Emergency Department Crowding Research Articles

34 PATHFINDER; AN ALTERNATIVE TO THE EMERGENCY DEPARTMENT FOR OLDER PATIENTS WHO DIAL 999/112

Abstract Background Traditionally in Ireland, people who dial 999/112 are transported to the Emergency Department (ED) unless they decline to travel. Many have non-urgent needs that could be treated elsewhere. Older people are particularly vulnerable to adverse events while in hospital. Alternative care pathway models can reduce ED crowding and improve patient outcomes. Methods This service is a collaboration between the National Ambulance Service and Occupational Therapy (OT) and Physiotherapy Departments at a Dublin teaching hospital; funded by the Sláintecare Integration fund. It responds to low acuity 999/112 calls for people 65 years and older within the hospital catchment. The team operates Monday–Friday, a ‘Rapid Response Team’ (Advanced Paramedic and Physiotherapy/OT) (8:00–20:00) and a ‘Follow-Up Team’ (Physiotherapy and OT) (8:00–16:00). Once activated, the ‘Rapid Response Team’ conduct a comprehensive assessment in the home to establish whether a suitable alternative to the ED is available, commonly through the activation of one or more alternative care pathways (e.g. GP, Integrated Care Team, Primary Care Team, Community Intervention Team, Geriatric Day Hospital). Results In the first year, 485 patients were responded to. Average age was 80 years, average Rockwood Clinical Frailty Scale score was 6 (moderately frail). The majority (68%) remained at home following initial review. Less than 1% re-presented within 24 hours, 9% re-presented within 7 days. The three most common reasons for dialling 999/112 were falls, generally unwell and non-traumatic back pain. Patient and care-giver feedback demonstrated a very high level of satisfaction with the service. Conclusion This model is a safe alternative to the ED for older people following a low-acuity 999/112 call. Access to a network of alternative care pathways and immediate follow-up are two key enablers. The overwhelmingly positive feedback confirms that older people want this service. It is the first model of this kind to be evaluated in Ireland, and could expand, with local adaptation, nationally.

The impact of Medicaid expansion on emergency department wait times.

To estimate the impact of Medicaid expansion on emergency department (ED) wait times. We used 2012-2017 hospital-level secondary data from the CMS Hospital Compare data warehouse. We used a state-level difference-in-differences approach to identify the impact of Medicaid expansion on four measures of ED wait times: time before being seen by a provider; time before being sent home after being seen by a provider; boarding time spent in the ED waiting to be discharged to an inpatient room; and the percentage of patients who left without being seen. We compared outcomes in states that expanded Medicaid with those in states that did not expand Medicaid. Our sample included all US acute care hospitals with EDs in states that did not ever expand Medicaid or that fully expanded Medicaid in January of 2014. Medicaid expansion was associated with a 3.1-min increase (SE: 0.994, baseline mean: 30.8min) in the time spent waiting to see an ED provider, a relative increase of 10%. Patients who were eventually sent home after being seen by a provider experienced a 7.5-min increase (SE: 1.8, baseline mean 142.1min) in wait time. Boarding time rose by 3.8min (SE 1.9, baseline mean 111.4min). The percentage of patients who left without being seen rose by 0.3 percentage points (SE: 0.09, baseline mean 2.0), a relative increase of 15.3%. This study provides multistate evidence that Medicaid expansion increased ED wait times for patients, indicating that ED crowding may have worsened post-expansion. Future work should aim to uncover the mechanisms through which insurance expansion increased ED wait times to provide policy direction.

Mixed effect of increasing outflow of medical patients from an emergency department

Background and aimSince 2014, the annual number of patients entering our emergency department (ED) has increased significantly. These were primarily Internal Medicine (IM) patients, and of these, 25–30% were admitted. The present governmental policy presents a deterrent to adding IM beds for these patients, and Emergency and IM departments cope with ever-increasing number of IM patients. We describe a quality improvement intervention to increase outflow of IM patients from the ED to the IM departments.MethodsWe conducted a quality improvement intervention at the Shaare Zedek Medical Center from 2014 to 2018. The first stage consisted of an effort to increase morning discharges from the IM departments. The second stage consisted of establishing a process to increase the number of admissions to the IM departments from the ED.ResultsImplementation of the first stage led to an increased morning discharge rate from a baseline of 2–4 to 18%. The second stage led to an immediate mean (± SD) morning transfer of 35 ± 7 patients to the medical departments (8–12 per department), providing significant relief for the ED. However, the additional workload for the IM departments’ medical and nursing staff led to a rapid decrease in morning discharges, returning to pre-intervention rates. Throughout the period of the new throughput intervention, morning admissions increased from 30 to > 70%, and were sustained. The number of patients in each department increased from 36 to 38 to a new steady state of 42–44, included constant hallway housing, and often midday peaks of 48–50 patients. Mean length of stay did not change. IM physician and nurse dissatisfaction led to increased number of patients being admitted during the evening and night hours and fewer during the morning.ConclusionWe describe a quality improvement intervention to improve outflow of medical patients from the ED in the morning hours. The new ED practices had mixed effects. They led to less ED crowding in the morning hours but increased dissatisfaction among the IM department medical and nursing staff due to an increased number of admissions in a limited number of hours. The present governmental reimbursement policy needs to address hospital overcrowding as it relates to limited community healthcare beds and an aging population.

