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Improvement of the efficiency of hospital care: a simulation-based approach
Mejora de la eficiencia de la atención hospitalaria: un enfoque basado en la simulación
DOI:
https://doi.org/10.15446/dyna.v92n239.120207Palabras clave:
industry 4.0, simulation, lean healthcare (en)industria 4.0, simulación, atención sanitaria ajustada (es)
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This article analyses the implementation of lean manufacturing tools integrated with industry 4.0 technologies, specifically with the discrete event simulation, to improve the operational performance of hospital processes. This study applied a quantitative and experimental methodological approach to the implementation of lean healthcare (LH) to reduce waste. Thus, statistical and scenario modelling tools called ExpertFit and Experimenter are employed using FlexSim software in the healthcare environment. Here, a detailed diagnosis of critical processes is performed, with emphasis on identifying bottlenecks in patient weighing, triage and surgery. The results reveal significant limitations in the responsiveness of the system to sudden increases in demand. The simulation model then proposes a structural intervention involving the addition of four nurses and three doctors. This reveals a potential increase in operational efficiency of up to 36.8% over the projected demand. Finally, this study highlights the relevance of the methodological integration of LH as a strategy for the systematic elimination of waste and the promotion of a culture of continuous improvement in decision-making. This multidimensional approach provides a transferable analytical framework for both academic researchers and healthcare professionals, contributing to the development of more efficient and adaptable hospital management models.
Este artículo analiza la implementación herramientas lean manufacturing integradas con tecnologías de la industria 4.0, específicamente con la simulación de eventos discretos para la mejora del rendimiento operacional de procesos hospitalarios. Para el estudio se aplica un enfoque metodológico cuantitativo y experimental con la implementación lean healthcare (LH) que permite reducir los desperdicios. Así, se emplean herramientas estadísticas y de modelamiento de escenarios denominadas ExpertFit y Experimenter utilizando el software FlexSim en el entorno sanitario. Aquí, se realiza un diagnóstico detallado de los procesos críticos, con énfasis en identificar cuellos de botella en el pesaje de pacientes, triaje y cirugías. Los resultados revelan limitaciones significativas en la capacidad de respuesta del sistema ante incrementos súbitos de demanda. Luego, el modelo de simulación propone una intervención estructural que contempla la incorporación de cuatro profesionales de enfermería y tres facultativos doctores. Esto revela un incremento potencial de la eficiencia operativa de hasta 36.8% con respecto a la demanda proyectada. Finalmente, el estudio destaca la relevancia de la integración metodológica de LH como estrategia para la eliminación sistemática de desperdicios y la promoción de una cultura de mejora continua en la toma de decisiones. Este enfoque multidimensional proporciona un marco analítico transferible tanto para investigadores académicos como para profesionales del sector salud, contribuyendo al desarrollo de modelos de gestión hospitalaria más eficientes y adaptables.
Referencias
[1] Bhamu, J., and Sangwan, K.S., Lean manufacturing: literature review and research issues. International Journal of Operations & Production Management, 34, pp. 876–940, 2014. DOI: https://doi.org/10.1108/IJOPM-08-2012-0315
[2] Rutberg, M.H., Wenczel, S., Devaney, J., Goldlust, E.J., and Day, T.E., Incorporating discrete event simulation into quality improvement efforts in health care systems. American Journal of Medical Quality, 30, pp. 31–35, 2013. DOI: https://doi.org/10.1177/1062860613512863
