In healthcare, more and more process execution information is stored in Hospital Information Systems. This data, in conjunction with data-driven process simulation, can be used, e.g. to support hospital management wit...
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ISBN:
(纸本)9783030726928;9783030726935
In healthcare, more and more process execution information is stored in Hospital Information Systems. This data, in conjunction with data-driven process simulation, can be used, e.g. to support hospital management with Capacity Management decisions. However, real-life event logs in healthcare often suffer from data quality issues, affecting the reliability of simulation results. In this work, we illustrate the effects of disregarding data quality issues on simulation outcomes and the importance of domain knowledge using a case study at the radiology department of a hospital. Current literature on data-driven process simulation acknowledges the need for domain expertise but does not provide a framework for conceptualising the involvement of domain experts. Therefore, we propose a novel conceptual framework which interactively involves experts during data-drivensimulation model development.
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 t...
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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.
Sorting plants are crucial for effective recycling, but their optimization can be challenging due to the heterogeneity of waste streams. We introduce a novel approach to holistically optimize sorting plants using digi...
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Sorting plants are crucial for effective recycling, but their optimization can be challenging due to the heterogeneity of waste streams. We introduce a novel approach to holistically optimize sorting plants using digital twins containing data-drivenprocess models. To demonstrate their technical feasibility, we developed a data -drivenprocess model for industrial-scale sensor-based sorting (SBS) units by combining near-infrared process monitoring with machine learning. Our results indicate a sorting performance change (F1-score) in the SBS unit by 0.22 a% for +1% occupation density and +0.19 a% for +1 wt% target material share. An artificial neural network predicted the SBS behavior with a 3.0% mean absolute error. Our case study demonstrates the potential of data-drivenprocess models for digital twins by clarifying the influence of throughput fluctuations on SBS performance and simulating different SBS cascade designs, thus paving the way towards improved design and operation of sorting plants and a more circular future.
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