Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application

Journal of Healthcare Informatics Research, vol. 8, pp. 523–554

Abstract

Abstract: Most process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result, process mining techniques function like black boxes with limited interaction options for analysts, such as simple sliders for filtering infrequent behavior. Recent research tries to break these black boxes by allowing process analysts to provide domain knowledge and guidance to process mining techniques, i.e., hybrid intelligence. Especially, in process discovery-a critical type of process mining-interactive approaches emerged. However, little research has investigated the practical application of such interactive approaches. This paper presents a case study focusing on using incremental and interactive process discovery techniques in the healthcare domain. Though healthcare presents unique challenges, such as high process execution variability and poor data quality, our case study demonstrates that an interactive process mining approach can effectively address these challenges.

Authors 4

1. Daniel Schuster corresponding Aachen Chair of Process and Data Science

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074 Aachen, Germany

   Data Science & Artificial Intelligence, Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, 53757 Sankt Augustin, Germany

2. Elisabetta Benevento

   University of Pisa

   Affiliation as printed

   Centro di Servizi Polo Universitario "Sistemi Logistici", University of Pisa, Via dei Pensieri 60, Livorno, 57128 Italy

   Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, Pisa, 56122 Italy

3. Davide Aloini

   University of Pisa

   Affiliation as printed

   Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, Pisa, 56122 Italy

4. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074 Aachen, Germany

   Data Science & Artificial Intelligence, Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, 53757 Sankt Augustin, Germany