Centralized Ambulance Destination Determination: A Retrospective Data Analysis to Determine Impact on EMS System Distribution, Surge Events, and Diversion Status.

IntroductionEmergency medical services (EMS) systems can become impacted by sudden surges that can occur throughout the day, as well as by natural disasters and the current pandemic. Because of this, emergency department crowding and ambulance “bunching,” or surges in ambulance-transported patients at receiving hospitals, can have a detrimental effect on patient care and financial implications for an EMS system. The Centralized Ambulance Destination Determination (CAD-D) project was initially created as a pilot project to look at the impact of an active, online base hospital physician and paramedic supervisor to direct patient destination and distribution, as a way to improve ambulance distribution, decrease surges at hospitals, and decrease diversion status.MethodsThe project was initiated March 17, 2020, with a six-week baseline period; it had three additional study phases where the CAD-D was recommended (Phase 1), mandatory (Phase 2), and modified (Phase 3), respectively. We used coefficients of variation (CV) statistical analysis to measure the relative variability between datasets (eg, CAD-D phases), with a lower variation showing better and more even distribution across the different hospitals. We used analysis of co-variability for the CV to determine whether level loading was improved systemwide across the three phases against the baseline period. The primary outcomes of this study were the following: to determine the impact of ambulance distribution across a geographical area by using the CV; to determine whether there was a decrease in surge rates at the busiest hospital in this area; and the effects on diversion.ResultsWe calculated the CV of all ratios and used them as a measure of EMS patient distribution among hospitals. Mean CV was lower in Phase 2 as compared to baseline (1.56 vs 0.80 P < 0.05), and to baseline and Phase 3 (1.56 vs. 0.93, P <0.05). A lower CV indicates better distribution across more hospitals, instead of the EMS transports bunching at a few hospitals. Furthermore, the proportion of surge events was shown to be lower between baseline and Phase 1 (1.43 vs 0.77, P <0.05), baseline and Phase 2 (1.43 vs. 0.33, P < 0.05), and baseline and Phase 3 (1.43 vs 0.42, P < 0.05). Diversion was shown to increase over the system as a whole, despite decreased diversion rates at the busiest hospital in the system.ConclusionIn this retrospective study, we found that ambulance distribution increased across the system with the implementation of CAD-D, leading to better level loading. The surge rates decreased at some of the most impacted hospitals, while the rates of hospitals going on diversion paradoxically increased overall. Specifically, the results of this study showed that there was an improvement when comparing the CAD-D implementation vs the baseline period for both the ambulance distribution across the system (level loading/CV), and for surge events at three of the busiest hospitals in the system.

Cost-minimisation analysis of acute myocardial infarction rule-out in low-risk patients: primary care emergency setting versus hospital setting

Abstract Background Hospital management of low-risk chest pain contributes to extensive use of healthcare resources and emergency department crowding. High efficacy rule-out, with subsequent reduction in costs and length of stay, has been demonstrated for the ESC 0/1-hour algorithm using high-sensitivity cardiac troponins (hs-cTn) in hospital cohorts. Purpose To estimate potential differences in healthcare costs by assessing patients with low risk for acute coronary syndromes (ACS) in a primary care emergency setting using the ESC 0/1-hour algorithm compared to routine management in a hospital setting. Methods This cost-minimisation analysis compared direct costs of applying the 0/1-hour algorithm in a low-risk primary care cohort to a low-risk chest pain cohort at a large general hospital in Norway. Data from the prospective OUT-ACS study (One-hoUr Troponin in a low-prevalence population of Acute Coronary Syndrome,[1] inclusion period 2016–2018) were used to calculate costs per patient at a primary care emergency clinic. For the hospital setting estimates, anonymous data were extracted for all low-risk chest pain patients treated at a large general hospital in 2018. Cost items include complete hospital costs per different diagnosis-related groups as defined in national assessments, as well as resource items required to use the algorithm in primary care, including personnel time and test- and treatment costs. Primary outcome was the difference in healthcare costs when assessing the low-risk cohort in a primary care setting compared to a hospital setting. The secondary outcome was the difference in length of stay. Results The costs of assessing the low-risk cohort at the primary care emergency clinic and the general hospital were estimated at €178 and €1480, respectively (Table 1). Thus, the estimated reduction in health care costs among patients assessable by the 0/1-hour algorithm outside of hospital was €1302 per patient, with a mean decrease in length of stay of 18.9 hours. Additional diagnostic procedures (e.g. stress ECG and echocardiogram) were performed in 31.9% (n=181/567) of the low-risk hospital cohort, which was part of the cost-driving estimate. Conclusion Assessment of patients considered as low-risk for ACS with the ESC 0/1-hour algorithm in a primary care emergency setting seems to decrease healthcare costs significantly, in addition to a reduction in both length of stay and potentially unnecessary hospitalisations. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Norwegian Research Fund for General PracticeThe Norwegian Committee on Research in General Practice

EMS non-conveyance: A safe practice to decrease ED crowding or a threat to patient safety?

