Discharge Length Of Stay Research Articles

Operations Factors Associated with Emergency Department Length of Stay: Analysis of a National Operations Database.

Prolonged emergency department (ED) length of stay (LOS) has been shown to adversely affect patient care. We sought to determine factors associated with ED LOS via analysis of a large, national, ED operations database. We performed retrospective, multivariable, linear regression modeling using the 2019 Emergency Department Benchmarking Alliance survey results to identify associated factors of ED LOS for admitted and discharged patients. A total of 1,052 general and adult-only EDs responded to the survey. Median annual volume was 40,946. The median admit and discharge LOS were 289 minutes and 147 minutes, respectively. R-squared values for the admit and discharge models were 0.63 and 0.56 with out-of-sample R-squared values of 0.54 and 0.59, respectively. Both admit and discharge LOS were associated with academic designation, trauma level designation, annual volume, proportion of ED arrivals occurring via emergency medical services, median boarding, and use of a fast track. Additionally, admit LOS was associated with transfer-out percentage, and discharge LOS was associated with percentage of high Current Procedural Terminology, percentage of patients <18 years old, use of radiographs and computed tomography, and use of an intake physician. Models derived from a large, nationally representative cohort identified diverse associated factors of ED length of stay, several of which were not previously reported. Dominant within the LOS modeling were patient population characteristics and other factors extrinsic to ED operations, including boarding of admitted patients, which was associated with both admitted and discharged LOS. The results of the modeling have significant implications for ED process improvement and appropriate benchmarking.

Machine learning–based triage to identify low-severity patients with a short discharge length of stay in emergency department

BackgroundOvercrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as “triage level 3” or “urgent” generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED.MethodsThis was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models—CatBoost, XGBoost, decision tree, random forest, and logistic regression—by using large ED visit data and examined their performance in internal and external validation.ResultsFor internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743–0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation.ConclusionsThis is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.

Are Split Flow and Provider in Triage Models in the Emergency Department Effective in Reducing Discharge Length of Stay?

A quality improvement (QI) project was completed early in 2015 to evaluate the split flow model of care delivery and a provider in triage model within a newly constructed emergency department. The QI project compared 2 emergency departments of similar volumes, one that splits the patient flow and employs a provider in triage model and the other that blends the patient flow and employs a traditional nurse triage model. A total of 68,603 patients were included in this project. The purpose of the split flow model is to create a second flow stream of patients through the emergency department, parallel to the regular acute/critical care flow stream, for patients with problems that are not complex. Specific patient outcomes that were evaluated for the purpose of this QI project were door to discharge or discharge length of stay (DLOS) for all ED patients. The provider in triage model enhances patient triage assessment, as well as patient flow within the emergency department, by allowing patients to be evaluated by an ED provider immediately at the point of triage when the patient first presents to the emergency department. The QI project demonstrated that the split flow model alone reduced DLOS for all ED patients, and when coupled with the provider in triage model, a greater reduction in DLOS, as well as an improvement in front-end throughput metrics, was realized.

Association Between Patient and Emergency Department Operational Characteristics and Patient Satisfaction Scores in a Pediatric Population.

Emergency departments (EDs) are seeing an increase in the importance of patient satisfaction scores, yet little is known about their association with patient and operational characteristics. This study aimed to identify patient and operational characteristics associated with patient satisfaction scores. This was a retrospective analysis of data from Press Ganey patient satisfaction surveys of pediatric patients (<18 years) and their families, discharged from the ED of a single, academic, pediatric ED from December 2009 to May 2013. A linear mixed-effects regression model was used to identify significant associations while taking the clustering within patients and physicians into account. Outcome variables included scores for overall experience (0-10), wait time to be seen by a provider (0-100), and likelihood to recommend (0-100). The ED characteristics considered included daily census, proportion of left without being seen, average length of stay (LOS), and total boarding hours, as well as time of day by shift, door-to-room time, and discharge LOS. Patient characteristics included patient age, sex, race, person completing survey, survey language, survey method (mailed or online), payer type, mode of arrival, distance to hospital, weekend or weekday visit, and difference of patient-reported LOS to actual LOS. Only statistically significant variables were included in the final model. A total of 810 pediatric surveys were included for analysis. The overall mean (SD) was 8.7 (2.0) for overall experience, 84.0 (23.5) for waiting time to be seen by a provider, and 90.1 (22.2) for likelihood to recommend. The score for overall experience was highly correlated with likelihood to recommend (r = 0.90) and less strongly correlated with score for waiting time (r = 0.58). In the final models, increased door-to-room time was associated with a significant decrease in scores for all 3 outcome variables. In addition, a difference between perceived and actual LOS (>2 hours) was significantly associated with lower scores in overall experience and likelihood to recommend, whereas surveys completed online had higher scores for waiting time to see a provider compared with mailed. Emergency departments looking to increase satisfaction scores should focus efforts on decreasing door-to-room times.