Emergency Department Crowding Research Articles

'See and Treat' Clinic Service Evaluation at a Tertiary Care Hospital in Abu Dhabi.

ObjectivesWe sought to evaluate the performance provided at a ‘See and Treat’ (ST) clinic at a tertiary hospital emergency department (ED) in Abu Dhabi, UAE, and to assess its impact on ED crowding.MethodsWe conducted a retrospective electronic medical chart review and database analyses. We included patients triaged as triage level 4 (T4) and triage level 5 (T5) as per the Emergency Severity Index treated at ED in the ST clinic and other ED areas, including the off-site Urgent Care Centre (UCC) between 1 June 2016 and 30 June 2017. We analyzed a group of process and outcome measures at our ST clinic and compared them to the same measures in other areas of our ED and the co-located UCC. The process measure analyzed was the door-to-doctor time. In addition, the outcome measures analyzed were the door-to-door time, unplanned return within 72 hours, and feedback from T4 and T5 triaged patients treated at the clinic.ResultsThe number of patients enrolled in the study was 43 109. Of these, 11 329 (26.3%) patients were treated at the ST clinic, 6328 (14.7%) were treated at the UCC, and 25 452 (59.0%) were treated at the main ED. The door-to-doctor time was within 30 minutes for 89.0% of ST clinic patients, and 94.0% of patients experienced a door-to-door time of within two hours; 2.1% of these patients returned within 72 hours. Among these, 78.7% returned for an issue related to their first visit. However, none of the patients were admitted on their return visit. For patients presenting to UCC and other parts of our ED, we recorded a door-to-doctor time of within 30 minutes for 80.5% of patients and a door-to-door time of within two hours for 73.0% of patients. We found the difference in waiting times (i.e., door-to-doctor times between ST clinic patients and the rest of ED) to be statistically significant (p < 0.001, 95% confidence interval (CI): 0.56–0.63). However, on comparing door-to-door times, we found the difference between ST clinic patients and the rest of ED patients was not statistically significant.ConclusionsDoor-to-doctor times were shorter in ST clinics compared to other parts of our ED, but there was no statistically significant difference in door-to-door times when comparing ST clinics to the rest of the ED. ST clinic patients had a lower rate of unplanned return within 72 hours, of which, none required admission on the return attendance. We believe ST clinics have a positive impact on reducing ED crowding but acknowledge they are one of the many plausible solutions attributing to optimized patient flow in the ED.

Role of Point-of-Care Testing in Reducing Time to Treatment Decision-Making in Urgency Patients: A Randomized Controlled Trial.

IntroductionShortening emergency department (ED) visit time can reduce ED crowding, morbidity and mortality, and improve patient satisfaction. Point-of-care testing (POCT) has the potential to decrease laboratory turnaround time, possibly leading to shorter time to decision-making and ED length of stay (LOS). We aimed to determine whether the implementation of POCT could reduce time to decision-making and ED LOS.MethodsWe conducted a randomized control trial at the Urgency Room of Siriraj Hospital in Bangkok, Thailand. Patients triaged as level 3 or 4 were randomized to either the POCT or central laboratory testing (CLT) group. Primary outcomes were time to decision-making and ED LOS, which we compared using Mann-Whitney-Wilcoxon test.ResultsWe enrolled a total of 248 patients: 124 in the POCT and 124 in the CLT group. The median time from arrival to decision was significantly shorter in the POCT group (106.5 minutes (interquartile [IQR] 78.3–140) vs 204.5 minutes (IQR 165–244), p <0.001). The median ED LOS of the POCT group was also shorter (240 minutes (IQR 161.3–410) vs 395.5 minutes (IQR 278.5–641.3), p <0.001).ConclusionUsing a point-of-care testing system could decrease time to decision-making and ED LOS, which could in turn reduce ED crowding.

Impact of Viral Seasonal Outbreaks on Crowding and Health Care Quality in Pediatric Emergency Departments.

