The Impact of Inpatient Boarding on ED Efficiency: A Discrete-Event Simulation Study

Bair, Aaron E.; Song, Wheyming Tina; Chen, Yi‐Chun; Morris, Beth

doi:10.1007/s10916-009-9307-4

Cited by 75 publications

(38 citation statements)

References 38 publications

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“…Inpatient flow and bed management has crucial impacts on hospital operations (Hall 2012), especially on emergency department (ED) crowdedness (Litvak et al 2001, Howell et al 2008, Bair et al 2010, Wong et al 2010, Schneider et al 2001). …”

Section: Introductionmentioning

confidence: 99%

Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time

et al. 2016

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O ne key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient's service time in the inpatient wards depends on that patient's admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed.(2) Pre-and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage.(3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 a.m. and 9 a.m. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics.

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Section: Introductionmentioning

confidence: 99%

Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time

et al. 2016

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“…We transformed the NEDOCS scores into six levels using previously derived thresholds of severity 20. We were unable to perform an a priori power calculation for this study as we did not know how the NEDOCS would perform in England or whether collection was feasible.…”

Section: Methodsmentioning

confidence: 99%

Comparison of the International Crowding Measure in Emergency Departments (ICMED) and the National Emergency Department Overcrowding Score (NEDOCS) to measure emergency department crowding: pilot study

et al. 2016

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IntroductionThere is uncertainty about the best way to measure emergency department crowding. We have previously developed a consensus-based measure of crowding, the International Crowding Measure in Emergency Departments (ICMED). We aimed to obtain pilot data to evaluate the ability of a shortened form of the ICMED, the sICMED, to predict senior emergency department clinicians’ concerns about crowding and danger compared with a very well-studied measure of emergency department crowding, the National Emergency Department Overcrowding Score (NEDOCS).MethodsWe collected real-time observations of the sICMED and NEDOCS and compared these with clinicians’ perceptions of crowding and danger on a visual analogue scale. Data were collected in four emergency departments in the East of England. Associations were explored using simple regression, random intercept models and models accounting for correlation between adjacent time points.ResultsWe conducted 82 h of observation in 10 observation sets. Naive modelling suggested strong associations between sICMED and NEDOCS and clinician perceptions of crowding and danger. Further modelling showed that, due to clustering, the association between sICMED and danger persisted, but the association between these two measures and perception of crowding was no longer statistically significant.ConclusionsBoth sICMED and NEDOCS can be collected easily in a variety of English hospitals. Further studies are required but initial results suggest both scores may have potential use for assessing crowding variation at long timescales, but are less sensitive to hour-by-hour variation. Correlation in time is an important methodological consideration which, if ignored, may lead to erroneous conclusions. Future studies should account for such correlation in both design and analysis.

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“…This phenomenon is manifested by a lack of human and material resources (mainly admission beds) in front of an urgent demand for care [6]. This leads to the dissatisfaction of patients [7] [8] not finding the required care quality [9] [10] and a tired and overwhelmed staff [11], without neglecting the substantial financial losses [12] and the risk of mortality which is the most serious issue [13] [14] [15].…”

Section: Overcrowding Phenomenon In the Pediatric Emergency Departmentmentioning

confidence: 99%

A Multi-criteria Optimization Approach to Health Care Tasks Scheduling Under Resources Constraints

Othman¹,

Hammadi²

2017

IJCIS

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We are interested in this paper in studying and developing a decision support tool for multi-skill health care tasks scheduling in the Pediatric Emergency Department. We use an evolutionary algorithm and we propose the use of fuzzy logic to formulate an adapted fitness function. We consider the potential performance of the system and we set up a set of lower bounds characterizing criteria limits allowing to calculate the minimum waiting time for incoming patients and the corresponding latest ending time.

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The Impact of Inpatient Boarding on ED Efficiency: A Discrete-Event Simulation Study

Cited by 75 publications

References 38 publications

Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time

Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time

Comparison of the International Crowding Measure in Emergency Departments (ICMED) and the National Emergency Department Overcrowding Score (NEDOCS) to measure emergency department crowding: pilot study

A Multi-criteria Optimization Approach to Health Care Tasks Scheduling Under Resources Constraints

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