Emergency Department Arrivals Research Articles

Comparing emergency department and oncology urgent care for non-emergent acute care.

e23107 Background: Adults with cancer often visit the emergency department (ED) during treatment for commonly anticipated issues (e.g. pain, nausea). Some providers have created oncology urgent care clinics (OUCC) to manage these non-emergent issues. Evidence suggests that they are associated with lower ED use. Little is known about the care delivered at OUCCs, and how it compares to ED care. We compared visits from our OUCC to ED visits at our comprehensive cancer center. Methods: We studiedvisits from Jan 1 – June 30, 2023. To limit selection bias, we excluded ED visits if patients were referred by an outpatient clinician, including phone triage. Triage nurses document a validated Emergency Severity Index (ESI, version 4) as part of usual care in the ED, but not the OUCC. The ESI ranks high-severity (level 1 – “patient dying”; 2 – “shouldn’t wait”) to lower-severity (level 5), with the three lower levels distinguished by vital signs and clinical resources needed to stabilize a patient. Two clinicians reviewed OUCC visits and recorded ESIs. We assessed inter-rater reliability of OUCC ESI coding. We used multivariate, mixed-effects methods (visits clustered to patient) to model two outcomes: care escalation (OUCC to ED/inpatient; ED to inpatient) and length of stay (LOS). We excluded visits with ESI at the extremes (1 and 5). We adjusted for sex, age, comorbidities, cancer type and stage, date/time of arrival, and ESI. We then used marginal effects methods to report adjusted outcomes, stratified by ESI. Results: We analyzed 904 OUCC visits (580 patients) and 4,115 ED visits (2,866 patients). Over half (55.5%) of ED arrivals occurred after business hours. Median patient age was 67 (IQR 57 - 74), 48% were female, and 58% non-Hispanic white. Cancer types included 18% hematologic, 17% GI/colorectal, 12% breast, and 7% lung; 11% were advanced-stage. Unweighted inter-rater reliability for ESI was high for a subset of 164 OUCC visits (k = 0.81, 95% CI: 0.72 - 0.89). Although OUCC had a sizable portion of level 2 visits, ED had significantly higher severity. At each ESI level, adjusted rates of care escalation were significantly higher for ED than OUCC (overall aOR 5.6, 95% CI: 4.4 - 7.2), and LOS was longer for ED than OUCC (5.2 hours longer, 95% CI 4.8 – 5.6). Conclusions: The severity of OUCC visits was high, though patient self-referred ED visit severity was higher. OUCC may be more efficient than the ED for managing non-emergency care in this population. Future work should further investigate the quality of care delivered at each site, including cost of care. [Table: see text]

Relationship between on-demand telehealth visits and emergency department and hospital surge during the COVID-19 pandemic.

The relationship between COVID-19-related telehealth calls could be used to predict emergency department visits and hospital surges 3 days later potentially facilitating staffing adjustments in advance of patient arrivals. The purpose of this research was to study the temporal association between frequencies of on demand telehealth calls and emergency department surges during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. This cohort study examined patients who self-initiated synchronous audio-video on-demand telehealth calls between January 1, 2020 and June 30, 2022, and compared these to emergency department arrivals. The exposure in question was a synchronous audio-video on-demand telehealth visit. Our main outcome measured was frequency of emergency department visits. After autocorrelation, a multivariate linear regression model was utilized to determine temporal relationships between the two variables. This cohort study examined 42,429 synchronous audio-video on-demand telehealth calls, of which 43.6% were COVID-19 related, and 540,686 emergency department visits, of which 3.9% were diagnosed with COVID-19. COVID-19-related telehealth calls 3 days prior were predictive of emergency department encounters (r 2=0.85, p<0.001). Emergency department encounters were strongly correlated with hospital admissions (r 2=0.71, p<0.001). Our results demonstrate that telehealth calls related to COVID-19 were an accurate predictor of emergency department encounters 3 days later, and emergency department encounters are highly correlated with hospital admissions. Limitations include that we only assessed a single health system in the region covered by the telemedicine healthcare professionals. We did not examine direct links between these two encounter types nor severity of illness at the patient level. Understanding that telehealth calls related to COVID-19 are highly predictive of emergency department encounters within 3 days may provide a brief but important window to upstaff hospitals at the beginning of future COVID-19 surges.

