TY - GEN
T1 - On analyzing process compliance in skin cancer treatment
T2 - 24th International Conference on Advanced Information Systems Engineering, CAiSE 2012
AU - Binder, Michael
AU - Dorda, Wolfgang
AU - Duftschmid, Georg
AU - Dunkl, Reinhold
AU - Fröschl, Karl Anton
AU - Gall, Walter
AU - Grossmann, Wilfried
AU - Harmankaya, Kaan
AU - Hronsky, Milan
AU - Rinderle-Ma, Stefanie
AU - Rinner, Christoph
AU - Weber, Stefanie
PY - 2012
Y1 - 2012
N2 - Process mining has proven itself as a promising analysis technique for processes in the health care domain. The goal of the EBMC 2 project is to analyze skin cancer treatment processes regarding their compliance with relevant guidelines. For this, first of all, the actual treatment processes have to be discovered from the available data sources. In general, the L * life cycle model has been suggested as structured methodology for process mining projects. In this experience paper, we describe the challenges and lessons learned when realizing the L * life cycle model in the EBMC 2 context. Specifically, we provide and discuss different approaches to empower data of low maturity levels, i.e., data that is not already available in temporally ordered event logs, including a prototype for structured data acquisition. Further, first results on how process mining techniques can be utilized for data screening are presented.
AB - Process mining has proven itself as a promising analysis technique for processes in the health care domain. The goal of the EBMC 2 project is to analyze skin cancer treatment processes regarding their compliance with relevant guidelines. For this, first of all, the actual treatment processes have to be discovered from the available data sources. In general, the L * life cycle model has been suggested as structured methodology for process mining projects. In this experience paper, we describe the challenges and lessons learned when realizing the L * life cycle model in the EBMC 2 context. Specifically, we provide and discuss different approaches to empower data of low maturity levels, i.e., data that is not already available in temporally ordered event logs, including a prototype for structured data acquisition. Further, first results on how process mining techniques can be utilized for data screening are presented.
KW - Data Quality
KW - Healthcare Processes
KW - Process Mining
KW - Process Modeling
UR - http://www.scopus.com/inward/record.url?scp=84867790395&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31095-9_26
DO - 10.1007/978-3-642-31095-9_26
M3 - Conference contribution
AN - SCOPUS:84867790395
SN - 9783642310942
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 398
EP - 413
BT - Advanced Information Systems Engineering - 24th International Conference, CAiSE 2012, Proceedings
Y2 - 25 June 2012 through 29 June 2012
ER -
