Combining Process Mining and Simulation for Outpatient Pediatric Ophthalmology: A Case Study
Source abstract
Abstract
Process mining (PM) and process simulation (PS) are key disciplines for analyzing, optimizing, and improving processes to support decision-making. In recent years, the healthcare sector has significantly increased the adoption of PM tools and has shown growing interest in integrating techniques from other disciplines, such as simulation. However, the literature shows that the combination of PM and PS in healthcare remains limited and is concentrated in a small number of medical specialties. This study aims to analyze and demonstrate the potential of combining PM and PS for healthcare process analysis. The study presents a case study illustrating the integration of PM and PS in the analysis of an outpatient consultation process at a pediatric hospital in Costa Rica. The analysis is based on a dataset of 1814 cases from 2022 and was conducted using the Celonis and Apromore tools, within the context of a specific clinical specialty. The results reveal complex process models, rework activities, bottlenecks, and cases with low levels of conformance, as well as a lack of communication among staff, while highlighting the involvement of domain experts throughout the research. The study designs three simulation scenarios to evaluate process behavior and identifies limitations in the application of the tools used, mainly related to the complexity and variability inherent in healthcare processes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Business Process Modeling and Analysis · Healthcare Operations and Scheduling Optimization · Clinical practice guidelines implementation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School Industrial Engineering, Universidad de Costa Rica, San José 11501, Costa Rica
Department of Engineering, Computer Science and Technology, Universidad de Costa Rica, San José 11501-2060, Costa Rica
School of Engineering, Pontificia Universidad Católica de Chile, Santiago 6904411, Chile
Hospital Nacional de Niños, San José 10103, Costa Rica
INCAE Business School, La Garita, Alajuela 20113, Costa Rica
Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
Department of Clinical Laboratories, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
Funding
Agencia Nacional de Investigación y Desarrollo de Chile (ANID), Fondo Nacional de Desarrollo Científico y Tecnológico
References (37)
Show source-provided references
1. Van der Aalst, W.M.P., and Carmona, J. (2022). Process Mining Handbook, Springer Nature.10.1007/978-3-031-08848-3 ↗
2. Santos, A., Leal, G.C.L., and Balancieri, R. (2025). Process mining in healthcare: A tertiary study. BMC Med. Inform. Decis. Mak., 25.10.1186/s12911-025-02967-z ↗
3. A process-oriented methodology for evaluating the impact of IT: A proposal and an application in healthcare10.1016/j.is.2013.06.005 ↗
4. Towards a characterization of BPM tools’ simulation support: The case of BPMN process models10.24874/IJQR13.04-02 ↗
5. Špaček, M., Půlpán, P., and Kopecký, Z. (2020). Management of business processes using stochastic simulation. Proceedings of the SHS Web of Conferences, EDP Sciences.10.1051/shsconf/20208301066 ↗
6. Business process analysis in healthcare environments: A methodology based on process mining10.1016/j.is.2011.01.003 ↗
7. IT support for healthcare processes–premises, challenges, perspectives10.1016/j.datak.2006.04.007 ↗
8. Silva, M., Rosa, M.J., and Teixeira, L. (2021, January 3–5). Supporting digital transformation using business process management: Proposal of a digital tool. Proceedings of the International Conference on Industrial Engineering and Operations Management, Monterrey, Mexico.
9. Absorptive capacity as a precondition for business process improvement
10. Process mining in healthcare: A literature review10.1016/j.jbi.2016.04.007 ↗
11. Simulation modelling of hospital outpatient department: A review10.1177/00375497221139282 ↗
12. Combining process mining and process simulation in healthcare: A literature review10.1109/ACCESS.2024.3501157 ↗
13. Tamburis, O. (2019, January 21–23). Bridging the gap between process mining and des modeling in the healthcare domain. Proceedings of the E-Health Bioengineering Conference, Iasi, Romania.10.1109/EHB47216.2019.8969912 ↗
14. Supporting capacity management decisions in healthcare using data-driven process simulation10.1016/j.jbi.2022.104060 ↗
15. Van Eck, M.L., Lu, X., Leemans, S.J.J., and Van der Aalst, W.M.P. (2015). PM: A process mining methodology. Advanced Information Systems Engineering, Springer.