Pediatric Trauma During COVID-19: What Have We Learned?

BackgroundIn December 2019, an outbreak of a novel pneumonia-like illness, COVID-19 (C-19), emerged in Wuhan, China. In March 2020, as the incidence of C-19 rose, the Israeli government, like that of other nations, declared a national state of emergency, and a full, general lockdown was announced. PurposeThe purpose of this study is to determine the incidence and characteristics of pediatric maxillofacial trauma presentation during the 2020 C-19 lockdown restrictions and compare them to periods outside lockdown between 2019 and 2020 (Pre-C-19). Study design, setting, sampleA retrospective cohort study was conducted and included all pediatric patients (0-18 years) referred to the emergency department (ED) at Tzafon Medical Center, Israel, and diagnosed with maxillofacial injuries. Patients with missing demographic data and patients who did not complete the medical examination were excluded. Predictor/exposure/independent variableThe exposure variable is the date of injury – 2020 C-19 lockdown period compared to the previous year (Pre-C-19). Main outcome variable(s)The main outcome variable was the number of pediatric maxillofacial trauma ED arrivals out of total pediatric ED arrivals and out of total maxillofacial ED arrival. CovariatesCovariates included patient demographics, etiology and place of injury. AnalysesThe association between categorical variables was tested using Fisher exact test or Chi-square test for binary variables with relative risk (RR) when appropriate, logistic regression model used to predict outcome variable. P-values were 2-tailed and statistical significance was defined as P < 0.05. ResultsThe study sample comprised 1174 patients. RR of presenting with oral and maxillofacial trauma out of all pediatric ED patients was 1.85 comparing C-19 period to Pre-C-19 period (CI [1.65 ; 2.073], p < 0.0001). Most of the injuries recorded during the C-19 restrictions occurred in the domestic environment (56.1% compared to 47.5% in Pre-C-19, p < 0.001). Incidences necessitating treatment increased from 45.8% to 55% during C-19 (p = 0.002). ConclusionsDuring C-19 there was a higher rate of pediatric injuries compared to Pre-C-19. The incidence of domestic oral and maxillofacial injuries increased despite the considered home safety.

Forecasting emergency department arrivals using INGARCH models

BackgroundForecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments.ObjectiveWe explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department.Material and methodsWe examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals.ResultsWe document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals.ConclusionOur results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.

Impact of ridesharing platforms on hospitals' emergency department admissions

This paper examines the impact of information technology-based ridesharing platforms on hospitals' emergency department (ED) admissions. Employing a difference-in-differences design, we analyze patient-level data for 42 million ED visits in Florida. We find that the availability of ridesharing positively affects ED arrivals, indicating enhanced accessibility. Our analysis of the heterogeneous impact of ridesharing suggests the most significant effect applies to young, middle-aged, low-income, and non-critical patients. Additionally, we find a significant increase in ED service time for patients with less critical conditions and no change in visit length for critical patients. The rise in ED demand due to ridesharing entry can have two distinct implications. On the one hand, ridesharing entry addresses the access barrier when the patient's condition is non-critical, but ED usage is necessary. This is a positive impact of ridesharing services availability, especially since it helps low-income communities the most. Therefore, hospitals and ridesharing companies can collaborate to address the healthcare access challenge. On the other hand, ridesharing entry may exacerbate already overcrowded EDs by facilitating non-critical and unnecessary ED usage, which can be an adverse effect. In this case, effective patient triage to identify those with urgent needs may become even more essential after ridesharing entry.

