Decreasing lab turnaround time improves emergency department throughput and decreases emergency medical services diversion: a simulation model.

BACKGROUND: The effect of decreasing lab turnaround times on emergency department (ED) efficiency can be estimated through system-level simulation models and help identify important outcome measures to study prospectively. Furthermore, such models may suggest the advantage of bedside or point-of-care testing and how they might affect efficiency measures. OBJECTIVES: The authors used a sophisticated simulation model in place at an adult urban ED with an annual census of 55,000 patient visits. The effect of decreasing turnaround times on emergency medical services (EMS) diversion, ED patient throughput, and total ED length of stay (LOS) was determined. METHODS: Data were generated by using system dynamics analytic modeling and simulation approach on 90 separate days from December 2, 2007, through February 29, 2008. The model was a continuous simulation of ED flow, driven by real-time actual patient data, and had intrinsic error checking to assume reasonable goodness-of-fit. A return of complete laboratory results incrementally at 120, 100, 80, 60, 40, 20, and 10 minutes was compared. Diversion calculation assumed EMS closure when more than 10 patients were in the waiting room and 100% ED bed occupancy had been reached for longer than 30 minutes, as per local practice. LOS was generated from data insertion into the patient flow stream and calculation of time to specific predefined gates. The average accuracy of four separate measurement channels (waiting room volume, ED census, inpatient admit stream, and ED discharge stream), all across 24 hours, was measured by comparing the area under the simulated curve against the area under the measured curve. Each channel's accuracy was summed and averaged for an overall accuracy rating. RESULTS: As lab turnaround time decreased from 120 to 10 minutes, the total number of diversion days (maximum 57 at 120 minutes, minimum 29 at 10 minutes), average diversion hours per day (10.8 hours vs. 6.0 hours), percentage of days with diversion (63% vs. 32%), and average ED LOS (2.77 hours vs. 2.17 hours) incrementally decreased, while average daily throughput (104 patients vs. 120 patients) increased. All runs were at least 85% accurate. CONCLUSIONS: This simulation model suggests compelling improvement in ED efficiency with decreasing lab turnaround time. Outcomes such as time on EMS diversion, ED LOS, and ED throughput represent important but understudied areas that should be evaluated prospectively. EDs should consider processes that will improve turnaround time, such as point-of-care testing, to obtain these goals.

Assessing breast cancer risk in women.

Understanding modifiable and nonmodifiable factors that increase or decrease breast cancer risk allows family physicians to counsel women appropriately. Nonmodifiable factors associated with increased breast cancer risk include advanced age, female sex, family history of breast cancer, increased breast density, genetic predisposition, menarche before age 12 years, and natural menopause after age 45 years. Hormonal factors associated with breast cancer include advanced age at first pregnancy, exposure to diethylstilbestrol, and hormone therapy. Environmental factors include therapeutic radiation. Obesity is also associated with increased rates of breast cancer. Factors associated with decreased cancer rates include pregnancy at an early age, late menarche, early menopause, high parity, and use of some medications, such as selective estrogen receptor modulators and, possibly, nonsteroidal anti-inflammatory agents and aspirin. No convincing evidence supports the use of dietary interventions for the prevention of breast cancer, with the exception of limiting alcohol intake.