Skip to main content

2020 | OriginalPaper | Buchkapitel

Constructing Holistic Patient Flow Simulation Using System Approach

verfasst von : Tesfamariam M. Abuhay, Oleg G. Metsker, Aleksey N. Yakovlev, Sergey V. Kovalchuk

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Patient flow often described as a systemic issue requiring a systemic approach because hospital is a collection of highly dynamic, interconnected, complex, ad hoc and multi-disciplinary sub-processes. However, studies on holistic patient flow simulation following system approach are limited and/or poorly understood. Several researchers have been investigating single departments such as ambulatory care unit, Intensive Care Unit (ICU), emergency department, surgery department or patients’ interaction with limited resources such as doctor, endoscopy or bed, independently. Hence, this article demonstrates how to achieve system approach in constructing holistic patient flow simulation, while maintaining the balance between the complexity and the simplicity of the model. To this end, system approach, network analysis and discrete event simulation (DES) were employed. The most important departments in the diagnosis and treatment process are identified by analyzing network of hospital departments. Holistic patient flow simulation is constructed using DES following system approach. Case studies are conducted and the results illustrate that healthcare systems must be modeled and investigated as a complex and interconnected system so that the real impact of changes on the entire system or parts of the system could be observed at strategic as well as operational levels.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Soulakis, N.D., et al.: Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J. Am. Med. Inform. Assoc. 22(2), 299–311 (2015)CrossRef Soulakis, N.D., et al.: Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J. Am. Med. Inform. Assoc. 22(2), 299–311 (2015)CrossRef
2.
Zurück zum Zitat Chand, S., Moskowitz, H., Norris, J.B., Shade, S., Willis, D.R.: Improving patient flow at an outpatient clinic: Study of sources of variability and improvement factors. Health Care Manag. Sci. 12(3), 325–340 (2009)CrossRef Chand, S., Moskowitz, H., Norris, J.B., Shade, S., Willis, D.R.: Improving patient flow at an outpatient clinic: Study of sources of variability and improvement factors. Health Care Manag. Sci. 12(3), 325–340 (2009)CrossRef
3.
Zurück zum Zitat Côté, M.J.: Understanding patient flow. Decis. Line 31, 8–13 (2000) Côté, M.J.: Understanding patient flow. Decis. Line 31, 8–13 (2000)
4.
Zurück zum Zitat Santibáñez, P., Chow, V.S., French, J., Puterman, M.L., Tyldesley, S.: Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Manag. Sci. 12(4), 392–407 (2009)CrossRef Santibáñez, P., Chow, V.S., French, J., Puterman, M.L., Tyldesley, S.: Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Manag. Sci. 12(4), 392–407 (2009)CrossRef
5.
Zurück zum Zitat Christensen, B.A.: Improving ICU patient flow through discrete-event simulation. Massachusetts Institute of Technology (2012) Christensen, B.A.: Improving ICU patient flow through discrete-event simulation. Massachusetts Institute of Technology (2012)
6.
Zurück zum Zitat Konrad, R., et al.: Modeling the impact of changing patient flow processes in an emergency department: insights from a computer simulation study. Oper. Res. Heal. Care 2(4), 66–74 (2013)CrossRef Konrad, R., et al.: Modeling the impact of changing patient flow processes in an emergency department: insights from a computer simulation study. Oper. Res. Heal. Care 2(4), 66–74 (2013)CrossRef
7.
Zurück zum Zitat Cocke, S., et al.: UVA emergency department patient flow simulation and analysis. In: 2016 IEEE Systems and Information Engineering Design Symposium, pp. 118–123 (2016) Cocke, S., et al.: UVA emergency department patient flow simulation and analysis. In: 2016 IEEE Systems and Information Engineering Design Symposium, pp. 118–123 (2016)
8.
Zurück zum Zitat Hurwitz, J.E., et al.: A flexible simulation platform to quantify and manage emergency department crowding. BMC Med. Inform. Decis. Mak. 14(1), 50 (2014)CrossRef Hurwitz, J.E., et al.: A flexible simulation platform to quantify and manage emergency department crowding. BMC Med. Inform. Decis. Mak. 14(1), 50 (2014)CrossRef
9.
Zurück zum Zitat Antonelli, D., Bruno, G., Taurino, T.: Simulation-based analysis of patient flow in elective surgery. In: Matta, A., Li, J., Sahin, E., Lanzarone, E., Fowler, J. (eds.) Proceedings of the International Conference on Health Care Systems Engineering. SPMS, vol. 61, pp. 87–97. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01848-5_7CrossRef Antonelli, D., Bruno, G., Taurino, T.: Simulation-based analysis of patient flow in elective surgery. In: Matta, A., Li, J., Sahin, E., Lanzarone, E., Fowler, J. (eds.) Proceedings of the International Conference on Health Care Systems Engineering. SPMS, vol. 61, pp. 87–97. Springer, Cham (2014). https://​doi.​org/​10.​1007/​978-3-319-01848-5_​7CrossRef
10.