In pediatric emergency departments (PEDs), seasonal viral outbreaks are believed to be associated with an increase of workload, but no quantification of this impact has been published. A retrospective cross-sectional study aimed to measure this impact on crowding and health care quality in PED. The study was performed in 1 PED for 3 years. Visits related to bronchiolitis, influenza, and gastroenteritis were defined using discharge diagnoses. The daily epidemic load (DEL) was the proportion of visits related to one of these diagnoses. The daily mean of 8 crowding indicators (selected in a published Delphi study) was used. A total of 93,976 children were admitted (bronchiolitis, 2253; influenza, 1277; gastroenteritis, 7678). The mean DEL was 10.4% (maximum, 33.6%). The correlation between the DEL and each indicator was significant. The correlation was stronger for bronchiolitis (Pearson R from 0.171 for number of hospitalization to 0.358 for length of stay). Between the first and fourth quartiles of the DEL, a significant increase, between 50% (patients left without being seen) and 8% (patient physician ratio), of all the indicators was observed. In conclusion, seasonal viral outbreaks have a strong impact on crowding and quality of care. The evolution of "patients left without being seen" between the first and fourth quartiles of DEL could be used as an indicator reflecting the capacity of adaptation of an emergency department to outbreaks.

Real-Time Measurement of Crowding in Pediatric Emergency Department: Derivation and Validation Using Consensual Perception of Crowding (SOTU-PED).

Our study aimed to develop and validate a real-time crowding composite scale for pediatric emergency department (PED). The study took place in one teaching PED for 2 months. The outcome was the perception of crowding evaluated by triage nurses and pediatricians on a 10-level Likert scale. Triage nurses evaluated crowding at each moment of a child's admission and pediatrician at each moment of a child's discharge. The outcome was the hourly mean of all evaluations of crowding (hourly crowding perception). For analysis, originally, we only selected hours during which more than 2 nurses and more than 2 pediatricians evaluated crowding and, moreover, during which evaluations were the most consensual. As predictors, we used hourly means of 10 objective crowding indicators previously selected as consensual in a published French national Delphi study and collected automatically in our software system. The model (SOTU-PED) was developed over a 1-month data set using a backward multivariable linear regression model. Then, we applied the SOTU-PED model on a 1-month validation data set. During the study period, 7341 children were admitted in the PED. The outcome was available for 1352/1392 hours, among which 639 were included in the analysis as "consensual hours." Five indicators were included in the final model, the SOTU-PED (R2 = 0.718). On the validation data set, the correlation between the outcome (perception of crowding) and the SOTU-PED was 0.824. To predict crowded hours (hourly crowding perception >5), the area under the curve was 0.957 (0.933-0.980). The positive and negative likelihood ratios were 8.16 (3.82-17.43) and 0.153 (0.111-0.223), respectively. Using a simple model, it is possible to estimate in real time how crowded a PED is.

The Inpatient Discharge Lounge as a Potential Mechanism to Mitigate Emergency Department Boarding and Crowding

Delayed access to inpatient beds for admitted patients contributes significantly to emergency department (ED) boarding and crowding, which have been associated with deleterious patient safety effects. To expedite inpatient bed availability, some hospitals have implemented discharge lounges, allowing discharged patients to depart their inpatient rooms while awaiting completion of the discharge process or transportation. This conceptual article synthesizes the evidence related to discharge lounge implementation practices and outcomes. Using a conceptual synthesis approach, we reviewed the medical and gray literature related to discharge lounges by querying PubMed, Google Scholar, and Google and undertaking backward reference searching. We screened for articles either providing detailed accounts of discharge lounge implementations or offering conceptual analysis on the subject. Most of the evidence we identified was in the gray literature, with only 3 peer-reviewed articles focusing on discharge lounge implementations. Articles generally encompassed single-site descriptive case studies or expert opinions. Significant heterogeneity exists in discharge lounge objectives, features, and apparent influence on patient flow. Although common barriers to discharge lounge performance have been documented, including underuse and care team objections, limited generalizable solutions are offered. Overall, discharge lounges are widely endorsed as a mechanism to accelerate access to inpatient beds, yet the limited available evidence indicates wide variation in design and performance. Further rigorous investigation is required to identify the circumstances under which discharge lounges should be deployed, and how discharge lounges should be designed to maximize their effect on hospitalwide patient flow, ED boarding and crowding, and other targeted outcomes.

