IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v32y2020i3d10.1007_s10696-019-09344-9.html
   My bibliography  Save this article

Fine-grained simulation optimization for the design and operations of a multi-activity clinic

Author

Listed:
  • P. Troy

    (CIUSSS du Centre-Ouest-de-l’ıle-de-Montréal)

  • N. Lahrichi

    (Polytechnique Montréal)

  • D. Porubska

    (The Sir Mortimer B. Davis Jewish General Hospital)

  • L. Rosenberg

    (CIUSSS du Centre-Ouest-de-l’ıle-de-Montréal)

Abstract

To ensure that patients are appropriately prepared for surgical procedures in a welcoming environment, the Sir Mortimer B. Davis Jewish General Hospital, a McGill University affiliated teaching hospital located in Montreal, is redesigning and relocating its existing presurgical screening clinic so that it provides additional services and is more patient friendly. Given the services being added, limited space, and the desire of senior management to minimize overtime costs, physician idle time, and excessive patient waiting times, we apply simulation optimization to the operations of the redesigned clinic. The simulation optimization is then used to evaluate the effect of possible design decisions to be made by senior management, to ensure that the resulting clinic meets their goals. In contrast to existing research which generally limits clinic optimization to just a few facets, we simultaneously optimize the clinic’s multiple objectives at a fine-grained level with respect to individual decision variables for the start time of each physician, the appointment time of each patient, and the start, break, and lunch times of each staff member. To perform the optimization, we apply a simple heuristic to a simulation model of the clinic. We show, with this simple heuristic, that simultaneously optimizing the clinics’s multiple objectives by adjusting decision variables at this more granular level can significantly reduce physician idle time, staff overtime, and excessive patient waiting. This in turn makes it possible to evaluate design decisions in context of optimized operations. These results suggest the usefulness of this approach to other multi-activity clinics such as cancer treatment clinics.

Suggested Citation

  • P. Troy & N. Lahrichi & D. Porubska & L. Rosenberg, 2020. "Fine-grained simulation optimization for the design and operations of a multi-activity clinic," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 599-628, September.
  • Handle: RePEc:spr:flsman:v:32:y:2020:i:3:d:10.1007_s10696-019-09344-9
    DOI: 10.1007/s10696-019-09344-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-019-09344-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10696-019-09344-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pablo Santibáñez & Vincent Chow & John French & Martin Puterman & Scott Tyldesley, 2009. "Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation," Health Care Management Science, Springer, vol. 12(4), pages 392-407, December.
    2. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    3. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    4. Bjorn Berg & Brian Denton & Heidi Nelson & Hari Balasubramanian & Ahmed Rahman & Angela Bailey & Keith Lindor, 2010. "A Discrete Event Simulation Model to Evaluate Operational Performance of a Colonoscopy Suite," Medical Decision Making, , vol. 30(3), pages 380-387, May.
    5. Sheldon H. Jacobson & Shane N. Hall & James R. Swisher, 2006. "Discrete-Event Simulation of Health Care Systems," International Series in Operations Research & Management Science, in: Randolph W. Hall (ed.), Patient Flow: Reducing Delay in Healthcare Delivery, chapter 0, pages 211-252, Springer.
    6. Bjorn Berg & Brian T. Denton, 2012. "Appointment Planning and Scheduling in Outpatient Procedure Centers," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 131-154, Springer.
    7. Jie Xu & Edward Huang & Chun-Hung Chen & Loo Hay Lee, 2015. "Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(03), pages 1-34.
    8. Thomas Rohleder & Peter Lewkonia & Diane Bischak & Paul Duffy & Rosa Hendijani, 2011. "Using simulation modeling to improve patient flow at an outpatient orthopedic clinic," Health Care Management Science, Springer, vol. 14(2), pages 135-145, June.
    9. J-F Cordeau & G Laporte & A Mercier, 2001. "A unified tabu search heuristic for vehicle routing problems with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(8), pages 928-936, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brian Zoll & Pratik J. Parikh & Jennie Gallimore & Stephen Harrell & Brian Burke, 2015. "Impact of Diabetes E-Consults on Outpatient Clinic Workflow," Medical Decision Making, , vol. 35(6), pages 745-757, August.
    2. Jia, Shuai & Li, Chung-Lun & Xu, Zhou, 2020. "A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 174-196.
    3. Xiuli Qu & Yidong Peng & Nan Kong & Jing Shi, 2013. "A two-phase approach to scheduling multi-category outpatient appointments – A case study of a women’s clinic," Health Care Management Science, Springer, vol. 16(3), pages 197-216, September.
    4. Baril, Chantal & Gascon, Viviane & Miller, Jonathan & Côté, Nadine, 2016. "Use of a discrete-event simulation in a Kaizen event: A case study in healthcare," European Journal of Operational Research, Elsevier, vol. 249(1), pages 327-339.
    5. Vahab Vahdat & Jacqueline Griffin & James E. Stahl, 2018. "Decreasing patient length of stay via new flexible exam room allocation policies in ambulatory care clinics," Health Care Management Science, Springer, vol. 21(4), pages 492-516, December.
    6. Martin Comis & Catherine Cleophas & Christina Büsing, 2021. "Patients, primary care, and policy: Agent-based simulation modeling for health care decision support," Health Care Management Science, Springer, vol. 24(4), pages 799-826, December.
    7. Xiang Zhong & Hyo Kyung Lee & Molly Williams & Sally Kraft & Jeffery Sleeth & Richard Welnick & Lori Hauschild & Jingshan Li, 2018. "Workload balancing: staffing ratio analysis for primary care redesign," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 6-29, June.
    8. Chong Pan & Dali Zhang & Audrey Kon & Charity Wai & Woo Ang, 2015. "Patient flow improvement for an ophthalmic specialist outpatient clinic with aid of discrete event simulation and design of experiment," Health Care Management Science, Springer, vol. 18(2), pages 137-155, June.
    9. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    10. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    11. Chang, Kuo-Hao & Kuo, Po-Yi, 2018. "An efficient simulation optimization method for the generalized redundancy allocation problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1094-1101.
    12. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    13. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    14. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    15. Pau Fonseca i Casas, 2023. "A Continuous Process for Validation, Verification, and Accreditation of Simulation Models," Mathematics, MDPI, vol. 11(4), pages 1-25, February.
    16. repec:dar:wpaper:62383 is not listed on IDEAS
    17. Shuang Xiao & Guo Li & Yunjing Jia, 2017. "Estimating the Constant Elasticity of Variance Model with Data-Driven Markov Chain Monte Carlo Methods," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-23, February.
    18. David J. Eckman & Shane G. Henderson & Sara Shashaani, 2023. "Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 350-367, March.
    19. Jeffrey W. Ohlmann & Michael J. Fry & Barrett W. Thomas, 2008. "Route Design for Lean Production Systems," Transportation Science, INFORMS, vol. 42(3), pages 352-370, August.
    20. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    21. Nair, D.J. & Grzybowska, H. & Fu, Y. & Dixit, V.V., 2018. "Scheduling and routing models for food rescue and delivery operations," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 18-32.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:flsman:v:32:y:2020:i:3:d:10.1007_s10696-019-09344-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.