Design and Analysis of Diagnostic Service Centers

Wang, Xiaofang; Debo, Laurens G.; Scheller‐Wolf, Alan; Smith, Stephen F.

doi:10.1287/mnsc.1100.1236

Cited by 66 publications

(57 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When queuing theory does not take strategic behavior into account, it instead suggests that pooling queues should result in shorter throughput times (Jouini et al 2008). Thus, our paper provides empirical support for prior analytical models that predicted that human behaviors could reduce the positive benefit of using a common pool (Cachon and Zhang 2007, Gilbert and Weng 1998, Hopp et al 2007, Jouini et al 2008, Wang et al 2010). …”

Section: Theoretical Contributionssupporting

confidence: 60%

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

2015

View full text Add to dashboard Cite

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.Key words: pooling, queue management, strategic servers, social loafing, empirical operations, health care 3 IntroductionImproving efficiency and customer experience are key objectives for managers of service organizations.Skillful application of operations management principles may prove helpful for achieving these goals. In this paper, we investigate queue management, a key operational decision. More specifically, we explore the impact on throughput time of using a pooled queuing system or a dedicated queuing system.Prior work on queue management has demonstrated through analytical models that pooling separate streams of identical customers into a single queue served by a bank of identical servers leads to a reduction in waiting time and an increase in server utilization (Eppen 1979, Kleinrock 1976. This occurs because pooling reduces the negative impact of variability in arrival times and processing times. Pooling enables incoming work to be processed by any one server from a bank of servers, which decreases the odds that an incoming unit of work will have to wait for service. This situation compares to the one where the unit of work can only be processed by a single dedicated server.Queuing theorists have advanced this stream of research by identifying conditions under which queue pooling may not yield the expected performance improvements (Debo et al. 2008, van Dijk and van der Sluis 2009, Hopp et al. 2007, Jouini et al. 2008, Loch 1998, Mandelbaum and Reiman 1998. However, few empirical studies have examined the performance of pooled versus dedicated queuing systems. This is an important omission because, in practice, employees can often make adjustments to how they manage their work system to achieve a goal, such as increasing their productivity (Hopp et al. 2009). Operations management scholars advocat...

show abstract

Section: Theoretical Contributionssupporting

confidence: 60%

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

2015

View full text Add to dashboard Cite

show abstract

“…We refer the reader to Hassin and Haviv's (2003) excellent review of this literature. Other notable papers that explore the interaction between service quality and congestion include Allon and Federgruen (2007), Chase and Tansik (1983), Gans (2002), Hopp et al (2007), Lovejoy and Sethuraman (2000), Oliva and Sterman (2001), Png and Reitman (1994), Ren and Wang (2008), Veeraraghavan andDebo (2009), andWang et al (2010).…”

Section: Related Literaturementioning

confidence: 99%

“…Research articles that acknowledge the existence of interactions between service duration and quality in different domains include Kostami and Rajagopalan (2009) Wang et al (2010) (medical diagnostic services). In these papers, the customer demand is assumed to be exogenous and/or pricing decisions are absent.…”

Section: Related Literaturementioning

confidence: 99%

Quality–Speed Conundrum: Trade-offs in Customer-Intensive Services

2011

View full text Add to dashboard Cite

I n many services, the quality or value provided by the service increases with the time the service provider spends with the customer. However, longer service times also result in longer waits for customers. We term such services, in which the interaction between quality and speed is critical, as customer-intensive services. In a queueing framework, we parameterize the degree of customer intensity of the service. The service speed chosen by the service provider affects the quality of the service through its customer intensity. Customers queue for the service based on service quality, delay costs, and price. We study how a service provider facing such customers makes the optimal "quality-speed trade-off." Our results demonstrate that the customer intensity of the service is a critical driver of equilibrium price, service speed, demand, congestion in queues, and service provider revenues. Customer intensity leads to outcomes very different from those of traditional models of service rate competition. For instance, as the number of competing servers increases, the price increases, and the servers become slower.

show abstract

“…Thus the focus should be placed on elective procedures and less severe illnesses, which are more likely controlled by insurers and providers to reduce the unnecessary care in peak season by changing copayments or service levels. For example, Wang et al (2010Wang et al ( , 2012 have focused on diagnostic service centers which provide expert advice over phone to help patients make more informed, and thus more cost-effective treatment decisions.…”

Section: Discussionmentioning

confidence: 99%