Arcadia Medical Center: Emergency Department Crowding (A)

Arcadia Medical Center's newly appointed medical director of operations for the department of emergency medicine has noticed a gradual increase in the length of time it takes for patients to be seen and their care completed. He frequently examines patients who are waiting in their stretchers in hallways in an effort to expedite patient care. It is becoming increasingly difficult to maneuver through the overcrowded emergency department to perform routine patient care activities. Having considered how to improve the crowding issue, the medical director of operations has created a team to analyze the situation and make recommendations to implement change. Excerpt UVA-OM-1468 Rev. Oct. 21, 2019 Arcadia Medical Center: Emergency Department Crowding (A) Early for the meeting, Dr. Marc Schuler sat in a conference room at Arcadia Medical Center (AMC) and stared at a thick file containing his recent notes about the current state of the emergency department (ED). Schuler was AMC's newly appointed medical director of operations for the department of emergency medicine. During the 10 years he had been working at AMC, Schuler had noticed a gradual increase in the length of time it took to see patients and complete their care. He frequently examined patients waiting in their stretchers in hallways in an effort to expedite patient care. The overcrowded ED at AMC looked more like an early–20th century infirmary than a state-of-the-art ED. It was becoming increasingly difficult to maneuver through the ED to perform routine patient-care activities. This was not how he had envisioned practicing medicine in 2008. After having considered how to improve the crowding issue, Schuler had decided his first task was to create a team. Given the complex nature of implementing change in the department, he would need the help of the nursing staff and the support of the hospital administration and the academic chair of the emergency medicine department. Cindy Locker, the ED nurse manager, had observed her nursing staff becoming increasingly stressed and overwhelmed by the patient volume, the lack of communication in the ED, and the amount of time patients waited to receive care. When Schuler had asked Locker to lead the team, she had agreed. Schuler and Locker had already selected five people for the ED crowding team. Schuler still needed to pick another physician, and Locker had to choose another nurse. They had also contacted the quality improvement (QI) office to ask for a staff member to assist in the technical aspects of the data analysis. . . .

Emergency department crowding: why do patients walk-in?

ABSTRACT Objectives: For years, general practitioners (GP) shortage and patients’ increasing demand for acute care have been associated with Emergency Department (ED) crowding. Indeed, EDs admissions for non-emergency care seem to constantly increase. Surprisingly, the rationale for patients own decision to directly reach EDs over primary care have been poorly investigated to date. Methods: We conducted a study on patients admitted in two University EDs during nine consecutive days. Patients were asked to answer a survey about their frames for coming and if they were self-referred, referred by a GP, a specialist or after calling the Emergency Number. Results: During the study period, 68.0% of patients were self-referred, 17.0% referred by their GP, 8.5% by a specialist and 7% after an emergency call. 51.0% of the self-referrals thought EDs were the appropriate location to deal with their health problem and 24.0% because of a better accessibility. We noticed that 15.0% of the incomings looked for specialized care and 4.22% reported that the stress had motivated them. Of note, 4.6% of the patients were attracted by the hospital reputation. Financial concerns represented less than 1.0% of the motives invocated. Conclusion: We found that patients’ self-perceived severity of illness is the predominant frame to each the ED when they face needs for acute care. EDs’ accessibility as compared with other facilities also seems to encourage patients to come to the ED. Other factors such as the hospital reputation or patients’ stress tend to influence ED attendance but to a much lesser extent.

Swamped: Emergency Department Crowding and Patient Mortality

U.S. emergency departments are experiencing extreme levels of crowding. This study estimates the impact of emergency department crowding on patient mortality. Identification relies on the abrupt crowding shocks felt by “old” emergency departments at the time a new emergency department opens nearby. Using death records linked to hospital administrative records, I find that a 10% alleviation of emergency department patient volume significantly lowers the average patient’s chance of mortality. Improvements appear to be realized both inside the hospital and after the patient has left.

Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use

Problem Definition: We study the effects of emergency department (ED) physician workload on healthcare system utilization after the patient leaves the ED. Further, we explore the mediation effects of care intensity in the ED on post-ED care use. Relevance: ED crowding has been a persistent concern in the U.S. healthcare system. As such, many researchers have studied its effects on outcomes within the ED. We present novel results regarding the impacts of ED crowding on system performance outside the ED, specifically post-ED care utilization. Methodology: We utilize a dataset assembled from more than four years of microdata from a large U.S. hospital and exhaustive billing data in an integrated health system. We use count models and instrumental variable analyses to answer the proposed research questions. Results: We show that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its 5th percentile to the median, the number of post-discharge care events (i.e., medical services) for patients who are discharged home from the ED increases by 5%. Further, we identify physicians’ test ordering behavior as a mechanism for this effect: when the queue length is high, physicians respond by ordering more tests for less severe patients in the queue. We document that this “extra” testing generates “extra” post-ED care utilization for these patients. Managerial Implications: This paper contributes new insights on how physicians’ and patients’ behavior under ED crowding impacts a previously unstudied system performance measure: post-ED care utilization. Our findings suggest that prior studies estimating the cost of ED crowding provide an underestimate of the true effect, as they do not consider the “extra” post-ED care utilization.

