Patient Flow in the Emergency Department: A Classification and Analysis of Admission Process Policies

Kang, Hyojung; Nembhard, Harriet Black; Rafferty, Colleen; DeFlitch, Christopher J.

doi:10.1016/j.annemergmed.2014.04.011

Cited by 42 publications

(24 citation statements)

References 24 publications

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“…The total number of arrivals comprises 94% of disaster-victim patient arrivals and 6% of nondisaster-victim patient arrivals. Therefore, additional scenarios for different arrival rates and patient type percentage as [17] were devised. The as-is scenario uses an arrival rate of 729 patients per day with percentages of 1.5%, 24% and 74.5% for red, yellow and green & injection room patients, respectively.…”

Section: Model Results and Patient Surge Scenariomentioning

confidence: 99%

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Simulation modelling of a patient surge in an emergency department under disaster conditions

Gül

Güneri

2015

Croat. Oper. Res. Rev.

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Abstract. The efficiency of emergency departments (EDs) in handling patient surges during disaster times using the available resources is very important. Many EDs require additional resources to overcome the bottlenecks in emergency systems. The assumption is that EDs consider the option of temporary staff dispatching, among other options, in order to respond to an increased demand or even the hiring temporarily non-hospital medical staff. Discrete event simulation (DES), a well-known simulation method and based on the idea of process modeling, is used for establishing ED operations and management related models. In this study, a DES model is developed to investigate and analyze an ED under normal conditions and an ED in a disaster scenario which takes into consideration an increased influx of disaster victims-patients. This will allow early preparedness of emergency departments in terms of physical and human resources. The studied ED is located in an earthquake zone in Istanbul. The report on Istanbul's disaster preparedness presented by the Japan International Cooperation Agency (JICA) and Istanbul Metropolitan Municipality (IMM), asserts that the district where the ED is located is estimated to have the highest injury rate. Based on real case study information, the study aims to suggest a model on pre-planning of ED resources for disasters. The results indicate that in times of a possible disaster, when the percentage of red patient arrivals exceeds 20% of total patient arrivals, the number of red area nurses and the available space for red area patients will be insufficient for the department to operate effectively. A methodological improvement presented a different distribution function that was tested for service time of the treatment areas. The conclusion is that the Weibull distribution function used in service process of injection room fits the model better than the Gamma distribution function.

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Section: Model Results and Patient Surge Scenariomentioning

confidence: 99%

“…Morgareidge et al [22] integrated DES and space syntax analysis (SAA) to optimize patient flow and to design the premises of an ED. In another study, Kang et al [17] revealed the impact of various patient admission processes on patient flow in an ED. A miscellaneous example on ED simulation was presented by Kadri et al [16].…”

Section: Introductionmentioning

confidence: 99%

Simulation modelling of a patient surge in an emergency department under disaster conditions

Gül

Güneri

2015

Croat. Oper. Res. Rev.

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“…Most recently, Kang and colleagues employed discreet‐event simulation to model the effect of several admission process policies on patient flow in the ED. They identified four basic admission policy types (admitting team decision‐making, admitting attending decision‐making, ED team decision‐making, and ED attending decision‐making) as well as two hybrid models.…”

Section: Discussionmentioning

confidence: 99%

Sustainable Mechanism to Reduce Emergency Department (ED) Length of Stay: The Use of ED Holding (ED Transition) Orders to Reduce ED Length of Stay