Zurück zum Zitat Azari-Rad, S., Yontef, A., Aleman, D.M., Urbach, D.R.: A simulation model for perioperative process improvement. Oper. Res. Heal. Care 3, 22–30 (2014)CrossRef Azari-Rad, S., Yontef, A., Aleman, D.M., Urbach, D.R.: A simulation model for perioperative process improvement. Oper. Res. Heal. Care 3, 22–30 (2014)CrossRef
11.
Zurück zum Zitat Swisher, J.R., Jacobson, S.H.: Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Manag. Sci. 5(2), 75–88 (2002)CrossRef Swisher, J.R., Jacobson, S.H.: Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Manag. Sci. 5(2), 75–88 (2002)CrossRef
12.
Zurück zum Zitat Almeida, R., Paterson, W.G., Craig, N., Hookey, L.: A patient flow analysis: identification of process inefficiencies and workflow metrics at an ambulatory endoscopy unit. Can. J. Gastroenterol. Hepatol. 2016, 1–7 (2016) Almeida, R., Paterson, W.G., Craig, N., Hookey, L.: A patient flow analysis: identification of process inefficiencies and workflow metrics at an ambulatory endoscopy unit. Can. J. Gastroenterol. Hepatol. 2016, 1–7 (2016)
13.
Zurück zum Zitat Monks, T., et al.: A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv. Res. 16, 1–8 (2016)CrossRef Monks, T., et al.: A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv. Res. 16, 1–8 (2016)CrossRef
14.
Zurück zum Zitat Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)CrossRef Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)CrossRef
15.
Zurück zum Zitat Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)CrossRef Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)CrossRef
16.
Zurück zum Zitat Gunal, M.M.: A guide for building hospital simulation models. Health Syst. 1(1), 17–25 (2012)CrossRef Gunal, M.M.: A guide for building hospital simulation models. Health Syst. 1(1), 17–25 (2012)CrossRef
17.
Zurück zum Zitat Anatoli Djanatliev, F.M.: Hospital processes within an integrated system view: a hybrid simulation approach. In: Proceedings of the 2016 Winter Simulation Conference, pp. 1364–1375 (2016) Anatoli Djanatliev, F.M.: Hospital processes within an integrated system view: a hybrid simulation approach. In: Proceedings of the 2016 Winter Simulation Conference, pp. 1364–1375 (2016)
18.
Zurück zum Zitat Kannampallil, T.G., Schauer, G.F., Cohen, T., Patel, V.L.: Considering complexity in healthcare systems. J. Biomed. Inform. 44(6), 943–947 (2011)CrossRef Kannampallil, T.G., Schauer, G.F., Cohen, T., Patel, V.L.: Considering complexity in healthcare systems. J. Biomed. Inform. 44(6), 943–947 (2011)CrossRef
19.
Zurück zum Zitat Kreindler, S.A.: The three paradoxes of patient flow: an explanatory case study. BMC Health Serv. Res. 17(1), 481 (2017)CrossRef Kreindler, S.A.: The three paradoxes of patient flow: an explanatory case study. BMC Health Serv. Res. 17(1), 481 (2017)CrossRef
20.
Zurück zum Zitat Vanberkel, P.T., Boucherie, R.J., Hans, E.W., Hurink, J.L., Litvak, N.: A survey of health care models that encompass multiple departments. University of Twente, Faculty of Mathematical Sciences (2009) Vanberkel, P.T., Boucherie, R.J., Hans, E.W., Hurink, J.L., Litvak, N.: A survey of health care models that encompass multiple departments. University of Twente, Faculty of Mathematical Sciences (2009)
21.
Zurück zum Zitat Abuhay, T.M., Krikunov, A.V., Bolgova, E.V., Ratova, L.G., Kovalchuk, S.V.: Simulation of patient flow and load of departments in a specialized medical center. Procedia Comput. Sci. 101, 143–151 (2016)CrossRef Abuhay, T.M., Krikunov, A.V., Bolgova, E.V., Ratova, L.G., Kovalchuk, S.V.: Simulation of patient flow and load of departments in a specialized medical center. Procedia Comput. Sci. 101, 143–151 (2016)CrossRef
22.
Zurück zum Zitat Kovalchuk, S.V., Funkner, A.A., Metsker, O.G., Yakovlev, A.N.: Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification. J. Biomed. Inform. 82, 128–142 (2018)CrossRef Kovalchuk, S.V., Funkner, A.A., Metsker, O.G., Yakovlev, A.N.: Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification. J. Biomed. Inform. 82, 128–142 (2018)CrossRef
23.