Patient Flow Within Hospitals: A Conceptual Model.

Optimal patient flow minimizes waiting and is associated with quality healthcare. Emergency Department crowding is an indicator of poor patient flow and has been the focus of patient flow interventions. These interventions have failed to address interactions among components of the hospital. This has led to an incomplete understanding of why poor patient flow occurs and what is the best strategy for improving patient flow. The purpose of this article is to review the literature on the importance of good patient flow within hospitals and present a conceptual model of patient flow to guide research in this area.

Validation of the short form of the International Crowding Measure in Emergency Departments: an international study

There is little consensus on the best way to measure emergency department (ED) crowding. We have previously developed a consensus-based measure, the International Crowding Measure in Emergency Departments. We aimed to externally validate a short form of the International Crowding Measure in Emergency Department (sICMED) against emergency physician's perceptions of crowding and danger. We performed an observational validation study in seven EDs in five different countries. We recorded sICMED observations and the most senior available emergency physician's perceptions of crowding and danger at the same time. We performed a times series regression model. A total of 397 measurements were analysed. The sICMED showed moderate positive correlations with emergency physician's perceptions of crowding, r = 0.4110, P < 0.05) and safety (r = 0.4566, P < 0.05). There was considerable variation in the performance of the sICMED between different EDs. The sICMED was only slightly better than measuring occupancy or ED boarding time. The sICMED has moderate face validity at predicting clinician's concerns about crowding and safety, but the strength of this validity varies between different EDs and different countries.

The impact of delayed transfers of care on emergency departments: common sense arguments, evidence and confounding

ObjectivesThere have been claims that Delayed Transfers of Care (DTOCs) of inpatients to home or a less acute setting are related to Emergency Department (ED) crowding. In particular DTOCs were...

Effectiveness of interventions to alleviate emergency department crowding by older adults: a systematic review

BackgroundThe growing demand for elderly care often exceeds the ability of emergency department (ED) services to provide quality of care within reasonable time. The purpose of this systematic review is to assess the effectiveness of interventions on reducing ED crowding by older patients, and to identify core characteristics shared by successful interventions.MethodsSix major biomedical databases were searched for (quasi)experimental studies published between January 1990 and March 2017 and assessing the effect of interventions for older patients on ED crowding related outcomes. Two independent reviewers screened and selected studies, assessed risk of bias and extracted data into a standardized form. Data were synthesized around the study setting, design, quality, intervention content, type of outcome and observed effects.ResultsOf the 16 included studies, eight (50%) were randomized controlled trials (RCTs), two (13%) were non-RCTs and six (34%) were controlled before-after (CBA) studies. Thirteen studies (81%) evaluated effects on ED revisits and four studies (25%) evaluated effects on ED throughput time. Thirteen studies (81%) described multicomponent interventions. The rapid assessment and streaming of care for older adults based on time-efficiency goals by dedicated staff in a specific ED unit lead to a statistically significant decrease of ED length of stay (LOS). An ED-based consultant geriatrician showed significant time reduction between patient admission and geriatric review compared to an in-reaching geriatrician.ConclusionInter-study heterogeneity and poor methodological quality hinder drawing firm conclusions on the intervention’s effectiveness in reducing ED crowding by older adults. More evidence-based research is needed using uniform and valid effect measures.Trial registrationThe protocol is registered with the PROSPERO International register of systematic reviews: ID = CRD42017075575).

Abstract 290: Association Between Hourly Call Volume in Emergency Medical Dispatch Center and Dispatcher -Assisted Cardiopulmonary Resuscitation Instruction Time in Out-Of-Hospital Cardiac Arrest