[3] DeRienzo, C.M., Shaw, R.J., Meanor, P., Lada, E., Ferranti, J., and Tanaka, D., A discrete event simulation tool to support and predict hospital and clinic staffing. Health Informatics Journal, 23, pp. 124–133, 2016. DOI: https://doi.org/10.1177/1460458216628314
[4] Devapriya, P., Strömblad, C.T.B., Bailey, M.D., Frazier, S., Bulger, J., Kemberling, S.T., et al., StratBAM: a discrete-event simulation model to support strategic hospital bed capacity decisions. Journal of Medical Systems, 39, p. 130, 2015. DOI: https://doi.org/10.1007/s10916-015-0325-0
[5] Karakra, A., Fontanili, F., Lamine, E., Lamothe, J., and Taweel, A., Pervasive computing integrated discrete event simulation for a hospital digital twin. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–6, 2018. DOI: https://doi.org/10.1109/AICCSA.2018.8612796
[6] Singh, A.R., Gupta, A., Satpathy, S., and Gowda, N., Study to assess the utility of discrete event simulation software in projection and optimization of resources in the out-patient department at an apex cancer institute in India. Health Science Reports, 5, pp. 1–8, 2022. DOI: https://doi.org/10.1002/hsr2.627
[7] Ahn, C., Rundall, T.G., Shortell, S.M., Blodgett, J.C., and Reponen, E., Lean management and breakthrough performance improvement in health care. Quality Management in Healthcare, 30, pp. 6–12, 2021. DOI: https://doi.org/10.1097/QMH.0000000000000282
[8] Quazi, F., Raju, N., and Viradia, V., Digital twin technology in healthcare: applications, challenges, and future insights. International Journal of Global Innovations and Solutions, pp. 1–13, 2024. DOI: https://doi.org/10.21428/e90189c8.bdb0264f
[9] Ivanov, D., Dolgui, A., and Sokolov, B., The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57, pp. 829–846, 2019. DOI: https://doi.org/10.1080/00207543.2018.1488086
[10] Sharma, V., Jamwal, A., Agrawal, R., and Pratap, S., A review on digital transformation in healthcare waste management: applications, research trends and implications. Waste Management & Research, 2024, art. 5420. DOI: https://doi.org/10.1177/0734242X241285420
[11] Becerra, P., Mula, J., and Sanchis, R., Green supply chain quantitative models for sustainable inventory management: a review. Journal of Cleaner Production, 328, art. 129544, 2021. DOI: https://doi.org/10.1016/j.jclepro.2021.129544
[12] García, S., and Montenegro, D., Implementation and evaluation of lean healthcare tools through the FlexSim simulator. Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology, 2021, art. 594. DOI: https://doi.org/10.18687/LACCEI2021.1.1.594
[13] Illusanguil-Corro, K.D., Villamarin-Jipa, V.V., Llerena-Rosado, E.D., and Saavedra-Freire, J.V., Impact of management models based on lean healthcare on the efficiency and quality of hospital services: a literature review. Reincisol, 3, pp. 5728–5739, 2024. DOI: https://doi.org/10.59282/reincisol.V3(6)5728-5739
[14] Reyes, J., Mula, J., and Diaz-Madroñero, M., Quantitative insights into the integrated push and pull production problem for lean supply chain planning 4.0. International Journal of Production Research, 62, pp. 6251–6275, 2024. DOI: https://doi.org/10.1080/00207543.2024.2312205
[15] Rosas-Hernández, L.C., Limon-Romero, J., Baez-López, Y., Perez-Sánchez, A., and Tlapa-Mendoza, D., Lean healthcare and DMAIC to improve the supply process in a public hospital. Dyna Management, 9, pp. 1–16, 2021. DOI: https://doi.org/10.6036/mn9986
[16] Cohen, R., Lean methodology in health care. Chest, 154, pp. 1448–1454, 2018. DOI: https://doi.org/10.1016/j.chest.2018.06.005
[17] Ahsan, M.M., and Siddique, Z., Industry 4.0 in healthcare: a systematic review. International Journal of Information Management Data Insights, 2, pp. 1–20, 2022. DOI: https://doi.org/10.1016/j.jjimei.2022.100079
[18] Roy, S., Wuest, T., Ahmed, I., and Saldanha, J., Bottleneck management through strategic sequencing in smart manufacturing systems. Graduate Theses, West Virginia University, 2023.
