1,844
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Machine learning integrated patient flow simulation: why and how?

ORCID Icon, , &
Pages 580-593 | Received 05 Oct 2021, Accepted 14 May 2023, Published online: 29 May 2023

References

  • Abuhay, T. M., Krikunov, A. V., Bolgova, E. V., Ratova, L. G., & Kovalchuk, S. V. (2016). Simulation of patient flow and load of departments in a specialized medical center. Procedia computer science, 101, 143–151. https://doi.org/10.1016/j.procs.2016.11.018
  • Abuhay, T. M., Mamuye, A. L., Robinson, S. L., & Kovalchuk, S. V. (2021). Why machine learning integrated patient flow simulation? The Operational Research Society Simulation Workshop 2021 (SW21), 22–26 March 2021, Online, (pp. 375–384). https://www.theorsociety.com/media/5897/sw21-proceedings.pdf
  • Abuhay, T. M., Metsker, O. G., Yakovlev, A. N., & Kovalchuk, S. V. (2020a). Constructing holistic patient flow simulation using system approach. In Computational Science – ICCS 2020 (Vol. 12140, pp. 418–429). Nature Publishing Group. https://doi.org/10.1007/978-3-030-50423-6_31
  • Abuhay, T. M., Nigatie, Y. G., Metsker, O. G., Yakovlev, A. N., & Kovalchuk, S. V. (2020b). Investigating coordination of hospital departments in delivering healthcare for acute coronary syndrome patients using data-driven network analysis. In Computational Science – ICCS 2020 (Vol. 12140, pp. 430–440). Nature Publishing Group. https://doi.org/10.1007/978-3-030-50423-6_32
  • Al-Jabery, K. K., Obafemi-Ajayi, T., Olbricht, G. R., & Wunsch, D. C., II. (2020). Clustering algorithms. Computational Learning Approaches to Data Analytics in Biomedical Applications, 29–100. https://doi.org/10.1016/B978-0-12-814482-4.00003-6
  • Allen, M. (2019). Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway. British Medical Journal Open, 9(9), e028296. https://doi.org/10.1136/bmjopen-2018-028296
  • Al Taleb, A. R., Hoque, M., Hasanat, A., & Khan, M. B. (2017). Application of data mining techniques to predict length of stay of stroke patients. In 2017 International Conference on Informatics, Health and Technology, ICIHT 2017, 21–23 Feb 2017, Riyadh, Saudi Arabia. (pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://ieeexplore.ieee.org/document/7899004
  • Ambinder, E. P. (2005). ElecTronic health records. Journal of Oncology Practice, 1(2), 57–63. https://doi.org/10.1200/jop.2005.1.2.57
  • Angelini, C. (2019). Regression Analysis. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 13, 722–730. https://doi.org/10.1016/B978-0-12-809633-8.20360-9
  • Antonelli, D., Bruno, G., & Taurino, T. (2014). Simulation-based analysis of patient flow in elective surgery. In Proceedings of the International Conference on Health Care Systems Engineering, May 22 and 24, 2013, Milan, Italy. (Vol. 61, pp. 87–97). Springer Cham. https://doi.org/10.1007/978-3-319-01848-5
  • Ardabili, S. F. (2020). COVID-19 outbreak prediction with machine learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
  • Asheim, A., Bache-Wiig Bjørnsen, L. P., Næss-Pleym, L. E., Uleberg, O., Dale, J., & Nilsen, S. M. (2019). Real-time forecasting of emergency department arrivals using prehospital data. BMC Emergency Medicine, 19(1), 42. https://doi.org/10.1186/s12873-019-0256-z
  • Aspland, E., Gartner, D., & Harper, P. (2019). Clinical pathway modelling: A literature review. Health Systems, 10(1), 1–23. https://doi.org/10.1080/20476965.2019.1652547
  • Azari-Rad, S., Yontef, A., Aleman, D. M., & Urbach, D. R. (2014). A simulation model for perioperative process improvement. Operations Research for Health Care, 3(1), 22–30. https://doi.org/10.1016/j.orhc.2013.12.003
