A Hybrid Process Mining Framework for Automated Simulation Modelling for Healthcare

Mesabbah, Mohammed; Abo-Hamad, Waleed; McKeever, Susan

doi:10.1109/wsc40007.2019.9004800

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…Martinez-Millana et al, [37] and Batra et al, [38] applied AHP to evaluate healthcare systems, with the former focusing on the features of a process mining dashboard and the latter using a fuzzy AHP strategy [39]. Elhadjamor and Ghannouchi [35] and Mesabbah et al, [40] proposed models for evaluating operational process variables and automated simulation modeling in healthcare, respectively, with the former incorporating data visualization techniques. Pereira et al, [19] developed a methodology for applying process mining in healthcare, emphasizing stakeholder involvement and KPI evaluation.…”

Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Matonya,

Pusztai,

Budai

2024

Decis. Mak. Appl. Manag. Eng.

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This study focuses on the application of process mining in the healthcare sector. Despite its potential to enhance efficiency, reduce costs, and improve patient satisfaction, the selection of process-mining software poses significant challenges due to the diverse nature of healthcare processes and the lack of comprehensive evaluation methods. To bridge this gap, this study employed a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating the Neural Network-Augmented Analytical Hierarchy Process (NNA-AHP) and Grey Relational Analysis—a technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS). The study evaluated process mining software on functionalities, ease of use, cost, technical support, scalability, and security with their respective sub-criteria. The principal results indicate that Disco is the top-performing alternative, followed by Celonis and ProM. Sensitivity analysis was conducted to understand the influence of variations in criteria weights on evaluating alternatives. In the NNA-AHP, Celonis consistently scored the highest. The GRA-TOPSIS method provided performance scores, indicating that higher scores yield better performance. The new hybrid method consolidates evaluations from all methods and offers the most comprehensive and dependable alternative assessment. Disco and its alternatives, Celonis and ProM, are recommended for optimizing healthcare processes. Further research is needed to investigate the integration of NNA-AHP and GRA-TOPSIS in healthcare management, especially in areas beyond business process analysis. This study provides valuable insights for professionals and researchers in the field and contributes to understanding the effectiveness of process mining.

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Section: Ahp Analysis In Evaluating Process Mining Software For Healt...mentioning

confidence: 99%

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Matonya,

Pusztai,

Budai

2024

Decis. Mak. Appl. Manag. Eng.

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“…Then, simulation model instances are created by populating the Petri Net data structure. Mesabbah et al [22] proposed an automated simulation model builder adapted for health-care applications. The methodology couples model generation with machine learning algorithms that allow for the prediction of system performances based on real-time data stream.…”

Section: Model Generation With An Underlying Structurementioning

confidence: 99%

“…Constraints (19) allow or forbid for self-loops depending on the value of L. Constraints (20) and ( 21) limit the number of input and output arcs from each node, depending on ν in and ν out . Constraints (22) state the nature of the decision variables.…”

Section: Mathematical Programming Formulationmentioning

confidence: 99%

Automated manufacturing system discovery and digital twin generation

Lugaresi

Matta

2021

Journal of Manufacturing Systems

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“…For example, process mining methods can be used to learn about changes to real process from process log data. This might ensure that the simulation model accurately reflects the current process (Mesabbah et al 2019). If there is a drift from the real process, the model can be modified accordingly.…”

Section: Symbiotic Simulation Modelmentioning

confidence: 99%

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

Onggo

Corlu

Juan

et al. 2020

Enterprise Information Systems

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A symbiotic simulation system (S3), sometimes also called a 'digital twin', enables interactions between a physical system and its computational model representation. With the goal of supporting operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors. This real-time data is also saved in an enterprise data storage system (EDSS), so it can be used as historical data for future use. Both real-time and historical data are then used as inputs to the different components of an S3, which typically comprises several modules: data acquisition, simulation, optimisation, machine learning, and an 'actuator'. The latter is needed when there is not a human agent between the S3 and the system. Given the amount of data generated by today's smart systems, an S3 needs to be coupled with an EDSS. Furthermore, the S3 may produce a large amount of output data that needs to be stored, since it might be re-used by the machine learning module to make the S3 adaptive in dynamic scenarios. With the goal of supporting real-time operational decision-making -specially in Industry 4.0 applications such as smart cities, smart factories, intelligent transportation systems, and digital supply chains -, this paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. Moreover, the paper reviews the state-of-the-art in S3, and analyses how these systems can interact with EDSSs to make real-time decision making a reality. Finally, the paper also points out several research challenges in S3.

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A Hybrid Process Mining Framework for Automated Simulation Modelling for Healthcare

Cited by 11 publications

References 14 publications

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Optimizing Healthcare Business Processes with Process Mining Software: A Comparative Analysis

Automated manufacturing system discovery and digital twin generation

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

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