Simulation program for optimal orthopedic call: a modeling system for orthopedic surgical trauma call

Mathematical modeling to define optimum operating room staffing for pediatric patients in trauma centers

BACKGROUND

Adult trauma centers (TCs) in the United States may be verified with an on-call operating room team if the performance improvement program shows no adverse outcome. Using queuing and simulation methodology, this study attempts to add a volume guideline for injured children.

METHODS

Data from 63 verified TCs identified demographic factors including specific information regarding the first pediatric trauma-related operation done between 11 pm and 7 am each month for 1 year.

RESULTS

The annual pediatric admits correlated with the number of operations (383) done from 11 pm to 7 am (P < .001). The probability of operation within 30 minutes of arrival varies with the number of admits and the percent of penetrating vs blunt injuries. This likely number of operations from 11 pm to 7 am beginning within 30 minutes of patient arrival would be 3.45, 4.21, and 4.95 for TCs admitting 150, 250, and 350 injured children per year, respectively. The probability that 2 rooms would be occupied simultaneously is 0.074 and 0.109 for centers with 160 and 260 pediatric trauma admissions, respectively.

CONCLUSION

Trauma centers performing less than 6 pediatric trauma operations per year from 11 pm to 7 am could conserve resources by using an on-call operating room team.

Surgical resource utilization in urban terrorist bombing: a computer simulation

BACKGROUND

The objective of this study was to analyze the utilization of surgical staff and facilities during an urban terrorist bombing incident.

METHODS

A discrete-event computer model of the emergency room and related hospital facilities was constructed and implemented, based on cumulated data from 12 urban terrorist bombing incidents in Israel.

RESULTS

The simulation predicts that the admitting capacity of the hospital depends primarily on the number of available surgeons and defines an optimal staff profile for surgeons, residents, and trauma nurses. The major bottlenecks in the flow of critical casualties are the shock rooms and the computed tomographic scanner but not the operating rooms. The simulation also defines the number of reinforcement staff needed to treat noncritical casualties and shows that radiology is the major obstacle to the flow of these patients.

CONCLUSION

Computer simulation is an important new tool for the optimization of surgical service elements for a multiple-casualty situation.

Using queueing theory to determine operating room staffing needs

BACKGROUND

To meet American College of Surgeons criteria, Level I and II trauma centers are required to have in-house operating room (OR) staff 24 hours per day. According to the number of emergency cases occurring, hospitals may have varying needs for OR staffing during the night shift. Queueing theory, the analysis of historic data to provide optimal service while minimizing waiting, is an objective method of determining staffing needs during any time period. This study was done to determine the need to activate a backup OR team during the night shift at a designated, verified Level II trauma center.

METHODS

The basic queueing theory formula for a single-phase, single-channel system was applied to patients needing the services of the OR. The mean arrival rate was determined by dividing the number of actual cases by 2,920 hours in a year (8 hours per night x 365). The mean service rate is determined by averaging the length of the actual cases during the period studied. Using the mean arrival rate and the mean service rate, the probability of two or more patients needing the OR at the same time was determined. This probability was used to reflect the likelihood of needing to activate the backup OR team. Simulation was then used to calculate the same probability and validate the results obtained from the queueing model.

RESULTS

All OR cases (n = 62) beginning after 11 PM and before 7 AM from July 1, 1996, through June 30, 1997, were analyzed. During the study period, the average arrival rate (A) was one patient every 5.9 days (0.0212 patient every hour), with an average service rate (mu) of 80.79 minutes per patient (0.7427 patients per hour). According to queueing theory, lambda = 0.0212 patients per hour, mu = 0.7427 patients per hour, lambda/mu = 0.0285, the probability of no patients being in the system (P0) = 0.9714, P1 = 0.0278, P> or =2 = 1 - (0.0278 + 0.9714) = 0.0008. The probability of two or more cases occurring simultaneously on the night shift is less than 0.1%.

CONCLUSION

In our institution, activation of a second OR team is unnecessary when the first team is busy with a case on the night shift because the likelihood of two cases occurring concurrently is less than one in a thousand. Queueing theory can be a valuable tool to use in determining the staffing needs of many hospital departments. Trauma centers should apply this mathematical model in optimizing the use of their operational resource.