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Modeling the impact of changing patient transportation systems on peri-operative process performance in a large hospital: insights from a computer simulation study

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Abstract

Transportation of patients is a key hospital operational activity. During a large construction project, our patient admission and prep area will relocate from immediately adjacent to the operating room suite to another floor of a different building. Transportation will require extra distance and elevator trips to deliver patients and recycle transporters (specifically: personnel who transport patients). Management intuition suggested that starting all 52 first cases simultaneously would require many of the 18 available elevators. To test this, we developed a data-driven simulation tool to allow decision makers to simultaneously address planning and evaluation questions about patient transportation. We coded a stochastic simulation tool for a generalized model treating all factors contributing to the process as JAVA objects. The model includes elevator steps, explicitly accounting for transporter speed and distance to be covered. We used the model for sensitivity analyses of the number of dedicated elevators, dedicated transporters, transporter speed and the planned process start time on lateness of OR starts and the number of cases with serious delays (i.e., more than 15 min). Allocating two of the 18 elevators and 7 transporters reduced lateness and the number of cases with serious delays. Additional elevators and/or transporters yielded little additional benefit. If the admission process produced ready-for-transport patients 20 min earlier, almost all delays would be eliminated. Modeling results contradicted clinical managers’ intuition that starting all first cases on time requires many dedicated elevators. This is explained by the principle of decreasing marginal returns for increasing capacity when there are other limiting constraints in the system.

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Notes

  1. This decision is reasonable because the first step in the process – the preadmission activities – is unchanged in the new configuration.

  2. Nonetheless, our analysis (to be discussed later) reveals interesting and fundamental relationships between the transportation component and the admission preoperative process. Specifically, it reveals delays that are inevitable unless one improves and expedites the admission process.

  3. To start 52 cases with 10 transporters, it is likely that all transporters are sometimes simultaneously occupied.

  4. Here it is particularly important to mention that by “scheduled start time” we mean the actual point in time where the surgery took place (when looking at historical data). For instance, if a given surgery should have started at 11:00, but due to various delays actually started at 14:00, then the right number (i.e., 14:00) was used. This way, we simulate and test exactly what happens in reality, instead of what was supposed to happen.

  5. Elevator dispatching in the improved model: For this purpose, since all traveling times from one location to the other are known (i.e., estimated in advance), the only dynamic ingredient of this estimate is resolving the question: “how many transporters are currently waiting in line for some given elevator?”. This could be easily answered by asking the last transporter that was dispatched to that elevator – via mobile phone, or video monitoring of the elevator lobbies.

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Financial Support

The work of the second author is supported in part by National Science Foundation grants DMS-0732175 and CMMI-0846554 (CAREER Award), an Air Force Office of Scientific Research (AFOSR) award FA9550-08-1-0369, a Singapore-MIT Alliance (SMA) grant and the Buschbaum Research Fund of Massachusetts Institute of Technology.

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No conflicts of interest.

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Authors and Affiliations

  1. Department of Statistics, University of Haifa, Haifa, 31905, Israel

    Danny Segev

  2. Sloan School of Management, Massachusetts Institute of Technology, E62-562, Cambridge, MA, 02139, USA

    Retsef Levi

  3. Harvard Medical School Anesthetist, Department of Anesthesia and Critical Care and Executive Medical Director of the Operating Rooms, Massachusetts General Hospital, Boston, MA, 02114, USA

    Peter F. Dunn

  4. Department of Anesthesiology, Surgery & Biomedical Informatics, Vanderbilt University School of Medicine, 1211 21st Avenue South, 701 MAB, Nashville, TN, 37212-1050, USA

    Warren S. Sandberg

Authors
  1. Danny Segev
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  2. Retsef Levi
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  3. Peter F. Dunn
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  4. Warren S. Sandberg
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Corresponding author

Correspondence to Warren S. Sandberg.

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Institution for Attribution

Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital

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Summary Statement

A generalized model of patient transport indicated that relatively few transporters and elevators are needed for optimal performance for delivering patients, but that process redesign is important to provide enough time for transportation.

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Segev, D., Levi, R., Dunn, P.F. et al. Modeling the impact of changing patient transportation systems on peri-operative process performance in a large hospital: insights from a computer simulation study. Health Care Manag Sci 15, 155–169 (2012). https://doi.org/10.1007/s10729-012-9191-1

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  • DOI: https://doi.org/10.1007/s10729-012-9191-1

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