Day of the Week Variation in Emergency Department Arrivals, Chest Pain, and Acute Myocardial Infarction Throughout 2016-2019.

Overcrowding in the Emergency department (ED) necessitates a major challenge in delivering high-quality care in acute settings. This study presents a novel approach to modeling the relationship between the day of the week, ED arrivals, chest pain (CP), and acute myocardial infarction (AMI) using regression analysis. We analyzed data from 2016 to 2019 across three platforms: a nationwide representative sample (NHAMCS), a federated data network (TriNetX), and a regional medical center. For the stated three outcomes, the number of patients in that category on each day of the week was calculated; these were then calculated separately for each year, as well as across all four years. In line with prior studies, this study demonstrates the highest percentage of ED arrival on Mondays and the lowest on the weekends. Similarly, chest pain was more prevalent on Mondays, with similar patterns for TriNetX and the regional medical center. Analyzing NHAMCS data demonstrated Wednesdays as the busiest day for AMI-related ED arrivals, although this observation was not statistically significant. This knowledge will better aid us in resource allocation and system awareness, paving a path toward better patient care, improving disease management, and reducing healthcare costs.

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.

Probabilistic forecasting of hourly emergency department arrivals

ABSTRACT An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients’ demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model based on Generalised Additive Models and an advanced dynamic model based on exponential smoothing to generate an hourly probabilistic forecast of ED arrivals for a prediction window of 48 hours. We compare the forecast accuracy of these models against appropriate benchmarks, including TBATS, Poisson Regression, Prophet, and simple empirical distribution. We use Root Mean Squared Error to examine the point forecast accuracy and assess the forecast distribution accuracy using Quantile Bias, PinBall Score and Pinball Skill Score. Our results indicate that the proposed models outperform their benchmarks. Our developed models can also be generalised to other services, such as hospitals, ambulances or clinical desk services.

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.

Cents of Urgency: How Opening a Co-located Urgent Care Center Affects Emergency Department Arrivals

Cents of Urgency: How Opening a Co-located Urgent Care Center Affects Emergency Department Arrivals

An EMS transport destination officer is associated with reductions in simultaneous emergency department arrivals

Objective: A centralized transport destination officer (TDO) is one technique used by EMS systems to distribute patients. This retrospective analysis examines the effect of a TDO on simultaneous arrivals and consecutive simultaneous arrivals at emergency departments in a suburban EMS system, and their relationship to transport unit throughput. Methods: Each system hospital arrival from July 1, 2020, to February 28, 2022, at six study hospitals was evaluated. An arrival within 300 seconds of the previous arrival at the same hospital was designated as a simultaneous arrival. Any simultaneous arrival where the previous arrival was also a simultaneous arrival was further designated as a consecutive simultaneous arrival. Simultaneous and consecutive simultaneous arrivals were aggregated by day to produce countywide daily totals, and days were weighted to account for volume variability. Median and interquartile ranges were calculated and compared, and a Wilcoxon rank-sum test was used for each outcome, with the presence of the TDO as the grouping variable. A Pearson correlation was used to assess the relationship between simultaneous and consecutive simultaneous arrivals to daily average hospital turnaround interval. Results: Median simultaneous arrivals and consecutive simultaneous arrivals decreased from 22 to 19 (non-TDO IQR =9) and from 6 to 4 per day (non-TDO IQR =5), respectively, on days when the TDO was in place. Simultaneous arrivals on non-TDO days had a rank-sum of 143582 compared to 41554 for TDO days (Asymp. Sig <0.001) and consecutive simultaneous arrivals on TDO days had a rank-sum of 142413 vs. 42722 for non-TDO days. (Asymp. Sig= <0.001) Increased simultaneous and consecutive simultaneous arrivals also showed statistically significant correlation with increased average turnaround interval (simultaneous r = 0.513, p < 0.001; consecutive simultaneous r = 0.362 p < 0.001). Conclusions: A centralized EMS transport destination officer is associated with a reduction in simultaneous and consecutive simultaneous arrivals of patients in the emergency department. Further analysis also shows a significant correlation between the number of simultaneous and consecutive simultaneous arrivals and transport unit hospital turnaround interval. This technique to achieve load balancing across transport destinations appears to be effective and can be considered in systems experiencing throughput difficulties.