[19] Mula, J., Peidro, D., Díaz-Madroñero, M., and Vicens, E., Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204, pp. 377–390, 2010. DOI: https://doi.org/10.1016/j.ejor.2009.09.008
[20] Ghobakhloo, M., Fathi, M., Iranmanesh, M., Maroufkhani, P., and Morales, M.E., Industry 4.0 ten years on: a bibliometric and systematic review of concepts, sustainability value drivers, and success determinants. Journal of Cleaner Production, 302, art. 127052, 2021. DOI: https://doi.org/10.1016/j.jclepro.2021.127052
[21] Anu, M., Introduction to modeling and simulation. In Healy, K., Withers, D., and Nelson, B. (eds.), Winter Simulation Conference, pp. 7–13, 1997. DOI: https://doi.org/10.1145/268437.268440
[22] Carson, J.S., Introduction to modeling and simulation. In Proceedings of the Winter Simulation Conference, art. 4235, 2005. DOI: https://doi.org/10.1109/WSC.2005.1574235
[23] López-Sánchez, M., Ramos-López, L., Pato-López, O., and López-Álvarez, S., Simulation-based training in medicine: a teaching tool. CIR MAY AMB, 18, pp. 25–29, 2012.
[24] Zhu, X., Zhang, R., Chu, F., He, Z., and Li, J., A FlexSim-based optimization for the operation process of cold-chain logistics distribution centre. Journal of Applied Research and Technology, 12, pp. 270–278, 2014. DOI: https://doi.org/10.1016/S1665-6423(14)72343-0
[25] Tlapa, D., Zepeda-Lugo, C.A., Tortorella, G.L., Baez-Lopez, Y.A., Limon-Romero, J., Alvarado-Iniesta, A., et al., Effects of lean healthcare on patient flow: a systematic review. Value in Health, 23, pp. 260–273, 2020. DOI: https://doi.org/10.1016/j.jval.2019.11.002
[26] Reyes, J., Mula, J., and Díaz-Madroñero, M., Development of a conceptual model for lean supply chain planning in industry 4.0: multidimensional analysis for operations management. Production Planning & Control, 34, pp. 1209–1224, 2023. DOI: https://doi.org/10.1080/09537287.2021.1993373
[27] Fernandes, H.M.L.G., de Jesus, M.V.N., da Silva, D., and Guirardello, E.B., Lean healthcare in the institutional, professional, and patient perspective: an integrative review. Revista Gaúcha de Enfermagem, 41, pp. 1–12, 2020. DOI: https://doi.org/10.1590/1983-1447.2020.20190340
[28] Poksinska, B.B., Fialkowska-Filipek, M., and Engström, J., Does lean healthcare improve patient satisfaction? a mixed-method investigation into primary care. BMJ Quality & Safety, 26, pp. 95–103, 2017. DOI: https://doi.org/10.1136/bmjqs-2015-004290
[29] Oz, B., Shneer, S., and Ziedins, I., Static vs accumulating priorities in healthcare queues under heavy loads. arXiv, 2, pp. 1–11, 2020. DOI: https://doi.org/10.48550/arXiv.2003.14087
[30] Régis, T.K.O., Gohr, C.F., and Santos, L.C., Lean healthcare implementation: experiences and lessons learned from Brazilian hospitals. Revista de Administração de Empresas, 58, pp. 30–43, 2018. DOI: https://doi.org/10.1590/S0034-759020180103
[31] Bahari, A., and Asadi, F., A simulation optimization approach for resource allocation in an emergency department healthcare unit. Global Heart, 15, pp. 1–6, 2020. DOI: https://doi.org/10.5334/GH.528
[32] Daldoul, D., Nouaouri, I., Bouchriha, H., and Allaoui, H., Simulation-based optimisation approach to improve emergency department resource planning: a case study of a Tunisian hospital. International Journal of Health Planning and Management, 37, pp. 2727–2751, 2022. DOI: https://doi.org/10.1002/hpm.3499
[33] Melman, G.J., Parlikad, A.K., and Cameron, E., Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Management Science, 24, pp. 356–374, 2021. DOI: https://doi.org/10.1007/s10729-021-09548-2
[34] Zhang, H., Best, T.J., Chivu, A., and Meltzer, D.O., Simulation-based optimization to improve hospital patient assignment to physicians and clinical units. Health Care Management Science, 23, pp. 117–141, 2020. DOI: https://doi.org/10.1007/s10729-019-09483-3
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