Emergency Department Crowding: Time for Interventions and Policy Evaluations

This paper summarises the consequences of emergency department crowding. It provides a comparison of the scales used to measure emergency department crowding. We discuss the multiple causes of crowding and present an up-to-date literature review of the interventions that reduce the adverse consequences of crowding. We consider interventions at the level of an individual hospital and a policy level

Improving Patient Flow & Reducing Emergency Department (ED) Crowding

Offers early lessons from RWJF's Urgent Matters Learning Network II, a six-hospital collaborative to assess the implementation of strategies for better patient flow and less crowding, develop standard performance measurements, and promote best practices

Optimizing Emergency Department Throughput Using Best Practices to Improve Patient Flow

Emergency Department (ED) crowding and bottle necks are the reality of hospitals across the country. Patients seeking care and needing inpatient beds via the emergency rooms are facing delays with attaining the right level of care. Orchestrating a patient through an ED admission requires a multidisciplinary effort to provide safe, effective and efficient care. This quality improvement project conducted in a tertiary acute care hospital focused on Centers for Medicare and Medicaid metrics to measure Emergency Department (ED) throughput. This multidisciplinary initiative focused on reducing time stamps for patient arrival to the ED through departure to hospital or home. Outcomes showed a significant decrease in the time frame for patient arrival to being seen by a qualified provider, left without being seen rates, ED diversion, and ancillary department turnaround times. The interventions can be applied at other hospital based emergency departments

Mitigating Emergency Department Crowding With Stochastic Population Models

Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has either focused on average behavior or used context-specific non-generalizable models. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding, using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) reduces severe overcrowding events by 50%. Such forecasting is crucial in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many natural systems.Comment: 21 pages, 6 figures + Supplementary informatio

Emergency Department Crowding: Factors Influencing Flow

Background: The objective of this study was to evaluate those factors, both intrinsic and extrinsic to the emergency department (ED) that influence two specific components of throughput: “door-to-doctor” time and dwell time.Methods: We used a prospective observational study design to determine the variables that played a significant role in determining ED flow. All adult patients seen or waiting to be seen in the ED were observed at 8pm (Monday-Friday) during a three-month period. Variables measured included daily ED volume, patient acuity, staffing, ED occupancy, daily admissions, ED boarder volume, hospital volume, and intensive care unit volume. Both log-rank tests and time-to-wait (survival) proportional-hazard regression models were fitted to determine which variables were most significant in predicting “door-to-doctor” and dwell times, with full account of the censoring for some patients.Results: We captured 1,543 patients during our study period, representing 27% of total daily volume. The ED operated at an average of 85% capacity (61-102%) with an average of 27% boarding. Median “door-to-doctor” time was 1.8 hours, with the biggest influence being triage category, day of the week, and ED occupancy. Median dwell time was 5.5 hours with similar variable influences.Conclusion: The largest contributors to decreased patient flow through the ED at our institution were triage category, ED occupancy, and day of the week. Although the statistically significant factors influencing patient throughput at our institution involve problems with inflow, an increase in ED occupancy could be due to substantial outflow obstruction and may indicate the necessity for increased capacity both within the ED and hospital. [West J Emerg Med. 2010; 11(1):10-15

Financial Impact of Emergency Department Crowding

Objective: The economic benefits of reducing emergency department (ED) crowding are potentially substantial as they may decrease hospital length of stay. Hospital administrators and public officials may therefore be motivated to implement crowding protocols. We sought to identify a potential cost of ED crowding by evaluating the contribution of excess ED length of stay (LOS) to overall hospital length of stay. Methods: We performed a retrospective review of administrative data of adult patients from two urban hospitals (one county and one university) in Brooklyn, New York from 2006-2007. Data was provided by each facility. Extrapolating from prior research (Krochmal and Riley, 2005), we determined the increase in total hospital LOS due to extended ED lengths of stay, and applied cost and charge analyses for the two separate facilities. Results: We determined that 6,205 (5.0%) admitted adult patients from the county facility and 3,017 (3.4%) patients from the university facility were held in the ED greater than one day over a one-year period. From prior research, it has been estimated that each of these patient’s total hospital length of stay was increased on average by 11.7% (0.61 days at the county facility, and 0.71 days at the university facility). The increased charges over one year at the county facility due to the extended ED LOS was therefore approximately $9.8 million, while the increased costs at the university facility were approximately$3.9 million. Conclusion: Based on extrapolations from Krochmal and Riley applied to two New York urban hospitals, the county hospital could potentially save $9.8 million in charges and the university hospital$3.9 million in costs per year if they eliminate ED boarding of adult admitted patients by improving movement to the inpatient setting. [West J Emerg Med. 2011;12(2):192-197.

