Predictors of Delayed Emergency Department Throughput Among Blunt Trauma Patients

Steren, Benjamin; Fleming, Matthew R; Zhou, Haoran; Zhang, Yawei; Pei, Kevin Y.

doi:10.1016/j.jss.2019.07.028

Cited by 4 publications

(6 citation statements)

References 58 publications

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“…Several factors affect LOS in ED patients, including organizational factors (such as shortage of beds leading to hospital transfer, radiological imaging, or sequential specialist consultations), as well as patient and hospital-specific factors (such as patient age, hospital teaching status, hospital size, and delayed ED throughput of trauma patients) [18][19]. Additionally, Salehi et al demonstrated that patients under isolation, under telemetry, older patients, and patients with a greater comorbidity burden had prolonged waits in the ED (either as prolonged ED LOS or prolonged boarding) and these prolonged boarding times were associated with greater inpatient LOS [20].…”

Section: Discussionmentioning

confidence: 99%

Emergency Department Boarding of Non-Trauma Patients Adversely Affects Trauma Patient Length of Stay

Hymel¹,

Leskovan²,

Thomas³

et al. 2020

Cureus

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Introduction Emergency Department (ED) boarding delays initiation of time-sensitive protocols for trauma patients and makes them susceptible to increased mortality and morbidity. In this study, we compared the ED boarding times of non-trauma patients and ED length of stay (LOS) of trauma patients. Methods This was a single-center retrospective cohort study in a Level 1 trauma center. The median boarding time among non-trauma patients and ED LOS among trauma patients was determined by month between the period of April 2018 to March 2019. Linear regression and Pearson correlation coefficient were used to express the magnitude and direction of the relationship between these two variables. Results During the study period, the mean number of non-trauma patients admitted in our ED per month was 1,154 and trauma patients was 89. The mean of the median boarding time per month for non-trauma patients was 76 minutes, and the mean of the median ED LOS per month for trauma patients was 198 minutes. There was a significant positive correlation between boarding time for non-trauma patients and ED LOS for trauma patients (Pearson correlation coefficient: 0.73; p = 0.007). Conclusion The long boarding times for non-trauma patients is associated with ED LOS for trauma patients, indicating that the total patient volume in the hospital contributes to the trauma patient's stay in the ED. Thus, ED LOS of trauma patients can be minimized by improving overall ED and hospital flow, including non-trauma patients.

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Section: Discussionmentioning

confidence: 99%

Emergency Department Boarding of Non-Trauma Patients Adversely Affects Trauma Patient Length of Stay

Hymel¹,

Leskovan²,

Thomas³

et al. 2020

Cureus

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“…The evaluation results of the model revealed its effectiveness; the model reduces the cost of ED and waiting time by 5%. However, the model does not efficiently address patient throughput time in terms of LoS and staff satisfaction to increase ED performance [40]. Huang et al [28] proposed a recent model in which chart review is used to measure LoS for trauma patients in ED.…”

Section: Related Workmentioning

confidence: 99%

“…Huang et al [28] proposed a recent model in which chart review is used to measure LoS for trauma patients in ED. The results of the model revealed the efficiency of the model with respect to supporting direct communication with trauma service by the ED provider and reservation of two temporary beds, resulting in reduced LoS for trauma patients [40]. However, this model does not consider the patient in different acuity case scales and does not address the decisionmaking factor, resulting in the inability to reduce waiting time and increased patient throughput time in ED [41].…”

Section: Related Workmentioning

confidence: 99%

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

2021

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Healthcare sectors face multiple threats, and the hospital emergency department (ED) is one of the most crucial hospital areas. ED plays a key role in promoting hospitals' goals of enhancing service efficiency. ED is a complex system due to the stochastic behavior of patient arrivals, the unpredictability of the care required by patients, and the department's complex nature. Simulations are effective tools for analyzing and optimizing complex ED operations. Although existing ED simulation models have substantially improved ED performance in terms of ensuring patient satisfaction and effective treatment services, many deficiencies continue to exist in addressing the key challenge in ED, namely, long patient throughput time. The patient throughput time issue is affected by causative factors, such as waiting time, length of stay, and decision-making. This research aims to develop a new simulation model of patient flow for ED (SIM-PFED) to address the reported key challenge of the patient throughput time. SIM-PFED introduces a new process for patient flow in ED on the basis of the newly proposed operational patient flow by combining discrete event simulation and agent-based simulation and applying a multi-attribute decisionmaking method, namely, the technique for order preference by similarity to the ideal solution. Experiments were performed on three actual hospital ED datasets to assess the effectiveness of SIM-PFED. Experimental results revealed the superiority of SIM-PFED over other alternative models in reducing patient throughput time in ED by consuming less patient waiting time and having a shorter length of stay. The findings also demonstrated the effectiveness of SIM-PFED in helping ED decision-makers select the best scenarios to be implemented in ED for ensuring minimal throughput time while being cost effective.

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“…Achieving optimal outcomes in trauma with limited resources has been accomplished by improving trauma systems, [1][2][3][4][5][6][7] understanding trauma epidemiology [8][9][10][11][12][13] and streamlining patient flow and logistics. [14][15][16][17][18][19] Resource allocation for trauma patients is difficult because injury severity, type-of-injury, and required resources are highly variable; the pattern of injury for two distinct patients experiencing the same mechanism (e.g., motor vehicle collision) may be vastly different from one another and therefore require a potentially broad set of resources. Understanding this variability is necessary to ensure an optimally functioning trauma system.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, predicting LOS and understanding factors related to length of stay in trauma patients is not new. 18,[39][40][41][42][43] However, no study to our knowledge has attempted to predict length of stay of trauma patients with only the parameters available in the trauma bay or while the patient is still in the ED. A prediction tool that could identify patients at high risk for prolonged length of stay at this critical time point would be useful clinically and provide an opportunity for early identification and intervention and could provide a paradigm for building a tool to assess patients at different time points to help with resource allocation.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay

et al. 2022

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Introduction Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS). Methods Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error. Results 2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79. Conclusion Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.

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Predictors of Delayed Emergency Department Throughput Among Blunt Trauma Patients

Cited by 4 publications

References 58 publications

Emergency Department Boarding of Non-Trauma Patients Adversely Affects Trauma Patient Length of Stay

Emergency Department Boarding of Non-Trauma Patients Adversely Affects Trauma Patient Length of Stay

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay

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