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation.

BackgroundEmergency department (ED) overcrowding is a concerning global health care issue, which is mainly caused by the uncertainty of patient arrivals, especially during the pandemic. Accurate forecasting of patient arrivals can allow health resource allocation in advance to reduce overcrowding. Currently, traditional data, such as historical patient visits, weather, holiday, and calendar, are primarily used to create forecasting models. However, data from an internet search engine (eg, Google) is less studied, although they can provide pivotal real-time surveillance information. The internet data can be employed to improve forecasting performance and provide early warning, especially during the epidemic. Moreover, possible nonlinearities between patient arrivals and these variables are often ignored.ObjectiveThis study aims to develop an intelligent forecasting system with machine learning models and internet search index to provide an accurate prediction of ED patient arrivals, to verify the effectiveness of the internet search index, and to explore whether nonlinear models can improve the forecasting accuracy.MethodsData on ED patient arrivals were collected from July 12, 2009, to June 27, 2010, the period of the 2009 H1N1 pandemic. These included 139,910 ED visits in our collaborative hospital, which is one of the biggest public hospitals in Hong Kong. Traditional data were also collected during the same period. The internet search index was generated from 268 search queries on Google to comprehensively capture the information about potential patients. The relationship between the index and patient arrivals was verified by Pearson correlation coefficient, Johansen cointegration, and Granger causality. Linear and nonlinear models were then developed with the internet search index to predict patient arrivals. The accuracy and robustness were also examined.ResultsAll models could accurately predict patient arrivals. The causality test indicated internet search index as a strong predictor of ED patient arrivals. With the internet search index, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the linear model reduced from 5.3% to 5.0% and from 24.44 to 23.18, respectively, whereas the MAPE and RMSE of the nonlinear model decreased even more, from 3.5% to 3% and from 16.72 to 14.55, respectively. Compared with each other, the experimental results revealed that the forecasting system with extreme learning machine, as well as the internet search index, had the best performance in both forecasting accuracy and robustness analysis.ConclusionsThe proposed forecasting system can make accurate, real-time prediction of ED patient arrivals. Compared with the static traditional variables, the internet search index significantly improves forecasting as a reliable predictor monitoring continuous behavior trend and sudden changes during the epidemic (P=.002). The nonlinear model performs better than the linear counterparts by capturing the dynamic relationship between the index and patient arrivals. Thus, the system can facilitate staff planning and workflow monitoring.

Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach

Background and objectiveEmergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown.MethodsWe extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric.ResultsCalendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant.ConclusionsOur study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

Synergistic effect of hypoalbuminaemia and hypotension in predicting in-hospital mortality and intensive care admission: a retrospective cohort study

ObjectiveHypoalbuminaemia is an important prognostic factor. It may be associated with poor nutritional states, chronic heart and kidney disease, long-standing infection and cancer. Hypotension is a hallmark of circulatory failure....

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories

ObjectivesTo assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. MethodsAn individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. ResultsTo illustrate the use of the model, a case study was developed for Guy’s and St Thomas’ Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. ConclusionsThe DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Operational factors associated with emergency department patient satisfaction: Analysis of the Academy of Administrators of Emergency Medicine/Association of Academic Chairs of Emergency Medicine national survey.

