Following the success of the first two editions of the minitrack on “Process Mining in Healthcare” at HICSS-55 and HICSS-56, we are happy to announce the third edition of this minitrack at HICSS-57.
Process mining has recently gained increasing attention in information science as a set of concepts, methods, and techniques to analyze the execution of business processes. In traditional application domains of business process technology, including finance and electronic commerce, process mining provides insights into how exactly business processes are performed. Since then, process mining has experienced an explosion of its use in other more novel application scenarios, including logistics, public administration, and, more prominently, healthcare.
In this minitrack, we intend to provide a forum for researchers and practitioners to discuss novel ideas in the area of process mining in healthcare environments.
This minitrack is supported by the Process-Oriented Data Science for Healthcare Alliance, the chapter within the IEEE Task Force on Process Mining to promote the use of Process Mining in Healthcare.
We welcome papers from a wide variety of topics related to process mining in healthcare. The topics of the minitrack include, but are not limited to, the following aspects:
Data preparation for process mining in healthcare
Event log generation in healthcare environments
Data privacy in healthcare
Process mining for interleaving treatment processes
Healthcare processes in the age of pandemics
Discovery and analysis of patient treatment processes
Healthcare process analytics
Care pathway construction and analysis
Formalization of medical knowledge and reasoning
Process conformance checking and clinical guidelines
Healthcare procedures training, assessment, and feedback
Data-driven compliance analysis for health care processes
Healthcare information, systems, and architectures
Logistics processes in healthcare
Processes in development, production, and provision of medication
Human-in-the-loop approaches to process mining in healthcare
Methods and frameworks to deploy process mining in healthcare
All submissions must fully conform to the HICSS format (i.e., double-column, single spaced, no more than 10 pages including references, tables, etc.) and the author information is omitted. Detailed instructions for authors can be found here.
June 15, 2023 - Paper submission deadline (11:59 pm HST)
August 17, 2023 - Notification for authors
September 22, 2023 - Deadline for authors to submit the final manuscript for publication
October 1, 2023 - Deadline for at least one author of each paper to register for the conference
January 3-6, 2024 - HICSS Conference
Iris Beerepoot, Utrecht University, The Netherlands
Ann-Kristin Cordes, University of Kiel, Germany
Benjamin Dalmas, Computer Research Institute of Montreal, Canada
Tuğba Gürgen Erdoğan, Hacettepe University, Turkey
Carlos Fernandez-Llatas, Universitat Politècnica de Valencia, Spain
Owen Johnson, University of Leeds, United Kingdom
Sander Leemans, RWTH Aachen University Aachen, Germany
Renata Medeiros de Carvalho, Eindhoven University of Technology, The Netherlands
Rüdiger Pryss, University of Würzburg, Germany
Hajo Reijers, Utrecht University, The Netherlands
Arik Senderovich, University of Toronto, Canada
Marcos Sepúlveda, Pontificia Universidad Católica de Chile, Chile
Minseok Song, Pohang University of Science and Technology, South Korea
Emilio Sulis, University of Torino, Italy
Moe Thandar Wynn, Queensland University of Technology, Australia
Francesca Zerbato, University of St. Gallen, Switzerland
Saturday, January 6th, 2024 - 4.00 - 5.30 p.m. (HST)
Room Coral 1
4.00 - 4.15 p.m. (HST): Welcome and introduction to the minitrack | Niels Martin
4.15 - 5.30 p.m. (HST): Paper presentations
Whetting the SWORD: Detecting Workarounds by Using Active Learning and Logistic Regression | Wouter Van Der Waal, Inge van de Weerd, Saskia Haitjema, Teus Kappen, Hajo A. Reijers
Care Records and Healthcare Processes: Adding Context to Clinical Codes | Lara Chammas, Owen Dwyer, Emanuel Sallinger, Jim Davies, Eva J.A. Morris
Process Mining Using Electronic Health Records Data - Quo Vadis? Reflections from Observing Nurses' Activities and Data Registration Behavior | Niels Martin, Isabeau Gielen, Jochen Bergs
Technische Universitaet München, Germany
Hasselt University, Belgium
(Primary Contact for the Minitrack)
HPI, University of Potsdam, Germany