  • Banks, J. (2005). Prentice-Hall International Series in Industrial and Systems Engineering. Discrete-event system simulation (Fourth). Retrieved from 03 March 2020 http://syndetics.com/index.php?isbn=0131293427/lc.jpg&client=brlibt&type=xw12
  • Bosbach, W. A., Heinrich, M., Kolisch, R., & Heiss, C. (2021). Maximization of open hospital capacity under shortage of sars-cov-2 vaccines—an open access, stochastic simulation tool. Vaccines, 9(6), 546. https://doi.org/10.3390/VACCINES9060546
  • Bovim, T. R., Gullhav, A. N., Andersson, H., Dale, J., & Karlsen, K. (2021). Simulating emergency patient flow during the COVID-19 pandemic. Journal of Simulation, 1–15. https://doi.org/10.1080/17477778.2021.2015259
  • Bramkamp, M., Radovanovic, D., Erne, P., & Szucs, T. D. (2007). Determinants of costs and the length of stay in acute coronary syndromes: A real life analysis of more than 10 000 patients. Cardiovascular Drugs and Therapy, 21(5), 389–398. https://doi.org/10.1007/s10557-007-6044-0
  • Bremer, V., Becker, D., Kolovos, S., Funk, B., Van Breda, W., Hoogendoorn, M., & Riper, H. (2018). Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis. Journal of Medical Internet Research, 20(8), e10275. https://doi.org/10.2196/10275
  • Brockwell, P. J. (2010). Time series analysis. In International Encyclopedia of Education (pp. 474–481). Elsevier Ltd. https://doi.org/10.1016/B978-0-08-044894-7.01372-5
  • Brown, G. (2005). Value-based medicine: The new paradigm. Current Opinion in Ophthalmology, 16(3), 139–140. https://doi.org/10.097/01.icu.0000164165.17432.ae
  • Chen, Y.-C. (2017). A Tutorial on Kernel Density Estimation and Recent Advances. http://arxiv.org/abs/1704.03924
  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13-17-August-2016, (pp. 785–794). https://doi.org/10.1145/2939672.2939785
  • Christensen, B. A. (2012). Improving ICU patient flow through discrete-event simulation. Massachusetts Institute of Technology. Retrieved from 21 December 2020 https://dspace.mit.edu/handle/1721.1/73436#files-area
  • Cocke, S. (2016). UVA emergency department patient flow simulation and analysis. 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, (pp. 118–123). https://doi.org/10.1109/SIEDS.2016.7489282
  • Côté, M. J. (2000). Understanding patient flow. Decision Line. Retrieved from 27 October 2020 http://web.uvic.ca/~h351/hinf351_course_data/Cote
  • Daghistani, T. A., Elshawi, R., Sakr, S., Ahmed, A. M., Al-Thwayee, A., & Al-Mallah, M. H. (2019). Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. International Journal of Cardiology, 288, 140–147. https://doi.org/10.1016/j.ijcard.2019.01.046
  • de Brito, C. C. R., Rêgo, L. C., & de Oliveira, W. R. (2015). Method for generating distributions and classes of probability distributions: the univariate case. Hacettepe Journal of Mathematics and Statistics, 48(3), 897–930. https://doi.org/10.15672/hjms.2018.619
  • El-Bouri, R., Taylor, T., Youssef, A., Zhu, T., & Clifton, D. A. (2021). Machine learning in patient flow: A review. Progress in Biomedical Engineering, 3(2), 022002. https://doi.org/10.1088/2516-1091/ABDDC5
  • Frank, R. J., Davey, N., & Hunt, S. P. (2001). TiMe series prediction and neural networks. Journal of Intelligent & Robotic Systems, 31(1/3), 91–103. https://doi.org/10.1023/A:1012074215150
  • Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2017). Data-driven modeling of clinical pathways using electronic health records. In Procedia computer science (Vol. 121, pp. 835–842). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.11.108
