Analysis of patient flow using discrete simulation in a chemotherapy unit of a non-profit organization
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Feb 2, 2021
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Feb 2, 2021
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[1]
H. T. Martinez Vera and E. L. Duarte Forero, “Analysis of patient flow using discrete simulation in a chemotherapy unit of a non-profit organization”, I, vol. 15, no. 29, pp. 23–36, Feb. 2021.
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Main Article Content
Abstract
Discrete simulation is a tool that allows having an overview of the system and easy traceability of the variables of interest. In this article, a representation of a chemotherapy unit is made in order to improve the use of available resources in a discrete simulation model that represents the system determined by its key variables such as: arrival of patients, length of stay, required personnel and associated operations. A patient segmentation methodology is proposed based on infusion times according to protocols. Subsequently, various scenarios are proposed within two experiments, preserving demand as a fundamental parameter, and the variable resource is modified in order to attend to as many patients as possible. Finally, the study allowed finding an adequate combination of resources (combination of oncology nurses and nursing assistants) to obtain the desired reduction in the length of stay.
References
[1] J. Vargas Caleño y M. M. Herrera R, “Comparación de técnicas de modelamiento para el control de procesos: un enfoque de aprendizaje con dinámica de sistemas”, Inventum, vol. 10, n.° 18, pp. 37–48, feb. 2015, doi: 10.26620/uniminuto.inventum.10.18.2015.37-48.
[2] S. M. Thompson, R. Day, and R. Garfinkel, “Improving the flow of patients through healthcare organizations”, en Handbook of healthcare operations management, New York: Springer, 2013, pp. 183–204. doi: https://doi.org/10.1007/978-1-4614-5885-2
[3] M. Chong et al., “Patient flow evaluation with system dynamic model in an emergency department: Data analytics on daily hospital records”, en 2015 IEEE international congress on big data, New York, 2015, pp. 320–323.
doi: https://doi.org/10.1109/BigDataCongress.2015.54.
[4] S. Barnes, B. Golden, and S. Price, “Applications of agent-based modeling and simulation to healthcare operations
management”, en Handbook of healthcare operations management, New York: Springer, 2013, pp. 45–74, doi:https://doi.org/10.1007/978-1-4614-5885-2_3.
[5] T. Bountourelis, M. Y. Ulukus, J. P. Kharoufeh, and S. G. Nabors, “The modeling, analysis, and management of intensive care units”, en Handbook of healthcare operations management, New York: Springer, 2013, pp.
153–182. doi: https://doi.org/10.1007/978-1-4614-5885-2_6.
[6] C. D. Pegden and D. T. Sturrock, Rapid modeling solutions: introduction to simulation and Simio, Sewickley, Pennsylvania Simio Forward Thinking, 2014.
[7] W. D. Kelton, J. S. Smith, D. T. Sturrock, and A. Verbraeck, Simio & simulation: Modeling, analysis, applications, Sewickley, Pennsylvania Learning Solutions Chennai.
[8] K. Bertsch, Day-of-Discharge Planning at Acute Care Hospitals, thesis, Master of Science in Engineering, Wright State
University, Dayton (Ohio) 2014. [Online]. Available: https://corescholar.libraries.wright.edu/etd_all/1235.
[9] Case, and K. Ellis, “Simulation-Based Improvement of the Discharge System in Highly Utilized Hospitals”, en Winter
Simulation Conference, Maryland, 2019, Consultado: jul. 17, 2020. [Online]. Available: https://www.simio.com/resources/papers/WinterSim2019/Simulation-Based-Improvement-of-the-Discharge-System-in-Highly-Utilized-Hospitals.php.
[10] M. Khasawneh, “Reducing Patient Waiting Time in an Outpatient Clinic: A Discrete Event Simulation (DES) Based Approach”, Pittsburgh, Pennsylvania 2017, [Online]. Available: https://www.researchgate.net/profile/Niamat_Ullah_Ibne_Hossain/publication/317730692_Reducing_Patient_Wai
ting_Time_in_an_Outpatient_Clinic_A_Discrete_Event_Simulation_DES_Based_Approach/links/5975a806a6fdcc83488e
9320/Reducing-Patient<-Waiting-Time-inan-Outpatient-Clinic-A-Discrete-Event-Simulation-DES-Based-Approach.pdf
[11] S. Suss, N. Bhuiyan, K. Demirli, and G. Batist, “Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency”, JOP, vol. 13, n.° 6, pp. e530-e537, may 2017, doi:10.1200/JOP.2016.020008.