Patient satisfaction is a focus for emergency department (ED) and hospital administrators. ED patient satisfaction studies have tended to be single site and focused on patient and clinician factors. Inclusion of satisfaction scores in a large, national operations database provided an opportunity to conduct an investigation that included diverse operational factors. We performed a retrospective analysis of the 2019 Academy of Administrators in Academic Emergency Medicine/Association of Academic Chairs of Emergency Medicine (AAAEM/AACEM) benchmarking survey to identify associations between operational factors and patient satisfaction. We identified 59 database variables as potential predictors of Press Ganey likelihood-to-recommend and physician overall scores. Using random forest modeling, we identified the top eight predictors in the models and described their associations. Forty-three (57.3%) academic departments responding to the AAAEM/AACEM survey reported patient satisfaction scores for 78 EDs. Likelihood to recommend ranged from 30.0 to 93.0 (median= 74.8) and was associated with ED length of stay, boarding, use of hallway spaces, hospital annual admissions, faculty base clinical hours, proportion of patients leaving before treatment complete (LBTC), and provider in triage hours per day. Physician overall score ranged from 53.3 to 93.4 (median= 81.9) and was associated with faculty base clinical hours, x-ray utilization, annual ED arrivals, LBTC, use of hallway spaces, arrivals per attending hour, and CT utilization. ED patient satisfaction was associated with intrinsic and extrinsic factors, some being potentially manageable within the ED but others being relatively fixed or outside the control of ED operations. For likelihood to recommend, patient flow was dominant, with erosion of satisfaction observed with increased boarding and longer LOS. Factors associated with physician overall score were more varied. The use of hallway spaces and base clinical hours greater than 1,500 per year were associated with both lower likelihood-to-recommend and lower physician overall scores.

Polynomial modeling of emergency department arrivals: An analysis of local and national utilization patterns

Emergency department (ED) overcrowding is a national problem that is associated with ambulance diversion, decreased patient and provider satisfaction and poor patient outcomes. This study presents a novel approach to modeling the relationship between time of day, day of week, and ED arrivals using a hierarchical polynomial regression model. A series of hierarchical regression models were created to determine polynomial effects and capture the covariability (defined as R2) of the relationships from the 2009 to 2017 National Hospital Ambulatory Medical Care Survey (NHAMCS) Emergency Department Public Use Data File and institutional data from a regional medical center from 2018 to 2019. The following hierarchical regression models were constructed: cubic main effects, cubic interaction effects, quartic main effects, quartic interaction effects, quintic main effects, and quintic interaction effects. Based on maximal improvement in R2 and significance of each of the four effects in both the national and institutional data sets, the quartic main effects model was determined to be optimal for describing ED arrival patterns. In alignment with prior studies, significantly higher ED arrival volumes were observed on Mondays in comparison to all other weekdays.

Impact of a Stay-at-Home Order on Stroke Admission, Subtype, and Metrics during the COVID-19 Pandemic

Objective: Our study aims to evaluate the impact of a stay-at-home order on stroke metrics during the 2019-novel coronavirus (COVID-19) pandemic. Methods: Data on baseline characteristics, stroke subtype, initial National Institutes of Health Stroke Scale (NIHSS) score, the time between last known well (LKW) to emergency department (ED) arrival, tissue plasminogen activator (tPA) administration, the involvement of large vessel occlusion (LVO), and whether mechanical thrombectomy (MT) was pursued in patients with acute stroke were extracted from 24 March to 23 April 2020 (the time period of a stay-at-home order was placed due to the COVID-19 pandemic as the study group) at a tertiary care hospital in West Michigan, USA, compared with data from 24 March to 23 April 2019 (control group). Results: Our study demonstrated a reduction in cases of acute ischemic stroke (AIS), although this did not reach statistical significance. However, there was an increase in hemorrhagic stroke (7.5% controls vs. 19.2% study group). The age of stroke patients was significantly younger during the period of the stay-at-home order compared to the control group. We identified a significant overall delay of ED arrivals from LKW in the study group. Additionally, an increased number of AIS patients with LVO in the study group (34.8%) was found compared to the control group (17.5%). A significantly increased number of patients received MT in the study group. Additionally, 11 patients were COVID-19 PCR-positive in the study group, 10 with AIS and only 1 with hemorrhagic stroke. Patients with COVID-19 had a high incidence of atrial fibrillation and hyperlipidemia. One AIS patient with COVID-19 rapidly developed cytotoxic edema with corresponding elevated inflammatory biomarkers. No statistical significance was noted when stroke subtype, LVO, and MT groups were compared. Conclusions: There was a trend of decreasing AIS admissions during the COVID-19 pandemic. There was also a significantly increased number of AIS patients with LVO who received MT, especially those with COVID-19. We conclude that cytokine storm resulting from SARS-CoV-2 infection might play a role in AIS patients with COVID-19.

