Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

Alessandro Stefanini, Davide Aloini, Riccardo Dulmin, Valeria Mininno


The knowledge of patient-flow is very important for healthcare organizations, because strongly connected to effectiveness and efficiency of resource allocation. Unfortunately, traditional approaches to process analysis are scarcely effective and low efficient: they are very time-consuming and they may not provide an accurate picture of healthcare processes. Process mining techniques help to overcome these problems. This paper proposes a methodology for building a DRG related patient-flow using process mining. Findings show that it is possible to discover the different sequences of activities associated with a DRG related process. Managerial implications concern both process identification, analysis and improvement. A case study, based on a real open data set, is reported.


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Paper Citation

in Harvard Style

Stefanini A., Aloini D., Dulmin R. and Mininno V. (2016). Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 438-443. DOI: 10.5220/0005817804380443

in Bibtex Style

author={Alessandro Stefanini and Davide Aloini and Riccardo Dulmin and Valeria Mininno},
title={Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)},

in EndNote Style

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)
TI - Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining
SN - 978-989-758-170-0
AU - Stefanini A.
AU - Aloini D.
AU - Dulmin R.
AU - Mininno V.
PY - 2016
SP - 438
EP - 443
DO - 10.5220/0005817804380443