Objectives: cardiac arrest recognition, ambulance dispatch and dispatcher assisted CPR by emergency medical dispatch (EMD) is crucial for good outcome of out-of-hospital cardiac arrest (OHCA).In EMD, crowding is caused by mismatch between numerous emergency calls and dispatchers per duty available Crowding in emergency department has been shown to decrease the performance and outcome, however little is known about effect of crowding in EMD. We aimed to evaluate incidence of crowding in emergency medical dispatch center and effect of emergency call crowding on dispatcher assisted CPR instruction performance in OHCA calls. Methods: We used nationwide OHCA database from 2013 to 2016, consisted of patients with presumed cardiac origin and dispatched by Seoul EMD. Main exposure was hourly number of total incoming emergency calls to EMD. Number of hourly calls were categorized into quartile (~4o calls, 41~51 calls, 52 ~61 calls and ~62 calls). Primary outcome was successful DA-CPR instruction provision within 120 seconds. Adjusted ratios (AORs) and 95% confidence intervals (CIs) were estimated to evaluate association between EMD crowding and outcome in multivariable logistic regression model. Results: Of a total of 12,722 patients, proportion of successful DA instruction were highest in least crowded quartile and lowest in the most crowded quartile (22.7% vs. 15.o% , p&lt;0.01). The adjusted odds ratios is 0.85 (95% CI 0.74 - 0.98) in most crowded EMD quartile with lesser proportion of DA instruction within 120 seconds. Crowding quartile 4 and quartile 3 was associated lesser favorable neurological outcome in multivariable logistic regression model.(adjusted OR(95% CI) 0.78 (0.60-0.99) and 0.70 (0.54- 0.91) respectively) . Conclusion: Crowding in emergency medicine dispatch caused by increased hourly call volume was associated with delayed dispatcher assisted CPR instruction provision. Medical directors might consider strategy approach to solve crowding in EMD according to crowding distribution.

Modeling emergency department nursing workload in real time: An exploratory study

Study of emergency department (ED) nursing workload has been largely subsumed under the related but separate phenomenon of ED crowding. Nursing workload is difficult to quantify directly. This observational study explored modeling ED nursing workload indirectly, in real time, from quantitative data available from the patient tracking computer system (PTCS). MethodsData on 2793 patient visits plus departmental statistics were collected during 167 60-minute survey periods (SP) in a 25-bed hospital ED in the United States. The charge nurse assessed a perceived workload score (WLS) according to pre-determined criteria following each SP as a validation measure. Data analysisCorrelations were calculated between the data and WLS, and strongly correlating variables were incorporated into linear regression models that sought to approximate WLS. ResultsA measure of aggregate patient acuity derived from the Emergency Severity Index (ESI) was the strongest predictor of WLS (r = 0.7991). The best-performing model agreed with WLS in 64% of SPs. ConclusionsGood agreement between model output and WLS suggests that ED nursing workload can be estimated indirectly in real time using data from a PTCS. Strong correlation between the ESI derivative and WLS further validates ESI and suggests a new application for the ESI score.

The effect of crowding in emergency department on precheck triage

Objective To explore the influence of crowding in emergency department on precheck triage. Methods Data of 126 078 patients admitted to the emergency department of the hospital from January 2017 to June 2017 were retrospectively analyzed. According to the occupancy rate of emergency beds, the patients were divided into crowded group and non-crowded group.The epidemiological data, number of patients waiting for triage, waiting time for triage, length of stay(LOS), delayed triage rate, missed triage rate, missed triage rate of critically ill patients, over triage rate of critically ill patients, disease level, years of service in triage of nurses and the flow of triage were compared between the two groups. Linear regression and Logstic regression were used to analyze patient wait triage, LOS, and factors influencing patient diversion to the clinic. Results The delayed rate of triage, missed rate and missed rate of critical patients in the crowded group were significantly higher than those in the non-crowded group, with statistically significant differences(P<0.01). There was no statistically significant difference in triage accuracy and overtest rate among critically ill patients. Waiting time for triage was significantly correlated with the number of waiting patients and the length of service of triage nurses (P<0.01). LOS was significantly positively correlated with the number of patients waiting for triage (P<0.001), and was negatively correlated with the triage nurse triage work year limit (P<0.001). They have nothing to do with the patient's age, disease level and gender.The working years of the triage nurse and the patients in the low age group were the influencing factors for the diversion to the outpatient service (P<0.001). The number of patients waiting for triage and gender were not the influencing factors. Conclusions Crowding in the emergency department causes many adverse effects on pre-diagnosis triage, leading to the extension of waiting time for triage and LOS, increasing the rate of missed tests in critically ill patients, which is not conducive to patient safety and brings hidden dangers to medical care. Therefore, it should be attached great importance to improve its negative effect on triage. Key words: Emergency department crowding; Pre-diagnosis triage; Missed rate; Triage accuracy

Improving the Efficiency of an Emergency Department Based on Activity-Relationship Diagram and Radio Frequency Identification Technology.