Zurück zum Zitat Suhaimi, N., Vahdat, V., Griffin, J.: Building a flexible simulation model for modeling multiple outpatient orthopedic clinics. In: 2018 Winter Simulation Conference (WSC), pp. 2612–2623 (2018) Suhaimi, N., Vahdat, V., Griffin, J.: Building a flexible simulation model for modeling multiple outpatient orthopedic clinics. In: 2018 Winter Simulation Conference (WSC), pp. 2612–2623 (2018)
24.
Zurück zum Zitat Tabassum, S., Pereira, F.S.F., Fernandes, S., Gama, J.: Social network analysis: an overview. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(5), 1–21 (2018) Tabassum, S., Pereira, F.S.F., Fernandes, S., Gama, J.: Social network analysis: an overview. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(5), 1–21 (2018)
25.
Zurück zum Zitat Dunn, A.G., Westbrook, J.I.: Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. Soc. Sci. Med. 72(7), 1064–1068 (2011)CrossRef Dunn, A.G., Westbrook, J.I.: Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. Soc. Sci. Med. 72(7), 1064–1068 (2011)CrossRef
26.
Zurück zum Zitat Benhiba, L., Loutfi, A., Abdou, M., Idrissi, J.: A classification of healthcare social network analysis applications. In: HEALTHINF 2017-10th International Conference on Health Informatics, pp. 147–158 (2017) Benhiba, L., Loutfi, A., Abdou, M., Idrissi, J.: A classification of healthcare social network analysis applications. In: HEALTHINF 2017-10th International Conference on Health Informatics, pp. 147–158 (2017)
28.
Zurück zum Zitat Banks, J.: Discrete-event System Simulation. International Series in Industrial and Systems Engineering, vol. Fourth. Prentice-Hall, Upper Saddle River (2005) Banks, J.: Discrete-event System Simulation. International Series in Industrial and Systems Engineering, vol. Fourth. Prentice-Hall, Upper Saddle River (2005)
32.
Zurück zum Zitat Marshall, A., Vasilakis, C., El-Darzi, E.: Length of stay-based patient flow models: recent developments and future directions. Health Care Manag. Sci. 8, 213–220 (2005)CrossRef Marshall, A., Vasilakis, C., El-Darzi, E.: Length of stay-based patient flow models: recent developments and future directions. Health Care Manag. Sci. 8, 213–220 (2005)CrossRef
33.
Zurück zum Zitat Ickowicz, A., Sparks, R., Wiley, J.: Modelling hospital length of stay using convolutive mixtures distributions. Stat. Med. 36(1), 122–135 (2016)MathSciNetCrossRef Ickowicz, A., Sparks, R., Wiley, J.: Modelling hospital length of stay using convolutive mixtures distributions. Stat. Med. 36(1), 122–135 (2016)MathSciNetCrossRef
34.
Zurück zum Zitat Lee, A.H., Ng, A.S., Yau, K.K.: Determinants of maternity length of stay: a Gamma mixture risk-adjusted model. Health Care Manag. Sci. 4(4), 249–55 (2001)CrossRef Lee, A.H., Ng, A.S., Yau, K.K.: Determinants of maternity length of stay: a Gamma mixture risk-adjusted model. Health Care Manag. Sci. 4(4), 249–55 (2001)CrossRef
35.
Zurück zum Zitat Houthooft, R., et al.: Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif. Intell. Med. 63, 191–207 (2015)CrossRef Houthooft, R., et al.: Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif. Intell. Med. 63, 191–207 (2015)CrossRef
37.
Zurück zum Zitat Chen, Y.-C.: A Tutorial on Kernel Density Estimation and Recent Advances (2017) Chen, Y.-C.: A Tutorial on Kernel Density Estimation and Recent Advances (2017)
38.
Zurück zum Zitat Vrieze, S.I.: Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17(2), 228–243 (2012)CrossRef Vrieze, S.I.: Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17(2), 228–243 (2012)CrossRef
39.
Zurück zum Zitat Simard, R., L’Ecuyer, P.: Computing the two-sided Kolmogorov-Smirnov distribution. J. Stat. Softw. 39(11), 1–18 (2011)CrossRef Simard, R., L’Ecuyer, P.: Computing the two-sided Kolmogorov-Smirnov distribution. J. Stat. Softw. 39(11), 1–18 (2011)CrossRef
Metadaten
Titel
Constructing Holistic Patient Flow Simulation Using System Approach
verfasst von
Tesfamariam M. Abuhay
Oleg G. Metsker
Aleksey N. Yakovlev
Sergey V. Kovalchuk
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-50423-6_31