Emergency department crowding and provider workload

This observational field study attempted to quantify the objective task load imposed on emergency department (ED) providers, determine the degree of subjective workload they experience, and to correlate these data with ED operational metrics, mainly ED crowding metrics. Participants were a convenience sample of 10 emergency care providers; the 3 female and 7 male participants represented a variety of provider levels (6 physicians, 3 physician assistants, and 1 nurse practitioner). Forty-two hours of data were collected. ED variables were obtained from the hospital\u27s existing information system each hour and included the Emergency Severity Index (ESI), number of people in the waiting room, patient/doctor ratio, patient/nurse ratio, number of patients assigned, number of providers on duty and crowding variables; Emergency Department Work Index (EDWIN) and occupancy level. Providers were shadowed and observed each hour by a researcher who recorded the type of tasks they performed, the number of tasks they performed, the time they spent on each task and the number of times they were interrupted. Subjective workload ratings (NASA-TLX) were obtained from providers at the end of each hour of observation. Correlations were performed to evaluate the relation of observed, subjective and hospital variables. Overall objective task load was quantified using time-on-task data and task difficulty weightings to achieve a single standardized value for overall objective workload (OTLX). OTLX scores were regressed against ED crowding measures of occupancy and EDWIN score. Structured interviews were conducted with each participant following the observation sessions. Results from the study revealed that providers spent 75 percent of their time performing tasks related to communication with staff, direct patient care, and paperwork. The other 25 percent of their time was spent checking test results, admitting patients to the hospital, taking breaks, looking for supplies, checking the electronic whiteboard, and other job-related tasks. ED occupancy was positively correlated to subjective workload and predicted 30 percent of the variance in subjective workload. The EDWIN score, on the other hand, only predicted 9 percent of the variance in subjective workload. This study revealed no correlation between ED crowding and objective task load and ED crowding predicted less than 4 percent of the variance in OTLX scores. In accordance with Occam\u27s razor , ED occupancy may provide an advantage over more complex compound measures of ED crowding such as the EDWIN score in predicting provider subjective workload and may be more useful in making ED staffing and scheduling decisions. In addition to collected and recorded variables, valuable insights were obtained from ED providers regarding issues of ED crowding, time-pressure and workload. It is apparent from their responses that, in the absence of observable changes in task load, the quantity and status of the unseen patient weighs heavily on their minds. Future research should assess the number of patients waiting or the number of patients who have left without being seen (LWBS) not only as a metric of ED crowding but as a predictor of ED provider workload

Implementation of a Code Lobby Surge and the Impact on Left Without Being Seen Rates

Practice Problem: Crowding of emergency departments contribute to higher-than-average left without being seen (LWBS) rates. LWBS patients pose risks to the hospital as well as to one’s own health. PICOT: The PICOT question that guided this project was in a pediatric emergency department (P), does implementation of a “Code Lobby Surge” (I), compared to standard care (C), decrease left without being seen rates (O) within eight weeks (T)? Evidence: Surge interventions and decreasing the visual of crowding have shown to decrease LWBS rates. Intervention: “Code Lobby Surge” was implemented to decrease LWBS rates and improve throughput within the pediatric emergency department. “Code Lobby Surge” is activated when the wait time for triage is over 30 minutes and the total number of patients pending triage exceeds 10 patients. Outcome: The intervention decreased LWBS rates by approximately four percent. Conclusion: “Code Lobby Surge” not only decreased LWBS rates, but also improved throughput of the emergency department. “Cody Lobby Surge” is an effective intervention to mitigate emergency department surges that contribute to LWBS rates