Supporting capacity management decisions in healthcare using data-driven process simulation

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: Capacity management; Data-driven process simulation; Domain knowledge; Healthcare processes; Process mining.