Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37267
Title: Supporting Capacity Management Decisions in Healthcare using Data-Driven Process Simulation
Authors: VAN HULZEN, Gerard 
MARTIN, Niels 
DEPAIRE, Benoit 
SOUVERIJNS, Geert 
Issue Date: 2022
Publisher: Elsevier Limited
Source: JOURNAL OF BIOMEDICAL INFORMATICS, 129 (Art N° 104060)
Abstract: Healthcare managers are confronted with various Capacity Management decisions to determine appropriate levels of resources such as equipment and staff. Given the significant impact of these decisions, they should be taken with great care. The increasing amount of process execution data – i.e. event logs – stored in Hospital Information Systems (HIS) can be leveraged using Data-Driven Process Simulation (DDPS), an emerging field of Process Mining, to provide decision-support information to healthcare managers. While existing research on DDPS mainly focuses on the fully automated discovery of simulation models from event logs, the interaction between process execution data and domain expertise has received little attention. Nevertheless, data quality issues in real-life process execution data stored in HIS prevent the discovery of accurate and reliable models from this data. Therefore, complementary information from domain experts is necessary. In this paper, we describe the application of DDPS in healthcare by means of an extensive real-life case study at the radiology department of a Belgium hospital. In addition to formulating our recommendations towards the radiology management, we will elaborate on the experienced challenges and formulate recommendations to move research on DDPS within a healthcare context forward. In this respect, explicit attention is attributed to data quality assessment, as well as the interaction between the use of process execution data and domain expertise.
Keywords: Data-driven process simulation;Process mining;Capacity management;Healthcare processes;Domain knowledge
Document URI: http://hdl.handle.net/1942/37267
ISSN: 1532-0464
e-ISSN: 1532-0480
DOI: 10.1016/j.jbi.2022.104060
ISI #: 000794840600003
Rights: © Elsevier Inc. 2022
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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