title: [How many patient transfer rooms are necessary for my OR suite? : Effect of the number of OR transfer rooms on waiting times and patient throughput in the OR - analysis by simulation].
abstract: BACKGROUND
  In most hospitals the operating rooms (OR) are separated from the rest of the hospital by transfer rooms where patients have to pass through for reasons of hygiene. In the OR transfer room patients are placed on the OR table before surgery and returned to the hospital bed after surgery. It could happen that the number of patients who need to pass through a transfer room at a certain point in time exceed the number of available transfer rooms. As a result the transfer rooms become a bottleneck where patients have to wait and which, in turn, may lead to delays in the OR suite. In this study the ability of a discrete event simulation to analyze the effect of the duration of surgery and the number of ORs on the number of OR transfer rooms needed was investigated.
  
  
  METHODS
  This study was based on a discrete event simulation model developed with the simulation software AnyLogic®. The model studied the effects of the number of OR transfer rooms on the processes in an OR suite of a community hospital by varying the number of ORs from one to eight and using different surgical portfolios. Probability distributions for the process duration of induction, surgery and recovery and transfer room processes were calculated on the basis of real data from the community hospital studied. Furthermore, using a generic simulation model the effect of the average duration of surgery on the number of OR transfer rooms needed was examined.
  
  
  RESULTS
  The discrete event simulation model enabled the analysis of both quantitative as well as qualitative changes in the OR process and setting. Key performance indicators of the simulation model were patient throughput per day, the probability of waiting and duration of waiting time in front of OR transfer rooms. In the case of a community hospital with 1 transfer room the average proportion of patients waiting before entering the OR was 17.9 % ± 9.7 % with 3 ORs, 37.6 % ± 9.7 % with 5 ORs and 62.9 % ± 9.1 % with 8 ORs. The average waiting time of patients in the setting with 3 ORs was 3.1 ± 2.7 min, with 5 ORs 5.0 ± 5.8 min and with 8 ORs 11.5 ± 12.5 min. Based on this study the community hospital needs a second transfer room starting from 4 ORs so that there is no bottleneck for the subsequent OR processes. The average patient throughput in a setting with 4 ORs increased significantly by 0.3 patients per day when a second transfer room is available. The generic model showed a strong effect of the average duration of surgery on the number of transfer rooms needed.
  
  
  CONCLUSIONS
  There was no linear correlation between the number of transfer rooms and the number of ORs. The shorter the average duration of surgery, the earlier an additional transfer room is required. Thus, hospitals with shorter duration of surgery and fewer ORs may need the same or more transfer rooms than a hospital with longer duration of surgery and more ORs. However, with respect to an economic analysis, the costs and benefits of installing additional OR transfer rooms need to be calculated using the profit margins of the specific hospital.
authors.affiliation: Karlsruher Institut für Technologie, Institut für Operations Research, Diskrete Optimierung und Logistik, Karlsruhe, Deutschland.
authors.forename: C
authors.initials: C
authors.lastname: Messer
authors.affiliation: Karlsruher Institut für Technologie, Institut für Operations Research, Diskrete Optimierung und Logistik, Karlsruhe, Deutschland.
authors.forename: A
authors.initials: A
authors.lastname: Zander
authors.affiliation: Karlsruher Institut für Technologie, Institut für Operations Research, Diskrete Optimierung und Logistik, Karlsruhe, Deutschland.
authors.forename: I V
authors.initials: IV
authors.lastname: Arnolds
authors.affiliation: Karlsruher Institut für Technologie, Institut für Operations Research, Diskrete Optimierung und Logistik, Karlsruhe, Deutschland.
authors.forename: S
authors.initials: S
authors.lastname: Nickel
authors.affiliation: Klinik für Anästhesiologie, Intensivmedizin, Notfallmedizin und Schmerztherapie, Fürst-Stirum-Klinik Bruchsal, Gutleutstr. 1-14, 76646, Bruchsal, Deutschland. martin.schuster@kliniken-lk.de.
authors.forename: M
authors.initials: M
authors.lastname: Schuster
keywords: Appointments and schedules; Computer simulation; Efficiency, organizational; Operating room management; Time factors