Optimal Workflow Decisions for Investigators in Systems with Interruptions

Dobson, Gregory; Tezcan, Tolga; Tilson, Vera

doi:10.2139/ssrn.1921888

Cited by 17 publications

(19 citation statements)

References 41 publications

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“…Recall that we have defined the switching time as an unproductive time during which no job is being processed. The existence of switching time is well recognized in the literature (Dobson et al 2013, Rubinstein et al 2001, Seshadri and Shapira 2001, Speier et al 1999. In a generic application, switching time is essentially a "fixed setup time" for the unloading or shutdown and the loading or startup of a job.…”

Section: Model Description and Propertiesmentioning

confidence: 99%

The Effects of Multitasking on Operations Scheduling

Hall

Leung

2015

Production and Operations Management

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This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.

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Section: Model Description and Propertiesmentioning

confidence: 99%

The Effects of Multitasking on Operations Scheduling

Hall

Leung

2015

Production and Operations Management

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“…The latter supplements class-separation with dynamic resource allocation, and it is shown to dominate the other two. There are additional papers that cater to specific ED characteristics: Yom-Tov and model the ED as a singleclass time-varying queueing system with feedback (Erlang-R), operating in the QED regime, and in support of staffing physicians and nurses; Dobson et al (2013) develop an overloaded queueing network to analyze the impact of interruptions on ED throughput; Zayas-Caban et al…”

Section: Literature Reviewmentioning

confidence: 99%

Control of Patient Flow in Emergency Departments, or Multiclass Queues with Deadlines and Feedback

Huang

Carmeli²,

Mandelbaum

2015

Operations Research

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We consider the control of patient flow through physicians in emergency departments (EDs). The physicians must choose between catering to patients right after triage, who are yet to be checked, and those that are in-process (IP), who are occasionally returning to be checked. Physician capacity is thus modeled as a queueing system with multiclass customers, where some of the classes face deadline constraints on their time-till-first-service, while the other classes feedback through service while incurring congestion costs. We consider two types of such costs: first, costs that are incurred at queue-dependent rates, and second, costs that are functions of IP sojourn time. The former is our base-model, which paves the way for the latter (perhaps more ED-realistic). In both cases, we propose and analyze scheduling policies that, asymptotically in conventional heavy-traffic, minimize congestion costs while adhering to all deadline constraints. Our policies have two parts: the first chooses between triage and IP patients; assuming triage patients are chosen, the physicians serve the one who is closest to violating the deadline; alternatively, IP patients are served according to a Gcµ rule, in which µ is simply modified to account for feedbacks. For our proposed policies, we establish asymptotic optimality, and develop some congestion laws (snapshot principles) that support forecasting of waiting and sojourn times. Simulation then shows that these policies outperform some commonly-used ones. It also validates our laws and demonstrates that some ED features, the complexity of which reaches beyond our model (e.g., time-varying arrival rates, Leave-Without-Being-Seen (LWBS) or Leave-Against-Medical-Advice (LAMA)), do not lead to significant performance degradation.

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“…Argon and Ziya (2009) used average waiting time as the performance metric in a general service system with two classes of customers, in which customer classification is imperfect, and showed that prioritizing customers according to the probability of being from the class that should have a higher priority when classification is perfect outperforms any finite-class priority policy. Dobson et al (2013) developed a heavy-traffic model with an investigator and server interruptions to study physician choice in prioritizing patients. Downloaded from informs.org by [141.213.167.64] on 17 April 2015, at 08:37 .…”

Section: Literature Reviewmentioning

confidence: 99%

Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency

Saghafian

Hopp

Oyen

et al. 2014

M&SOM

103

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H ospital emergency departments (EDs) typically use triage systems that classify and prioritize patients almost exclusively in terms of their need for timely care. Using a combination of analytic and simulation models, we demonstrate that adding an up-front estimate of patient complexity to conventional urgency-based classification can substantially improve both patient safety (by reducing the risk of adverse events) and operational efficiency (by shortening the average length of stay). Moreover, we find that EDs with high resource (physician and/or examination room) utilization, high heterogeneity in the treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most from complexity-augmented triage. Finally, we find that (1) although misclassification of a complex patient as simple is slightly more harmful than vice versa, complexity-augmented triage is relatively robust to misclassification error rates as high as 25%; (2) streaming patients based on complexity information and prioritizing them based on urgency is better than doing the reverse; and (3) separating simple and complex patients via streaming facilitates the application of lean methods that can further amplify the benefit of complexity-augmented triage.

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Optimal Workflow Decisions for Investigators in Systems with Interruptions

Cited by 17 publications

References 41 publications

The Effects of Multitasking on Operations Scheduling

The Effects of Multitasking on Operations Scheduling

Control of Patient Flow in Emergency Departments, or Multiclass Queues with Deadlines and Feedback

Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency

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