  • Garcia-Vicuña, D., Esparza, L., & Mallor, F. (2022). Hospital preparedness during epidemics using simulation: The case of COVID-19. Central European Journal of Operations Research, 30(1), 213–249. https://doi.org/10.1007/s10100-021-00779-w
  • German Anatoli, D. R. (2013). Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation. In Proceedings of the 2013 Winter Simulation Conference, December 8–11, Washington D.C., USA. (pp. 270–281). https://ieeexplore.ieee.org/document/6721426
  • Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2018). Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. The American Journal of Emergency Medicine, 36(9), 1650–1654. https://doi.org/10.1016/J.AJEM.2018.06.062
  • Gualandi, R., Masella, C., & Tartaglini, D. (2019). Improving hospital patient flow: A systematic review. In Business process management journal. Emerald Group Publishing Ltd. https://doi.org/10.1108/BPMJ-10-2017-0265
  • Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17–25. https://doi.org/10.1057/hs.2012.8
  • Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018). Predicting hospital admission at emergency department triage using machine learning. PLos One, 13(7), 1–13. https://doi.org/10.1371/journal.pone.0201016
  • Hoot, N. R., LeBlanc, L. J., Jones, I., Levin, S. R., Zhou, C., Gadd, C. S., & Aronsky, D. (2009). Forecasting emergency department crowding: A prospective, real-time evaluation. Journal of the American Medical Informatics Association: JAMIA, 16(3), 338–345. https://doi.org/10.1197/JAMIA.M2772
  • Houthooft, R. (2015). Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artificial Intelligence in Medicine, 63(3), 191–207. https://doi.org/10.1016/J.ARTMED.2014.12.009
  • Huang, Z., Lu, X., & Duan, H. (2012). On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in Medicine, 56(1), 35–50. https://doi.org/10.1016/J.ARTMED.2012.06.002
  • Hurwitz, J. E., Lee, J. A., Lopiano, K. K., McKinley, S. A., Keesling, J., & Tyndall, J. A. (2014). A flexible simulation platform to quantify and manage emergency department crowding. BMC Medical Informatics & Decision Making, 14(1), 50. https://doi.org/10.1186/1472-6947-14-50
  • Ickowicz, A., Sparks, R., & Wiley, J. (2016). Modelling hospital length of stay using convolutive mixtures distributions. Statistics in Medicine, 36(1), 122–135. https://doi.org/10.1002/sim.7135
  • Jasti, N. V. K., & Kodali, R. (2014). A literature review of empirical research methodology in lean manufacturing. International Journal of Operations & Production Management, 34(8), 1080–1122. https://doi.org/10.1108/IJOPM-04-2012-0169
  • Jödicke, A. M. (2019). Prediction of health care expenditure increase: How does pharmacotherapy contribute? BMC Health Services Research, 19(1), 953. https://doi.org/10.1186/s12913-019-4616-x
  • Karnon, J., Stahl, J., Brennan, A., Caro, J. J., Mar, J., & Möller, J. (2012). MoDeling using discrete event simulation: A Report of the ISPOR-SMDM modeling good research practices task force-4. Value in Health, 15(6), 821–827. https://doi.org/10.1016/J.JVAL.2012.04.013
  • Khaldi, R., Afia, A. E., & Chiheb, R. (2019). Forecasting of weekly patient visits to emergency department: Real case study. In ProCedia computer science (Vol. 148, pp. 532–541). Elsevier B.V. https://doi.org/10.1016/j.procs.2019.01.026
  • Konrad, R., DeSotto, K., Grocela, A., McAuley, P., Wang, J., Lyons, J., & Bruin, M. (2013). Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study. Operations Research for Health Care, 2(4), 66–74. https://doi.org/10.1016/j.orhc.2013.04.001
