Advertisement

Perioperative Workflow Simulation and Optimization in Orthopedic Surgery

  • Juliane NeumannEmail author
  • Christine Angrick
  • Daniel Rollenhagen
  • Andreas Roth
  • Thomas Neumuth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11041)

Abstract

Operating room management aims at the efficient coordination of surgical procedures by maximizing the number of surgical cases while minimizing the required surgery time, with the main goal of improving the patient outcome. Discrete Event Simulation can be utilized to describe, analyze and predict the impact of procedural changes in perioperative processes. The aim of this work is to provide a simulation approach for a holistic perioperative optimization. Therefore, two different process simulation techniques, namely Business Process Simulation and 3D Process Flow Simulation, were utilized. It could be shown that perioperative simulation could lead to the improvement of OR utilization, reduction of process duration and a decrease in personnel workload.

Keywords

Surgical process simulation Discrete event simulation Perioperative process opitimization Operating room management 

Notes

Acknowledgements

We would like to thank the staff of the Department of Joint Replacement for their kind support during the study. Many thanks are also owed to Fabiola Fernández-Gutiérrez for her patient assistance on Delmia.

References

  1. 1.
    Busse, T.: OP-Management: Grundlagen. medhochzwei Verlag (2010)Google Scholar
  2. 2.
    Dexter, F., Macario, A., Lubarsky, D.A., Burns, D.D.: Statistical method to evaluate management strategies to decrease variability in operating room utilization: application of linear statistical modeling and Monte Carlo simulation to operating room management. Anesthesiology 91, 262–274 (1999)CrossRefGoogle Scholar
  3. 3.
    Baumgart, A., et al.: Using computer simulation in operating room management: impacts on process engineering and performance. In: 40th HICSS, Hawaii (2007)Google Scholar
  4. 4.
    Marjamaa, A., Torkki, P.M., Hirvensalo, E.J., Kirvelä, O.A.: What is the best workflow for an operating room? A simulation study of five scenarios. Health Care Manag. Sci 12(2), 142 (2009)CrossRefGoogle Scholar
  5. 5.
    Fernandez-Gutierrez, F., Barnett, I., Taylor, B., Houston, G., Melzer, A.: Framework for detailed workflow analysis and modelling for simulation of multimodal image-guided interventions. JEIM 26(1), 75–90 (2013)Google Scholar
  6. 6.
    Fernandez-Gutierrez, F., Wolska-Krawczyk, M., Buecker, A., Houston, G., Melzer, A.: Workflow optimisation for multimodal imaging procedures: a case of combined X-ray and MRI-guided TACE. Minim. Invasive Ther. Allied Technol. 26(1), 31–38 (2016)CrossRefGoogle Scholar
  7. 7.
    Wiemuth, M., et al.: Application fields for the new Object Management Group (OMG) standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN) in the perioperative field. Int. J. Comput. Assist. Radiol Surg. 12(8), 1439–1449 (2017)CrossRefGoogle Scholar
  8. 8.
    Signavio Process Editor (academic Version). https://www.signavio.com/en/bpm-academic-initiative/. Accessed 13 June 2018
  9. 9.
    BIMP. http://bimp.cs.ut.ee/. Accessed 13 June 2018
  10. 10.
    Dassault Systèmes. http://www.3ds.com/delmia/. Accessed 13 June 2018

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Innovation Center Computer Assisted SurgeryLeipzig UniversityLeipzigGermany
  2. 2.Department of Orthopaedics, Traumatology and Reconstructive Surgery, Division of Joint Replacement and OrthopaedicsUniversity Hospital LeipzigLeipzigGermany

Personalised recommendations