University of Texas Medical Branch at Galveston
Galveston, Texas
Subodh M. Lele, MD

S.M. Lele1, J.R. Petersen, A.O.1, Okorodudu1, R. Hagner2, C. Horne3, D.F. Cowan1, and A.A. Mohammad1
1The University of Texas Medical Branch, Galveston, Texas.
2Intelligent Medical Imaging, Inc., West Palm Beach, Florida
3MD Anderson Cancer Center, Houston, Texas

The clinical laboratory is a complex system composed of several variables (such as laboratory design, analytical instruments, number and type of samples and employees) and their interactions. There is a constant need for laboratory managers to experiment with these variables so as to meet the need for improved turn around times (TAT) and customer satisfaction. Computer simulation technology helps in achieving this goal by experimenting with a detailed model of the real system but without the high cost of the conventional trial and error process.

Simulation by definition is experimentation with a detailed model of a real system to determine how the system will respond to changes in its structure, environment or underlying assumptions. Medmodel, a commercially available simulation software program, was used to develop a comprehensive model of a clinical laboratory. Models of the hematology laboratory and the receiving and processing areas of the chemistry and hematology laboratories were tested at the MD Anderson Cancer Center (MDACC), in Houston, and at University of Texas Medical Branch in Galveston (UTMB), respectively. At MDACC, the simulation model was used to study the impact of MICRO 21 (an automated microscope), on TAT and employee utilization for performing manual differential counts on blood smears. At UTMB, the simulation model was used to determine the impact of robotic automation of the processes performed in the receiving and processing areas of the hematology and chemistry laboratories.

At both institutions the TAT and employee utilization predicted by the simulation model correlated very well with the actual values. By using the validated models, we were able to predict a 20% reduction in the amount of labor required to perform 550 manual differential counts per day with the MICRO 21 and a 75% improvement in TAT for samples without differential counts at MDACC. At UTMB, the simulation model predicted no significant improvement in TAT and only a marginal gain in labor utilization.

In conclusion, we found computer simulation to be a useful and relatively inexpensive tool for accurately predicting the impact of changes in workflow, instrumentation and/or manpower and in providing a total solution.