Project R-11557

Interactive Data-Driven Process Simulation for Capacity Management in Healthcare (Research)

Today, healthcare systems worldwide are under constant pressure. On the one hand, increasing population numbers, ageing populations, lifestyle factors, and new technologies are increasing the yearly expenses on healthcare. On the other hand, budgets are under pressure due to economic austerity. In order to provide high-quality care to all patients, healthcare managers are forced to improve their care processes. Efficient Capacity Management (CM) is one of the key aspects to ensure this. This involves, amongst others, determining the suitable resource levels – i.e. staff size, equipment, and facilities. Business Process Simulation (BPS) can be used to support managers for capacity planning decisions. BPS uses a (computer) model to imitate the behaviour of a business process. This approach allows to evaluate the effects of changes before implementing them in the real process. For instance, BPS can be used to determine suitable equipment levels, e.g. by simulating the effect of an additional X-ray scanner on patient waiting times, throughput rates, and staff workload. The emerging field of data-driven process simulation provides promising first results to generate simulation models from process execution information captured in event logs. These simulation models can form the basis to compare the operational effects of various capacity levels. The main advantage of data-driven process simulation over "traditional" simulation model development is the availability and objectivity of event logs compared to information sources, such as interviews, process documentation, and observations. However, some challenges remain in the field of automated BPS model generation. Most importantly, the lack of domain knowledge makes it challenging to extract a reliable and usable simulation model. In addition, event logs often suffer from data quality issues. Because the reliability of the simulation results strongly depends on the quality of the input data, it is imperative to take these problems seriously. Therefore, this PhD will mainly focus on how domain experts can be interactively involved during the generation of simulation models from process execution data. In addition, further support and improvements of modelling tasks are required to generate high-quality simulation models. This should culminate in the development of a prototype tool which allows interactive data-driven generation of BPS models based on event logs and domain knowledge. The derived simulation model can support CM decisions in healthcare. Nevertheless, the prototype would also be usable in many other applications in different fields besides healthcare, such as production planning in manufacturing, supply chain logistics, and transportation.

01 September 2019 - 31 August 2023