Person:

Song, Hummy

We conduct an empirical investigation on the impact of pooling tasks and resources on throughput times in a discretionary work setting. We use an Emergency Department’s (ED) patient-level data (N = 234,334) from 2007 to 2010 to test our hypotheses. We find that when the ED’s work system had pooled tasks and resources, patients’ lengths of stay were longer than when the ED converted to having dedicated tasks and resources. More specifically, we find that dedicated systems resulted in a 9 percent overall decrease in length of stay, which corresponds to a 25-minute reduction in length of stay for an average patient of medium severity in this ED. We propose that the improved performance comes from a reduction in social loafing and a more distributed utilization of shared resources. These benefits outweigh the expected efficiency gains from pooling, which are commonly predicted by queuing theory.

We conduct an empirical investigation of the impact of two different queue management systems on throughput times. Using an Emergency Department’s (ED) patient-level data (N = 231,081) from 2007 to 2010, we find that patients’ lengths of stay (LOS) were longer when physicians were assigned patients under a pooled queuing system, compared to when each physician operated under a dedicated queuing system. The dedicated queuing system resulted in a 10 percent decrease in LOS—a 32-minute reduction in LOS for an average patient of medium severity in this ED. We propose that the dedicated queuing system yielded shorter throughput times because it provided physicians with greater ability and incentive to manage their patients’ flow through the ED from arrival to discharge. Consistent with social loafing theory, our analysis shows that patients were treated and discharged at a faster rate in the dedicated queuing system than in the pooled queuing system. We conduct additional analyses to rule out alternate explanations, such as stinting on care and decreased quality of care. Our paper has implications for health care organizations and others seeking to reduce throughput time, resource utilization, and costs.