Haydar

Strout

Baumann

2016

Academic Emergency Medicine

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Objective: The objective was to evaluate the effect of an emergency clinician-initiated "ED admission holding order set" on emergency department (ED) treatment times and length of stay (LOS). We further describe the impact of a performance improvement strategy with sequential plan-do-study-act (PDSA) cycles used to influence the primary outcome measures, ED LOS, and disposition decision to patient gone (DDTPG) time, for admitted patients. Methods:We developed and implemented an expedited, emergency physician-facilitated admission protocol that bypassed typical inpatient workflows requiring inpatient evaluations prior to the placement of admission orders. During the 48-month study period, ED flow metrics generated during the care of 27,580 admissions from the 24-month period prior to the intervention were compared to the 29,978 admissions that occurred during the 24-month period following the intervention. The intervention was the result of an in-depth, five-phase PDSA cycle quality improvement intervention evaluating ED flow, which identified the requirement of bedside inpatient evaluations prior admission order placement as being a "non-value-added" activity. ED output flow metrics evaluating the admission process were tracked for 24 months following the intervention and were compared to the 24 months prior.Results: The use of an emergency physician-initiated admission holding order protocol resulted in sustainable reductions in ED LOS when comparing the 2 years prior to the intervention, with median LOS of 410 (interquartile range [IQR] = 295 to 543) and 395 (IQR = 283 to 527) minutes, to the 2 calendar years following the intervention, with the median LOS of 313 (IQR = 21 to 431) and 316 (IQR = 224 to 438) minutes, respectively. This overall reduction in ED LOS of nearly 90 minutes was found to be primarily the result of a decrease in the time from the emergency physician's admitting DDTPG times with median times of 219 (IQR = 150 to 306) and 200 (IQR = 136 to 286) minutes for the 2 years prior to the intervention compared to 89 (IQR = 58 to 138) and 92 (IQR = 60 to 147) minutes for the 2 years following the intervention. It is notable that there was a modest increase in the door to disposition decision of admission times during this same study period with annual medians of 176 (IQR = 112 to 261) and 178 (IQR = 129 to 316) minutes, respectively, for the 2 years prior to 207 (IQR = 129 to 316) and 202 (IQR = 127 to 305) minutes following the intervention. Conclusions:We conclude that the use of emergency physician-initiated holding orders can lead to marked reductions in ED LOS for admitted patients. Continued improvement can be demonstrated with an effective performance improvement initiative designed to continuously optimize the process change.ACADEMIC EMERGENCY MEDICINE 2016;23:776-785 © 2016 by the Society for Academic Emergency Medicine E mergency department (ED) crowding has taken center stage with growing emphasis now being placed on system-wide approaches to addressing issues related to health...

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“…Excessive boarding is a common cause of ED crowding and important marker of overall hospital patient flow (Institute of Medicine 2006; Joint Commission 2012). Modeling the transition of patients from the ED to inpatient units, particularly the inpatient unit assignment decision, can be complex based on hospital size and clinical admission policies (Kang et al 2014). Figure 1 depicts the simple process modeled in the ED.…”

Section: 1mentioning

confidence: 99%

Simulating wait time in healthcare: Accounting for transition process variability using survival analyses

Levin¹,

Garifullin²

2015

2015 Winter Simulation Conference (WSC)

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Maxim GarifullinGE Healthcare 540 W Northwest Hwy, Barrington, IL 60010, USA Wait or queuing time is a principal performance measure for many discrete-event simulation (DES) models in healthcare. However, variation in wait time is often caused by both occupied downstream servers (e.g., beds) and organizational and human transition processes. DES models that attribute wait solely to occupied servers, ignoring transition process variability, face challenges in adequate baseline validation. Embedding regression models for survival data in DES to estimate patient wait times is a method capable of integrating the effects of transition processes with queuing. Developing these models as a sUb-component is further valuable in understanding the socio-technical system factors that drive pro longed waits. These general methods are exhibited in a DES for a large urban hospital with a primary output of wait time in the emergency department (ED) for transfer to an inpatient bed (boarding time). Simulated boarding time is compared before and after accounting for transition processes using survival analysis. 1 978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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Patient Flow in the Emergency Department: A Classification and Analysis of Admission Process Policies

Cited by 42 publications

References 24 publications

Simulation modelling of a patient surge in an emergency department under disaster conditions

Simulation modelling of a patient surge in an emergency department under disaster conditions

Sustainable Mechanism to Reduce Emergency Department (ED) Length of Stay: The Use of ED Holding (ED Transition) Orders to Reduce ED Length of Stay

Simulating wait time in healthcare: Accounting for transition process variability using survival analyses

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