TY - JOUR
T1 - Recommendations for enhancing the usability and understandability of process mining in healthcare
AU - Martin, Niels
AU - De Weerdt, Jochen
AU - Fernández-Llatas, Carlos
AU - Gal, Avigdor
AU - Gatta, Roberto
AU - Ibáñez, Gema
AU - Johnson, Owen
AU - Mannhardt, Felix
AU - Marco-Ruiz, Luis
AU - Mertens, Steven
AU - Munoz-Gama, Jorge
AU - Seoane, Fernando
AU - Vanthienen, Jan
AU - Wynn, Moe Thandar
AU - Boilève, David Baltar
AU - Bergs, Jochen
AU - Joosten-Melis, Mieke
AU - Schretlen, Stijn
AU - Van Acker, Bart
N1 - Publisher Copyright: © 2020 Elsevier B.V.
PY - 2020/9
Y1 - 2020/9
N2 - Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.
AB - Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.
KW - Event log
KW - Health information system
KW - Healthcare processes
KW - Hospital information system
KW - Process analysis
KW - Process execution data
KW - Process improvement
KW - Process mining
UR - http://www.scopus.com/inward/record.url?scp=85091991155&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.artmed.2020.101962
DO - https://doi.org/10.1016/j.artmed.2020.101962
M3 - مقالة
SN - 0933-3657
VL - 109
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 101962
ER -