Patient Arrival In Emergency Department Research Articles

Sociodemographic Disparities in Queue Jumping for Emergency Department Care

Emergency department (ED) triage models are intended to queue patients for treatment. In the absence of higher acuity, patients of the same acuity should room in order of arrival. To characterize disparities in ED care access as unexplained queue jumps (UQJ), or instances in which acuity and first come, first served principles are violated. Retrospective, cross-sectional study between July 2017 and February 2020. Participants were all ED patient arrivals at 2 EDs within a large Northeast health system. Data were analyzed from July to September 2022. UQJ was defined as a patient being placed in a treatment space ahead of a patient of higher acuity or of a same acuity patient who arrived earlier. Primary outcomes were odds of a UQJ and association with ED outcomes of hallway placement, leaving before treatment complete, escalation to higher level of care while awaiting inpatient bed placement, and 72-hour ED revisitation. Secondary analysis examined UQJs among high acuity ED arrivals. Regression models (zero-inflated Poisson and logistic regression) adjusted for patient demographics and ED operational variables at time of triage. Of 314 763 included study visits, 170 391 (54.1%) were female, the mean (SD) age was 50.46 (20.5) years, 132 813 (42.2%) patients were non-Hispanic White, 106 401 (33.8%) were non-Hispanic Black, and 66 465 (21.1%) were Hispanic or Latino. Overall, 90 698 (28.8%) patients experienced a queue jump, and 78 127 (24.8%) and 44 551 (14.2%) patients were passed over by a patient of the same acuity or lower acuity, respectively. A total of 52 959 (16.8%) and 23 897 (7.6%) patients received care ahead of a patient of the same acuity or higher acuity, respectively. Patient demographics including Medicaid insurance (incident rate ratio [IRR], 1.11; 95% CI, 1.07-1.14), Black non-Hispanic race (IRR, 1.05; 95% CI, 1.03-1.07), Hispanic or Latino ethnicity (IRR, 1.05; 95% CI, 1.02-1.08), and Spanish as primary language (IRR, 1.06; 95% CI, 1.02-1.10) were independent social factors associated with being passed over. The odds of a patient receiving care ahead of others were lower for ED visits by Medicare insured (odds ratio [OR], 0.92; 95% CI, 0.88-0.96), Medicaid insured (OR, 0.81; 95% CI, 0.77-0.85), Black non-Hispanic (OR, 0.94; 95% CI, 0.91-0.97), and Hispanic or Latino ethnicity (OR, 0.87; 95% CI, 0.83-0.91). Patients who were passed over by someone of the same triage severity level had higher odds of hallway bed placement (OR, 1.01; 95% CI, 1.00-1.02) and leaving before disposition (OR, 1.02; 95% CI, 1.01-1.04). In this cross-sectional study of ED patients in triage, there were consistent disparities among marginalized populations being more likely to experience a UQJ, hallway placement, and leaving without receiving treatment despite being assigned the same triage acuity as others. EDs should seek to standardize triage processes to mitigate conscious and unconscious biases that may be associated with timely access to emergency care.

627 Strategies for Reducing Emergency Department Lengths of Stay for Admitted Pediatric Burn Patients

Abstract Introduction Various strategies to reduce emergency department (ED) lengths of stay (LOS) for admitted pediatric burn patients may be employed as a quality improvement project. Decreasing ED LOS may promote patient outcomes and reduce morbidity. Initial discussions were brought forth during trauma and burn multidisciplinary peer review rounds in March 2019 and have persisted to present day. Methods Several strategies, such as preparation of the burn unit staff within one hour of patient arrival in ED, notification to the burn unit by the burn attending of an incoming pediatric burn patient, allowing the PICU charge nurses or advisors to assist with room set up and admissions, and creating a checklist to assist PICU nurses and advisors in helping prepare for anticipating inpatient admissions. These strategies were designed and enforced in March/April 2019. In addition to these action plans, trauma activation alerts were added in December 2019 to the burn charge nurse phone for pediatric burn trauma one and trauma alerts for more expedient notifications. Finally, communication efforts between ED and burn leadership teams were conducted in June 2020 to help with additional mitigating of ED LOS, such as discussing the appropriateness of specialty consults while in the ED. Results Initial ED LOS was reduced from 209 minutes in March 1019 to 150 minutes in June 2019. Increased trends were noted in early 2020, with a peak at 244 minutes in July 2020. Additional interventions, such as trauma activation alerts and ED/Burn team communications, did not provide sustainable long-term reductions. Conclusions Recent strategies to reduce overall ED LOS trends have been beneficial, but not consistent, in sustaining downward trends. Action to perform a gap analysis to discover persistent barriers and to introduce additional structure, such as a burn trauma one algorithm, may provide stability to this metric.