Rebound of Severe Alcoholic Intoxications in Adolescents and Young Adults After COVID-19 Lockdown

PurposeThe coronavirus lockdown in Italy ended, but the postlockdown phase may be even more challenging than the outbreak itself if the impact on mental health is considered. To date, little evidence is available about the effect of lockdown release in terms of adolescent health from the perspective of an emergency department (ED). MethodsWe reviewed data on ED arrivals of adolescents and young adults (aged 13–24 years) in the weeks immediately before and after the Italian lockdown release in 2020, and in the same periods in 2019, with a focus on cases of severe alcohol abuse, psychomotor agitation, and other mental issues. ResultsThe relative frequency of severe alcohol intoxications increased from .88% during the last part of the lockdown to 11.3% after lockdown release. When comparing these data with the same period in 2019, a highly significant difference emerged, with severe alcohol intoxications accounting for 11.31% of ED visits versus 2.96%, respectively. The relative frequency of ED arrivals related to psychomotor agitation or other mental health issues was not significantly increased after lockdown release. ConclusionsThis report suggests that emergency services should be prepared for a possible peak of alcohol intoxication-related emergencies in adolescents and young adults. The connection between alcohol abuse and mental health should not be overlooked.

Social Determinants of Hallway Bed Use.

IntroductionHallway beds in the emergency department (ED) produce lower patient satisfaction and inferior care. We sought to determine whether socioeconomic factors influence which visits are assigned to hallway beds, independent of clinical characteristics at triage.MethodsWe studied 332,919 visits, across 189,326 patients, to two academic EDs from 2013–2016. We estimated a logistic model of hallway bed assignment, conditioning on payor, demographics, triage acuity, chief complaint, patient visit frequency, and ED volume. Because payor is not generally known at the time of triage, we interpreted it as a proxy for other observable characteristics that may influence bed assignment. We estimated a Cox proportional hazards model of hallway bed assignment on length of stay.ResultsMedian patient age was 53. 54.0% of visits were by women. 42.1% of visits were paid primarily by private payors, 37.1% by Medicare, and 20.7% by Medicaid. A total of 16.2% of visits were assigned to hallway beds. Hallway bed assignment was more likely for frequent ED visitors, for lower acuity presentations, and for psychiatric, substance use, and musculoskeletal chief complaints, which were more common among visits paid primarily by Medicaid. In a logistic model controlling for these factors, as well as for other patient demographics and for the volume of recent ED arrivals, Medicaid status was nevertheless associated with 22% greater odds of assignment to a hallway bed (odds ratio 1.22, [95% confidence interval, CI, 1.18–1.26]), compared to private insurance. Visits assigned to hallway beds had longer lengths of stay than roomed visits of comparable acuity (hazard ratio for departure 0.91 [95% CI, 0.90–0.92]).ConclusionWe find evidence of social determinants of hallway bed use, likely involving epidemiologic, clinical, and operational factors. Even after accounting for different distributions of chief complaints and for more frequent ED use by the Medicaid population, as well as for other visit characteristics known at the time of triage, visits paid primarily by Medicaid retain a disproportionate association with hallway bed assignment. Further research is needed to eliminate potential bias in the use of hallway beds. [West J Emerg Med. 2020;21(4)949–958.]