[12] C. Forero-Almanza, M. A. Martínez, y M. Mauricio Herrera, “Modelo de simulación relacionado con la
https://www.simio.com/resources/papers/WinterSim2016/Eradicating-the-Average-Answering-Complex-Healthcare-Questions-Using-Discrete-Event-Simulation.php.
[14] C. M. DeRienzo, R. J. Shaw, P. Meanor, E. Lada, J. Ferranti, and D. Tanaka, “A discrete event simulation tool to support and predict hospital and clinic staffing”, Health Informatics J, vol. 23, n.° 2, pp. 124-133, jun. 2017, doi: 10.1177/1460458216628314.
[15] Bernatchou, Maryam, Adil Bellabdaoui, and Fatima Ouzayd. 2017. “Performance Evaluation of a Chemotherapy Treatment Unit through Simulation.” In Proceedings of the International Conference on Industrial Engineering and Operations Management, Rabat, Moroco, 2017, pp. 5720–5726. [Online] Available: http://ieomsociety.org/ieom2017/papers/582.pdf
[16] T. M. Abuhay, A. V. Krikunov, E. V. Bolgova, L. G. Ratova, and S. V. Kovalchuk, “Simulation of Patient Flow and Load of Departments in a Specialized Medical Center”, Procedia Computer Science, vol. 101, pp. 143-151,
ene. 2016, doi: 10.1016/j.procs.2016.11.018
[17] L. M. Pantoja Rojas, y L. A. Garavito Herrera, “Análisis del proceso de urgencias y hospitalización del CAMI Diana Turbay a través de un modelo de simulación con Arena 10.0 para la distribución óptima del recurso
humano”, Ingeniería e Investigación, vol. 28, n.° 1, pp. 146-153, abr. 2008. [En línea]. Disponible en: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-5 6 0 9 2 0 0 8 0 0 0 1 0 0 0 1 6 & l n g = e n &nrm=iso
[18] M. Alvarado, T. G Cotton, N. Lewis, E. Perez, y W. Carpentier, “Modeling and simulation of oncology clinic operations in discrete event system specification”, vol. 94, n°. 2, pp. 105-121, may 31, 2017. [En línea]. Disponible
en: https://journals.sagepub.com/doi/full/10.1177/0037549717708246, doi: https://doi.org/10.1177/0037549717708246
[19] G. Lamé, O. Jouini, and J. S.-L. Cardinal, “Combining Soft Systems Methodology, ethnographic observation, and discreteevent simulation: A case study in cancer care”, Journal of the Operational Research Society, pp. 1-18, jun. 2019, doi: 10.1080/01605682.2019.1610339.
[2] S. M. Thompson, R. Day, and R. Garfinkel, “Improving the flow of patients through healthcare organizations”, en Handbook of healthcare operations management, New York: Springer, 2013, pp. 183–204. doi: https://doi.org/10.1007/978-1-4614-5885-2
[3] M. Chong et al., “Patient flow evaluation with system dynamic model in an emergency department: Data analytics on daily hospital records”, en 2015 IEEE international congress on big data, New York, 2015, pp. 320–323.
doi: https://doi.org/10.1109/BigDataCongress.2015.54.
[4] S. Barnes, B. Golden, and S. Price, “Applications of agent-based modeling and simulation to healthcare operations
management”, en Handbook of healthcare operations management, New York: Springer, 2013, pp. 45–74, doi:https://doi.org/10.1007/978-1-4614-5885-2_3.
[5] T. Bountourelis, M. Y. Ulukus, J. P. Kharoufeh, and S. G. Nabors, “The modeling, analysis, and management of intensive care units”, en Handbook of healthcare operations management, New York: Springer, 2013, pp.
153–182. doi: https://doi.org/10.1007/978-1-4614-5885-2_6.
[6] C. D. Pegden and D. T. Sturrock, Rapid modeling solutions: introduction to simulation and Simio, Sewickley, Pennsylvania Simio Forward Thinking, 2014.