  • Kovalchuk, S. V., Funkner, A. A., Metsker, O. G., & Yakovlev, A. N. (2018). Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification. Journal of Biomedical Informatics, 82, 128–142. https://doi.org/10.1016/j.jbi.2018.05.004
  • Kreindler, S. A. (2017). The three paradoxes of patient flow: An explanatory case study. BMC Health Services Research, 17(1), 481. https://doi.org/10.1186/s12913-017-2416-8
  • Lee, A. H., Ng, A. S. K., & Yau, K. K. W. (2001). Determinants of maternity length of stay: A Gamma mixture risk-adjusted model. Health Care Management Science, 4(4), 249–255. https://doi.org/10.1023/A:1011810326113
  • Liashchynskyi, P., & Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: A big comparison for NAS. arXiv preprint. https://doi.org/10.48550/arXiv.1912.06059
  • Luo, L., Luo, L., Zhang, X., & He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research, 17(1), 469. https://doi.org/10.1186/s12913-017-2407-9
  • Mandrola, J. (2015, August 19). Doctor doesn’t always know best Discloures. Retrieved January 16, 2022, from https://www.medscape.com/viewarticle/849689
  • Maria, A., & Anu. (1997). Introduction to modeling and simulation. In Proceedings of the 29th conference on Winter simulation - WSC ’97 (pp. 7–13). December 7–10, 1997, New York, New York, USA: ACM Press. https://doi.org/10.1145/268437.268440
  • Mekhaldi, R. N., Caulier, P., Chaabane, S., Chraibi, A., & Piechowiak, S. (2020). UsiNg machine learning models to predict the length of stay in a hospital setting. In Advances in Intelligent Systems and Computing (Vol. 1159, pp. 202–211). AISC. https://doi.org/10.1007/978-3-030-45688-7_21
  • Mishra, S. B., & Alok, S. (2017). Handbook of research methodology. https://www.researchgate.net/publication/319207471
  • Mohammed, R., Rawashdeh, J., & Abdullah, M. (2020). Machine learning with oversampling and undersampling techniques: Overview study and experimental results. 2020 11th International Conference on Information and Communication Systems, ICICS 2020, 07–09 April, Irbid, Jordan, 243–248. https://doi.org/10.1109/ICICS49469.2020.239556
  • Mohiuddin, S. (2017). Patient flow within UK emergency departments: A systematic review of the use of computer simulation modelling methods. British Medical Journal Open, 7(5), e015007. https://doi.org/10.1136/BMJOPEN-2016-015007
  • Monks, T., Worthington, D., Allen, M., Pitt, M., Stein, K., & James, M. (2016). A modelling tool for capacity planning in acute and community stroke services. BMC Health Services Research, 16(1), 1–8. https://doi.org/10.1186/s12913-016-1789-4
  • Morquin, D., & Ologeanu-Taddei, R. (2016). Professional facing coercive work formalization: Vicious circle of the Electronic Medical Record (EMR) implementation and appropriation. Retrieved from 23 September 2020 https://hal.archives-ouvertes.fr/hal-02138238
  • Muhlestein, W. E., Akagi, D. S., Davies, J. M., & Chambless, L. B. (2019). Predicting inpatient length of stay after brain tumor surgery: DeveloPing machine learning ensembles to improve predictive performance. Clinical Neurosurgery, 85(3), 384–393. https://doi.org/10.1093/neuros/nyy343
  • Nas, S., & Koyuncu, M. (2019). Emergency department capacity planning: A recurrent neural network and simulation approach. Computational & Mathematical Methods in Medicine, 2019, 1–13. https://doi.org/10.1155/2019/4359719
  • Nau, R. (2020). Data transformations and forecasting models: What to use and when, Duke University. Retrieved January 16, 2022, from https://people.duke.edu/~rnau/whatuse.htm
  • Ngiam, K. Y., & Khor, I. W. (2019). Big data and machine learning algorithms for health-care delivery. In The lancet oncology. Lancet Publishing Group. May, 1. https://doi.org/10.1016/S1470-2045(19)30149-4.
