcare processes are pre-eminently heterogeneous and multi-disciplinary, information systems supporting these processes face important challenges in terms of design, implementation and diagnosis. Nonetheless, streamlining clinical pathways with the purpose of delivering high quality care while at the same time reducing costs is a promising goal. In this paper, we propose a methodology founded on process mining for intelligent analysis of clinical pathway data. Process mining can be considered a valuable approach to obtain a better understanding about the actual way of working in human-centric processes such as clinical pathways by investigating the event data as recorded in healthcare information systems. However, capturing tangible knowledge from clinical processes with their ad hoc and complex nature proves difficult. Accordingly, this paper proposes a data analysis methodology focussing on the extraction of tangible insights from clinical pathway data by adopting both a drill up and a drill down perspective.