A multi-method patient arrival forecasting outline for hospital emergency departments

ABSTRACT Patient arrivals at the emergency department (ED) of hospitals has an unpredictable behavior. So that, adequate forecasting of this process can serve a management baseline to better allocate ED human resources and medical equipment. In this paper, a multi-method patient arrival forecasting outline for EDs is developed. The methods followed within this study include single methods as linear regression (LR), autoregressive integrated moving average (ARIMA), artificial neural network (ANN), exponential smoothing and hybrid methods as ARIMA–ANN and ARIMA-LR. As the subject of the study, a private hospital ED case in Turkey is carried out. Data of ED patient arrivals for the year of 2016 was used to set up models. Forecasting performance of the multi-method outline was measured using mean absolute percentage error. The ARIMA–ANN hybrid model is shown to outperform in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying these methods to model ED patient arrivals and making an overall assessment among them. The results can be used to aid in strategic decision-making on ED staffing and scheduling policy planning in response to predictable arrival variations.

An Analysis of the Relationship Between the Heat Index and Arrivals in the Emergency Department.

Background: Heatwaves are one of the most deadly weather-related events in the United States and account for more deaths annually than hurricanes, tornadoes, floods, and earthquakes combined. However, there are few statistically rigorous studies of the effect of heatwaves on emergency department (ED) arrivals. A better understanding of this relationship can help hospitals plan better and provide better care for patients during these types of events.Methods: A retrospective review of all ED patient arrivals that occurred from April 15 through August 15 for the years 2008 through 2013 was performed. Daily patient arrival data were combined with weather data (temperature and humidity) to examine the potential relationships between the heat index and ED arrivals as well as the length of time patients spend in the ED using generalized additive models. In particular the effect the 2012 heat wave that swept across the United States, and which was hypothesized to increase arrivals was examined.Results: While there was no relationship found between the heat index and arrivals on a single day, a non-linear relationship was found between the mean three-day heat index and the number of daily arrivals. As the mean three-day heat index initially increased, the number of arrivals significantly declined. However, as the heat index continued to increase, the number of arrivals increased. It was estimated that there was approximately a 2% increase in arrivals when the mean heat index for three days approached 100°F. This relationship was strongest for adults aged 18-64, as well as for patients arriving with lower acuity. Additionally, a positive relationship was noted between the mean three-day heat index and the length of stay (LOS) for patients in the ED, but no relationship was found for the time from which a patient was first seen to when a disposition decision was made. No significant relationship was found for the effect of the 2012 heat wave on ED arrivals, though it did have an effect on patient LOS.Conclusion: A single hot day has only a limited effect on ED arrivals, but continued hot weather has a cumulative effect. When the heat index is high (~90°F) for a number of days in a row, this curtails peoples activities, but if the heat index is very hot (~100°F) this likely results in an exacerbation of underlying conditions as well as heat-related events that drives an increase in ED arrivals. Periods of high heat also affects the length of stay of patients either by complicating care or by making it more difficult to discharge patients.

An Emergency Department Patient Flow Model Based on Queueing Theory Principles

The objective was to derive and validate a novel queuing theory-based model that predicts the effect of various patient crowding scenarios on patient left without being seen (LWBS) rates. Retrospective data were collected from all patient presentations to triage at an urban, academic, adult-only emergency department (ED) with 87,705 visits in calendar year 2008. Data from specific time windows during the day were divided into derivation and validation sets based on odd or even days. Patient records with incomplete time data were excluded. With an established call center queueing model, input variables were modified to adapt this model to the ED setting, while satisfying the underlying assumptions of queueing theory. The primary aim was the derivation and validation of an ED flow model. Chi-square and Student's t-tests were used for model derivation and validation. The secondary aim was estimating the effect of varying ED patient arrival and boarding scenarios on LWBS rates using this model. The assumption of stationarity of the model was validated for three time periods (peak arrival rate = 10:00 a.m. to 12:00 p.m.; a moderate arrival rate = 8:00 a.m. to 10:00 a.m.; and lowest arrival rate = 4:00 a.m. to 6:00 a.m.) and for different days of the week and month. Between 10:00 a.m. and 12:00 p.m., defined as the primary study period representing peak arrivals, 3.9% (n = 4,038) of patients LWBS. Using the derived model, the predicted LWBS rate was 4%. LWBS rates increased as the rate of ED patient arrivals, treatment times, and ED boarding times increased. A 10% increase in hourly ED patient arrivals from the observed average arrival rate increased the predicted LWBS rate to 10.8%; a 10% decrease in hourly ED patient arrivals from the observed average arrival rate predicted a 1.6% LWBS rate. A 30-minute decrease in treatment time from the observed average treatment time predicted a 1.4% LWBS. A 1% increase in patient arrivals has the same effect on LWBS rates as a 1% increase in treatment time. Reducing boarding times by 10% is expected to reduce LWBS rates by approximately 0.8%. This novel queuing theory-based model predicts the effect of patient arrivals, treatment time, and ED boarding on the rate of patients who LWBS at one institution. More studies are needed to validate this model across other institutions.