16. Abohamad, W., Ramy, A., and Arisha, A. (2017, January 3–6). A hybrid process-mining approach for simulation modeling. Proceedings of the Winter Simulation Conference, Las Vegas, NV, USA.10.1109/WSC.2017.8247894 ↗
17. Le Lay, J., Neveu, J., Dalmas, B., and Augusto, V. (2022, January 18–20). Automated generation of patient population for discrete-event simulation using process mining. Proceedings of the 2022 Annual Modeling and Simulation Conference (ANNSIM), San Diego, CA, USA.10.23919/ANNSIM55834.2022.9859406 ↗
18. Castro, S., and Teixeira, L. (2021, January 7–11). Industry 4.0 and business process management: An exploratory study on the bilateral effects. In Proceedings of International Conference on Industrial Engineering and Operations Management (IEOM), Singapore.10.46254/AN11.20210839 ↗
19. Systematic mapping of process mining studies in healthcare10.1109/ACCESS.2018.2831244 ↗
20. Dumas, M., Rosa, M.L., Mendling, J., and Reijers, H.A. (2018). Fundamentals of Business Process Management, Springer.10.1007/978-3-662-56509-4 ↗
21. A health digital twin framework for discrete event simulation based optimised critical care workflows10.1038/s41746-025-01738-4 ↗
22. Opportunities and challenges for applying process mining in healthcare: A systematic mapping study10.1007/s12652-021-02894-7 ↗
23. Process mining for healthcare: Characteristics and challenges10.1016/j.jbi.2022.103994 ↗
24. Fernandez-Llatas, C. (2020). Interactive Process Mining in Healthcare: An Introduction. Interactive Process Mining in Healthcare, Springer.10.1007/978-3-030-53993-1 ↗
25. Rojas, E., Sepúlveda, M., Munoz-Gama, J., Capurro, D., Traver, V., and Fernandez-Llatas, C. (2017). Question-driven methodology for analyzing emergency room processes using process mining. Appl. Sci., 7.10.3390/app7030302 ↗
26. Augusto, V., Xie, X., Prodel, M., Jouaneton, B., and Lamarsalle, L. (2016, January 11–14). Evaluation of discovered clinical pathways using process mining and joint agent-based discrete-event simulation. Proceedings of the Winter Simulation Conference, Washington, DC, USA.10.1109/WSC.2016.7822256 ↗
27. Human resource allocation in business process management and process mining: A systematic mapping study10.1108/MD-05-2017-0476 ↗
28. Discovering simulation models10.1016/j.is.2008.09.002 ↗
29. Ferronato, J.J., Scalabrin, E.E., and Carvalho, D.R. (2022, January 9–12). PM4SOS: Low-effort resource allocation optimization in a dynamic environment. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.10.1109/SMC53654.2022.9945393 ↗
30. A hybrid model to support decision making in the stroke clinical pathway10.1016/j.simpat.2022.102602 ↗
31. Mesabbah, M., Abo-Hamad, W., and McKeever, S. (2019, January 8–11). A hybrid process mining framework for automated simulation modelling for healthcare. Proceedings of the Winter Simulation Conference, National Harbor, MD, USA.10.1109/WSC40007.2019.9004800 ↗
32. Helm, E., Krauss, O., Lin, A., Pointner, A., Schuler, A., and Küng, J. (2020, January 4–9). Process mining on FHIR-an open standards-based process analytics approach for healthcare. Proceedings of the International Conference on Process Mining (ICPM), Padua, Italy.10.1007/978-3-030-72693-5_26 ↗
33. Yin, R.K. (2009). Case Study Research: Design and Methods, SAGE.
34. Theory building from cases: Opportunities and challenges10.5465/amj.2007.24160888 ↗
35. Tratnjek, T., and Polančič, G. (2026). Methodology for Evaluating Process Mining Tools. Sensors, 26.10.3390/s26031020 ↗
36. Vasconcelos, G.S., Bernardini, F., and Viterbo, J. (2023, January 4–6). A comparison between the most used process mining tools in the market and in academia: Identifying the main features based on a qualitative analysis. Proceedings of the World Conference on Information Systems and Technologies (WCIST), Pisa, Italy.
37. Data-driven queueing modelling: A simulation case study of emergency department crowding10.1136/bmjhci-2025-101462 ↗