Simulation model for emergency department

Selected input data, including the number of physicians, nurses, and treatment beds, and the blood test turnaround time, then were varied systematically to determine their simulated effect on patient throughput time, selected queue sizes, and rates of resource utilization.

Methods The new approach models a hierarchy of heterogeneous interacting pseudo-agents in a DES, where pseudo-agents are entities with embedded decision logic.

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Background Overcrowded emergency departments ED are an ongoing issue for hospital staff, healthcare administrators, policy makers and patients. Published online May Methods This new approach of modeling physicians and their delegates as interacting pseudo-agents is first validated before being implemented in an ED DES model.

Methods of computer simulation have often been employed to model ED activity because it allows researchers to analyze the effects of re-organizing resources in the ED without making potentially costly changes. Published online May The physician attends to patients of a higher priority before anything else. A number of studies have argued for the inclusion of skill-based specification which would allow a physician or delegate to prioritize tasks and produce a more realistic result [ 2 , 3 ]. The model assumes that at anytime one physician and one delegate are scheduled. Patient throughput time varied directly with laboratory service times and inversely with the number of physician or nurse servers. The purpose of this study is to present an alternative approach where physicians and their delegates in the ED are modeled as interacting pseudo-agents in a DES and to compare it with the traditional approach ignoring such interactions. To model interactions, separate entities are created for the physician and delegate. Selected input data, including the number of physicians, nurses, and treatment beds, and the blood test turnaround time, then were varied systematically to determine their simulated effect on patient throughput time, selected queue sizes, and rates of resource utilization. Compared to analytic queuing models and system dynamics, DES is capable of modeling more complex non-linear systems while taking into account patient history, staff scheduling and multiple resource constraints. Background Overcrowded emergency departments ED are an ongoing issue for hospital staff, healthcare administrators, policy makers and patients. It is a process-oriented model that is represented by a network of queues for services that a patient flows through where attributes determine the pathway of the patient. The interaction occurs once the delegate has assessed a patient and requires consultation with the physician about the treatment plan. To the best of our knowledge, this approach of using interacting pseudo-agents has never been implemented when modeling the ED using DES.

Consequently, this study did not incorporate interaction between the physician and their delegates, which is a common limitation in previous simulation studies of the ED.

To overcome some of these issues, analysts have used agent-based modeling ABM.

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Morgan E Lim: ac. There are two signals: one is sent to both the physician and delegate to alert that there is a patient waiting in the queue and the other is sent to the physician from the delegate to alert that the delegate is waiting for a consult.

Decisions made by the physician and delegate are summarized below. To model interactions, separate entities are created for the physician and delegate. Additionally, we conduct sensitivity analyses on key parameters in the model. As such, ignoring indirect patient demands by ignoring the interactions between physicians and their delegates may result in an overestimation of staff resource availability and thus provide inaccurate estimates of resource utilization percent of scheduled time spent with patient and patient LOS. Process-oriented models also tend to neglect indirect patient-related tasks that physicians are required to perform e. Previous work has explored the use of ABM to model different ED physician staffing schedules [ 15 ], patient diversion strategies [ 16 ], and differing radiology process times [ 17 ]. The interaction occurs once the delegate has assessed a patient and requires consultation with the physician about the treatment plan.

As such, ignoring indirect patient demands by ignoring the interactions between physicians and their delegates may result in an overestimation of staff resource availability and thus provide inaccurate estimates of resource utilization percent of scheduled time spent with patient and patient LOS. Conclusion This example shows the importance of accurately modeling physician relationships and the roles in which they treat patients.

They are compared based on physician and delegate utilization, patient waiting time for treatment, and average length of stay. Decisions made by the physician and delegate are summarized below. Neglecting these relationships could lead to inefficient resource allocation due to inaccurate estimates of physician and delegate time spent on patient related activities and length of stay. In the review [ 1 ], only one previous DES study attempted to include multi-tasking by fragmenting physicians and nurses into several parts where each part represented a task [ 4 ]. This is an unrealistic depiction of ED care because physicians have a skill hierarchy where a physician will most likely not perform a task that can easily be performed by a delegate such as a medical student, resident physician, physician assistant, or nurse practitioner. ABMs are also difficult to implement because these types of models are often based on theories or subjective data e. The pseudo-agents represent a physician and delegate, where the physician plays a senior role to the delegate i. To overcome these issues, a new method was developed to model a hierarchy of heterogeneous interacting pseudo-agents in a DES, where pseudo-agents are entities with embedded decision logic [ 20 ].

Agents can represent people e. In the review [ 1 ], only one previous DES study attempted to include multi-tasking by fragmenting physicians and nurses into several parts where each part represented a task [ 4 ]. Additionally, we conduct sensitivity analyses on key parameters in the model.

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