Assessing Healthcare Utilization for Influenza-like Illness at an Emergency Department and a Student Health Service during the 2009-2010 H1N1 Pandemic.

Estimates of healthcare utilization during an influenza pandemic are needed in order to plan for the allocation of staff and resources. The aim of this study was to assess the number, age, and arrival time of patients with influenza-like-illness (ILI), and associations between their symptoms during the 2009–2010 H1N1 pandemic. We conducted a cross-sectional analysis of electronic health records from the student health service (SHS) and an emergency department (ED) in Morgantown, West Virginia, between January 2009 and December 2010. During the 2009–2010 H1N1 pandemic, patient arrivals at SHS and ED varied over the week. SHS patients arrived early in the week and primarily in the afternoon. ED patient arrivals were more evenly distributed, with busier evenings and weekends. Those with fever were more likely to experience cough, sore throat, vomiting/nausea, chills, congestion, headache, and body-ache. These results can assist health professionals in preparing for an influenza pandemic.

Forecast Driven Simulation Model for Service Quality Improvement of the Emergency Department in the Moses H. Cone Memorial Hospital

Patient satisfaction with the Emergency Department (ED) in a hospital is related to the length of stay, and especially to the amount of waiting time for medical treatments. ED overcrowding decreases quality and efficiency, therefore affecting hospitals’ profitability. This paper presents a forecasting and simulation model for resource management of the ED at Moses H. Cone Memorial Hospital. A linear regression forecasting model is proposed to predict the number of ED patient arrivals, and then a simulation model is provided to estimate the length of stay of ED patients, system throughput, and the utilization of resources such as triage nurses, patient beds, registered nurses, and medical doctors. The near future load level of each resource is presented using the proposed models.

Elevated level of methylglyoxal during diabetic ketoacidosis and its recovery phase

Carbonyl stress is hypothesized to be an associated complication of diabetic ketoacidosis. The production of the glycolytic intermediate methylglyoxal (MG) was followed up in 7 diabetic patients treated for ketoacidosis during pretreatment and recovery phase. Blood samples for methylglyoxal analysis were collected upon patient arrival in emergency department (0 h), and during ketoacidosis treatment between 12-24 h and at 168 h. The study also included 10 normoglycaemic healthy volunteers and 31 type 1 diabetic patients (control diabetes group). The methylglyoxal assay, based on methylglyoxal derivation with 1,2-diamino-4,5-dimethoxybenzene (DDB), was performed by HPLC, only assessing the level of free methylglyoxal. The baseline level of methylglyoxal recorded in normoglycemic healthy controls was 338 +/- 62 nmol/l versus 374 +/- 89 nmol/l in control diabetes group (P = 0.0407). A consistent feature of diabetic ketoacidosis before and during treatment was striking elevation of methylglyoxal as compared with control diabetes group (median test chi(2) = 14.6, df = 3, P = 0.0021). Friedman's ANOVA indicated differences (P = 0.04) among the three sampling times with a peak value (601 +/- 95 nmol/l) at 12-24 h following therapy initiation. However, fasting treatment values at 168 h were still significantly higher than the mean fasting methylglyoxal level in control diabetes group (P = 0.008). The study indicated that diabetic ketoacidosis results in an increase in methylglyoxal level. Excessive production of toxic intermediates such are alpha-dicarbonyls may be a link connecting an acute metabolic event with accelerated tissue damage, a feature characteristic of long-term complications of diabetes.