  • Noohi, S., Kalantari, S., Hasanvandi, S., & Elikaei, M. (2020). Determinants of length of stay in a psychiatric ward: A retrospective chart review. The Psychiatric Quarterly, 91(2), 273–287. https://doi.org/10.1007/s11126-019-09699-0
  • Papi, M., Pontecorvi, L., & Setola, R. (2016). A new model for the length of stay of hospital patients. Health Care Management Science, 19(1), 58–65. https://doi.org/10.1007/s10729-014-9288-9
  • Pillay, N., & Nyathi, T. (2021). Automated Design of Classification Algorithms. Natural Computing Series, 171–184. https://doi.org/10.1007/978-3-030-72069-8_10
  • Prokofyeva, E., & Zaytsev, R. (2020). Clinical pathways analysis of patients in medical institutions based on hard and fuzzy clustering methods. Business Informatics, 14(1), 19–31. https://doi.org/10.17323/2587-814x.2020.1.19.31
  • Ram, S. (2020). GitHub - AutoViML/featurewiz: Use advanced feature engineering strategies and select the best features from your data set fast with a single line of code. https://github.com/AutoViML/featurewiz
  • Robinson, S. (2008a). Conceptual modelling for simulation part I: Definition and requirements. The Journal of the Operational Research Society, 59(3), 278–290. https://doi.org/10.1057/PALGRAVE.JORS.2602368
  • Robinson, S. (2008b). Conceptual modelling for simulation part II: A framework for conceptual modelling. The Journal of the Operational Research Society, 59(3), 291–304. https://doi.org/10.1057/PALGRAVE.JORS.2602369
  • Robinson, S. (2013). Conceptual modeling for simulation. Winter Simulation Conference, 08–11 December, Washington, DC, USA, (pp. 377–388). https://doi.org/10.1109/WSC.2013.6721435
  • Santibáñez, P., Chow, V. S., French, J., Puterman, M. L., & Tyldesley, S. (2009). Reducing patient wait times and improving resource utilization at British Columbia cancer agency’s ambulatory care unit through simulation. Health Care Management Science, 12(4), 392–407. https://doi.org/10.1007/s10729-009-9103-1
  • Schleidgen, S., Klingler, C., Bertram, T., Rogowski, W. H., & Marckmann, G. (2013). What is personalized medicine: Sharpening a vague term based on a systematic literature review. BMC Medical Ethics, 14(1), 55. https://doi.org/10.1186/1472-6939-14-55
  • Schoenfelder, J., Kohl, S., Glaser, M., McRae, S., Brunner, J. O., & Koperna, T. (2021). Simulation-based evaluation of operating room management policies. BMC Health Services Research, 21(1), 1–13. https://doi.org/10.1186/s12913-021-06234-5
  • Siddiqui, N., Dwyer, M., Stankovich, J., Peterson, G., Greenfield, D., Si, L., & Kinsman, L. (2018). Hospital length of stay variation and comorbidity of mental illness: A retrospective study of five common chronic medical conditions. BMC Health Services Research, 18(1), 498. https://doi.org/10.1186/s12913-018-3316-2
  • Suhaimi, N., Vahdat, V., & Griffin, J. (2018). Building a flexible simulation model for modeling multiple outpatient orthopedic clinics. In 2018 Winter Simulation Conference (WSC), December 9–12, Gothenburg, Sweden. (pp. 2612–2623). IEEE. https://doi.org/10.1109/WSC.2018.8632451
  • Swana, E. F. ;, Doorsamy, W. ;, Bokoro, P. T., Link, S., Fezeka Swana, E., Doorsamy, W., & Bokoro, P. (2022). Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset. Sensors 2022, 22(9), 3246. https://doi.org/10.3390/S22093246
  • Tabassum, S., Pereira, F. S. F., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 8(5), 1–21. https://doi.org/10.1002/widm.1256
  • Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: A real-case study. Medical & Biological Engineering & Computing, 60(4), 969–990. https://doi.org/10.1007/s11517-022-02525-z
  • Traoré, M. K., Zacharewicz, G., Duboz, R., & Zeigler, B. (2019). Modeling and simulation framework for value-based healthcare systems. SIMULATION, 95(6), 481–497. https://doi.org/10.1177/0037549718776765
  • Verburg, I. W. M., Keizer, N. F. D., Jonge, E. D., Peek, N., & Salluh, J. I. F. (2014). Comparison of Regression Methods for Modeling Intensive Care Length of Stay, 9(10), 9(10. https://doi.org/10.1371/journal.pone.0109684
  • Zaric, G. S. (2003). The impact of ignoring population heterogeneity when Markov models are used in cost-effectiveness analysis. Medical Decision Making, 23(5), 379–386. https://doi.org/10.1177/0272989X03256883
  • Zhang, W., Emrich, S., & Zeng, E. (2010). A two-stage machine learning approach for pathway analysis. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, 18–21 December, Hong Kong, China. (pp. 274–279). https://doi.org/10.1109/BIBM.2010.5706576
  • Zhang, Y., Luo, L., Zhang, F., Kong, R., Yang, J., Feng, Y., & Guo, H. (2020). Emergency patient flow forecasting in the radiology department. Health Informatics Journal, 26(4), 146045822090188. https://doi.org/10.1177/1460458220901889