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Operating room design using agent-based simulation to reduce room obstructions

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Abstract

This study seeks to improve the safety of clinical care provided in operating rooms (OR) by examining how characteristics of both the physical environment and the procedure affect surgical team movement and contacts. We video recorded staff movements during a set of surgical procedures. Then we divided the OR into multiple zones and analyzed the frequency and duration of movement from origin to destination through zones. This data was abstracted into a generalized, agent-based, discrete event simulation model to study how OR size and OR equipment layout affected surgical staff movement and total number of surgical team contacts during a procedure. A full factorial experiment with seven input factors – OR size, OR shape, operating table orientation, circulating nurse (CN) workstation location, team size, number of doors, and procedure type – was conducted. Results were analyzed using multiple linear regression with surgical team contacts as the dependent variable. The OR size, the CN workstation location, and team size significantly affected surgical team contacts. Also, two- and three-way interactions between staff, procedure type, table orientation, and CN workstation location significantly affected contacts. We discuss implications of these findings for OR managers and for future research about designing future ORs.

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Availability of data and material

Data used within this research is protected under an IRB protocol approved by the Medical University of South Carolina.

Code availability

Simulation models were developed in AnyLogic and are available upon request.

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Acknowledgements

The authors would like to thank the RIPCHD.OR Study Group for their contribution to the work supporting the effort in this study.

Funding

Research was supported by the Agency for Healthcare Research and Quality [grant number P30HS0O24380, 2015], as well as the Harriet and Jerry Dempsey Professorship in Industrial Engineering at Clemson University.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, College of Engineering, Computing and Applied Sciences, Clemson University, 100B Freeman Hall, Clemson, SC, 29634, USA

    Kevin Taaffe

  2. Hull College of Business, Augusta University, Augusta, GA, 30912, USA

    Yann B. Ferrand

  3. Department of Industrial Engineering, Clemson University, Clemson, SC, 29634, USA

    Amin Khoshkenar

  4. Department of Management, Clemson University, Clemson, SC, 29634, USA

    Lawrence Fredendall

  5. Access Telecare, Mount Pleasant, SC, 29464, USA

    Dee San

  6. Department of Psychology, Clemson University, Clemson, SC, 29634, USA

    Patrick Rosopa

  7. School of Architecture, Clemson University, Clemson, SC, 29634, USA

    Anjali Joseph

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  1. Kevin Taaffe
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Correspondence to Kevin Taaffe.

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This research received IRB approval from the Medical University of South Carolina.

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Appendices

Appendix 1: Table 6

Table 6 A snapshot of a spreadsheet log for an actual surgery (S01-ART5-Preoperative)
Full size table

Appendix 2: Zone Visits Selection Procedure

  • Step 1 – Remove zone visits with short durations:

    Starting with the full dataset of zone visits, discard all entries where the zone visit is less than 15 seconds, as this is a pass-through visit only. This will avoid considering such visits as destinations in the final path, since there was no sustained activity observed there in the video recordings.

  • Step 2 – Remove transient zone visits:

    Discard all remaining transitional and doorway zone visits from the travel path of a subject since these cannot be conceptually considered destination zones. The associated duration of these zone visits is assigned to the nearest functional zone (distances are measured between the central points of two zones). The video recordings of the 23 procedures indicate that the occurrence of activities in these zones is low, and a reassignment of activity greater than 15 seconds occurred less than 6% of the time. (This reduced the zone count from 19 to 13 zones, each contained in at least one group in the next step.)

  • Step 3a – Aggregate zones into zone groups:

    Based on proximity and functionality, identify zone groups where an opportunity exists to select a single zone to represent activity. These zone groupings do not need to be mutually exclusive. The zone groups identified for the OR in Figure 1 are:

  •         Group 1.CN Workstation, Support Zone 1, Support Zone 5

  •         Group 2.Support Zone 2, Supply Zone 1, Support Zone 3

  •         Group 3.Supply Zone 2, Surgeon Workstation, Anesthesia Workstation

  •         Group 4.Surgical Table 1, Foot of Table

  •         Group 5.Surgical Table 2, Foot of Table, Anesthesia Workstation

  •         Group 6.Supply Zone 2, Support Zone 3, Anesthesia Workstation

  • Step 3b – Aggregate zone visits within zone groups:

    For each subset of consecutive zone visits within a zone group, identify and denote the zone visit with the longest duration as the destination zone. All durations of the other zone visits of the subset are assigned to the destination zone, and the zone visits are removed from the dataset.

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Taaffe, K., Ferrand, Y.B., Khoshkenar, A. et al. Operating room design using agent-based simulation to reduce room obstructions. Health Care Manag Sci 26, 261–278 (2023). https://doi.org/10.1007/s10729-022-09622-3

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