Emergency department crowding has been one of the main issues in the health system in Taiwan. Previous studies have usually targeted the process improvement of patient treatment flow due to the difficulty of collecting Emergency Department (ED) staff data. In this study, we have proposed a hybrid model with Discrete Event Simulation, radio frequency identification applications, and activity-relationship diagrams to simulate the nurse movement flows and identify the relationship between different treatment sections. We used the results to formulate four facility layouts. Through comparing four scenarios, the simulation results indicated that 2.2 km of traveling distance or 140 min of traveling time reduction per nurse could be achieved from the best scenario.

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example.

Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

118 Emergency Department Crowding is Not Associated With Delays in Acute Stroke Care

Delays in identification and treatment of acute stroke contribute to significant morbidity and mortality. Multiple clinical factors have been associated with delays in acute stroke care. We aimed to determine the relationship between emergency department (ED) crowding and the delivery of timely emergency stroke care. We used prospectively collected data from our institutional Get with the Guidelines-Stroke registry to identify consecutive acute ischemic stroke patients presenting to our large, urban academic ED from July 2016 to August 2018. Patients included in analysis were those who presented within six hours of last known well time. We used ED capacity logs to determine the degree of ED crowding at the time of patients’ presentation (normal, high, and severe capacity constraints). Outcomes of interest were door-to-imaging time (DIT), door-to-needle time (DTN), and door-to-groin puncture times (DGPT). Bivariate comparisons were made using t-tests, chi-square, and non-parametric testing as appropriate. We used wilcoxon rank sum testing to examine the bivariate relationship between ED capacity and our outcomes of interest, and regression models to examine the relationship after accounting for patient demographics, arrival mode, whether the patient was transferred from another hospital, and stroke severity. Of the 1,378 patients who presented with ischemic strokes during the study period, 1,080 (78%) presented during times of normal ED utilization, 203 (15%) during high ED utilization, and 94 (6.8%) during severe capacity constraints. Among patients presenting acutely, median DIT was 51.5 minutes (interquartile range [IQR] 23-130), median DTN time was 43 minutes (IQR 31-59), and median DGPT was 72 mins (IQR 57-117.5). Overall DIT for patients who received alteplase was 18 mins (IQR 14-26). Treatment times were not significantly different during periods of higher ED utilization in bivariate or in multivariable testing. In our single institution analysis, we found no significant delays in stroke care delivery associated with increased ED crowding. This finding suggests that robust processes of care may enable continued high-quality acute care delivery, even during times with an increased capacity burden.

144 Time Series Analysis and Forecasting Daily Emergency Department Visits Utilizing Facebook's Prophet Method

Emergency department (ED) boarding and crowding have significant implications for patient care. The capability of being able to model ED volumes to optimize staff and hospital resource allocation may lead to significant positive operational and patient care outcomes. The number of daily visits in 24 hours can often be challenging to predict, but over the last few decades, several studies have attempted to develop and deploy several forecasting models including exponential smoothing, and autoregressive integrated moving average models with varying levels of success. The analysis and identification of patterns and trends in large scale data are vital in other industries, such as social media, where several innovative methodologies have been employed to assess pattern recognition and trends. Facebook® uses a novel Bayesian forecasting model known as Prophet to decompose time series factors identifying patterns. This study leveraged this innovative methodology to identify patterns of ED daily visits with the hope of predicting future number of daily ED visits. Data were collected from a single urban community-based hospital in the New York Metropolitan area from January 2016 to December 2018. A time series analysis and decomposition were performed on daily visits using Prophet, which is an open source forecasting time series model based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality as well as holiday effects. The model trends were revealed and used for predicting daily ED visits for the first two weeks of January 2019. The Facebook Prophet method identified two distinct seasonal patterns. The resulting dataframe contained fourteen days of forecasted data with corresponding 95% confidence intervals. The model appears to provide insight into the general trend and seasonality in the time series data. Not surprisingly, the busiest day of the week is a Monday and the busiest time of the year is during the late winter season, such as February and March. A quick comparison with the actual visits from the first two weeks in 2019 shows that its predictions are relatively reasonable. The result of our study suggests that the Facebook Prophet model can be used to provide reasonably accurate forecasts and predictions in daily ED visits. However, the limitation was that the data was from a single site with only three years of data. Building on this single site study, future multi-site prospective longitudinal work can assess if such a methodological approach can provide forecasting information useful for departmental administration as well as validation in other hospital sites.View Large Image Figure ViewerDownload Hi-res image Download (PPT)