[7] W. D. Kelton, J. S. Smith, D. T. Sturrock, and A. Verbraeck, Simio & simulation: Modeling, analysis, applications, Sewickley, Pennsylvania Learning Solutions Chennai.
[8] K. Bertsch, Day-of-Discharge Planning at Acute Care Hospitals, thesis, Master of Science in Engineering, Wright State
University, Dayton (Ohio) 2014. [Online]. Available: https://corescholar.libraries.wright.edu/etd_all/1235.
[9] Case, and K. Ellis, “Simulation-Based Improvement of the Discharge System in Highly Utilized Hospitals”, en Winter
Simulation Conference, Maryland, 2019, Consultado: jul. 17, 2020. [Online]. Available: https://www.simio.com/resources/papers/WinterSim2019/Simulation-Based-Improvement-of-the-Discharge-System-in-Highly-Utilized-Hospitals.php.
[10] M. Khasawneh, “Reducing Patient Waiting Time in an Outpatient Clinic: A Discrete Event Simulation (DES) Based Approach”, Pittsburgh, Pennsylvania 2017, [Online]. Available: https://www.researchgate.net/profile/Niamat_Ullah_Ibne_Hossain/publication/317730692_Reducing_Patient_Wai
ting_Time_in_an_Outpatient_Clinic_A_Discrete_Event_Simulation_DES_Based_Approach/links/5975a806a6fdcc83488e
9320/Reducing-Patient<-Waiting-Time-inan-Outpatient-Clinic-A-Discrete-Event-Simulation-DES-Based-Approach.pdf
[11] S. Suss, N. Bhuiyan, K. Demirli, and G. Batist, “Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency”, JOP, vol. 13, n.° 6, pp. e530-e537, may 2017, doi:10.1200/JOP.2016.020008.
[12] C. Forero-Almanza, M. A. Martínez, y M. Mauricio Herrera, “Modelo de simulación relacionado con la
https://www.simio.com/resources/papers/WinterSim2016/Eradicating-the-Average-Answering-Complex-Healthcare-Questions-Using-Discrete-Event-Simulation.php.
[14] C. M. DeRienzo, R. J. Shaw, P. Meanor, E. Lada, J. Ferranti, and D. Tanaka, “A discrete event simulation tool to support and predict hospital and clinic staffing”, Health Informatics J, vol. 23, n.° 2, pp. 124-133, jun. 2017, doi: 10.1177/1460458216628314.
[15] Bernatchou, Maryam, Adil Bellabdaoui, and Fatima Ouzayd. 2017. “Performance Evaluation of a Chemotherapy Treatment Unit through Simulation.” In Proceedings of the International Conference on Industrial Engineering and Operations Management, Rabat, Moroco, 2017, pp. 5720–5726. [Online] Available: http://ieomsociety.org/ieom2017/papers/582.pdf
[16] T. M. Abuhay, A. V. Krikunov, E. V. Bolgova, L. G. Ratova, and S. V. Kovalchuk, “Simulation of Patient Flow and Load of Departments in a Specialized Medical Center”, Procedia Computer Science, vol. 101, pp. 143-151,
ene. 2016, doi: 10.1016/j.procs.2016.11.018
[17] L. M. Pantoja Rojas, y L. A. Garavito Herrera, “Análisis del proceso de urgencias y hospitalización del CAMI Diana Turbay a través de un modelo de simulación con Arena 10.0 para la distribución óptima del recurso
humano”, Ingeniería e Investigación, vol. 28, n.° 1, pp. 146-153, abr. 2008. [En línea]. Disponible en: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-5 6 0 9 2 0 0 8 0 0 0 1 0 0 0 1 6 & l n g = e n &nrm=iso
[18] M. Alvarado, T. G Cotton, N. Lewis, E. Perez, y W. Carpentier, “Modeling and simulation of oncology clinic operations in discrete event system specification”, vol. 94, n°. 2, pp. 105-121, may 31, 2017. [En línea]. Disponible
en: https://journals.sagepub.com/doi/full/10.1177/0037549717708246, doi: https://doi.org/10.1177/0037549717708246
[19] G. Lamé, O. Jouini, and J. S.-L. Cardinal, “Combining Soft Systems Methodology, ethnographic observation, and discreteevent simulation: A case study in cancer care”, Journal of the Operational Research Society, pp. 1-18, jun. 2019, doi: 10.1080/01605682.2019.1610339.