BackgroundThe safety of the Emergency Medical Service’s (EMS’s) non-conveyance decision was evaluated by EMS re-contacts, primary health care or emergency department (ED) visits, and hospitalization within 48 h. The secondary outcome was 28-day mortality.MethodsThis cohort study used prospectively collected data on non-conveyed EMS patients from three different regions in Finland between June 1 and November 30, 2018. The Adjusted International Classification of Primary Care (ICPC2) as the reason for care was compared to hospital discharge diagnoses (ICD10). Multivariable logistic regressions were used to determine factors that were independently associated with adverse outcomes. Results are presented with adjusted odds ratios (aORs) together with 95% confidence intervals (CIs). Data regarding deceased patients were reviewed by the study group.ResultsOf the non-conveyed EMS patients (n = 11,861), 6.3% re-contacted the EMS, 8.3% attended a primary health care facility, 4.2% went to the ED, 1.6% were hospitalized, and 0.1% died 0–24 h after the EMS mission. The 0–24 h adverse event rate was higher than 24–48 h. After non-conveyance, 32 (0.3%) patients were admitted to an intensive care unit within 24 h. Primary non-urgent EMS mission (aOR 1.49; 95% CI 1.25 to 1.77), EMS arrival at night (aOR 1.82; 95% CI 1.58 to 2.09), ALS unit type vs BLS (aOR 1.43; 95% CI 1.16 to 1.77), rural area (aOR 1.74; 95% CI 1.51 to 1.99), and older patient age (aOR 1.41; 95% CI 1.20 to 1.66) were associated with subsequent primary health care visits (0–24 h).ConclusionsFour in five non-conveyed patients did not have any re-contact in follow-up period. EMS non-conveyance seems to be a relatively safe method of focusing ED resources and avoiding ED crowding.

149 Examining the Relationship Between Markers of Emergency Department Crowding and Physician Wellbeing

Literature on emergency department (ED) crowding and boarding has primarily focused on adverse patient outcomes including increased patient mortality, and longer hospital length-of-stay. Few studies have explored relationships between ED crowding, boarding and physician wellbeing (burnout, professional satisfaction) despite evidence demonstrating that EM specialists are consistently among those with the highest rates of burnout. Therefore, we sought to evaluate the relationships between markers of ED crowding and boarding and physician wellbeing. Data for this prospective, observational study were collected from resident and attending emergency physicians employed at our Level-I, tertiary care referral center ED. Participants completed the 7- item Physician Well-Being Index (PWBI) twice weekly for three months following clinical shifts. The PWBI assesses physician distress across several dimensions using a score from 0 to 7 and has previously demonstrated validity with similar physician groups. For the same period, we collected markers of ED crowding and boarding: ED volume, numbers of arriving and boarding patients by shift, and average census by shift. Generalized Estimating Equations (GEE) analyses were performed to examine relationships between ED crowding and boarding markers and physician PWBI scores. Hierarchical regression models were constructed to control for the potentially confounding effects of the day of the week, shift type, and provider role (resident/attending). 42 emergency physicians (18 (43%) resident, 24 (57%) attending) participated in the study, providing 273 PWBI assessments. Data for 4, 318 ED patient encounters were summarized for the same period. Mean PWBI scores were 2.3 (SD 1.53, 95% CI: 2.1-2.5); no significant differences between the resident and attending groups were noted (2.0, ±1.50, 95% CI: 1.5-2.6 vs. 2.5, ±1.52, 95% CI: 1.9-3.0, respectively). After adjusting for the effects of day of week, shift type, and provider role, we did not observe statistically significant relationships between PWBI scores and count of arrivals in 24 hours (b=-0.089, SE=0.0593, CI: -0.206-0.027, p=0.133), patient arrivals by shift (b=-0.006, SE=0.013, CI: -0.031-0.02, p=0.667), average census by shift (b=0.004, SE=0.019, CI: 0.041-0.047, p=0.829), or average boarding census by shift (b=-0.161, SE=0.1051, CI: -0.367-0.045, p=0.127). In this study, ED crowding and boarding were not significantly related to physician wellbeing, as measured by the PWBI. Findings suggest further research is needed to validate the PWBI in capturing ED physician wellbeing during periods of ED crowding and boarding. Next steps are to explore the relationships between individual dimensions of wellbeing (fatigue, mental quality of life) and ED busyness, given the important impact of dimensions of burnout on care team wellbeing and patient outcomes.

7 Impact of a Hospital Cyberattack on EMS Arrivals at Neighboring Emergency Departments

Cyber attacks, including ransomware on health care delivery organizations (HDO), are increasing in severity and frequency. Emergency medical services (EMS) plays a significant role in emergency care and disaster response, including the rapid transport of acute patients to receiving emergency departments (EDs). EMS ambulance diversion from one facility to another is a common occurrence with ED crowding, but it can be a result of internal or external disasters as well. The effects of health care cyber attacks on EMS transports and census has yet to be reported in the literature. The purpose of this study is to report regional EMS census impacts during an active cyber-attack on a neighboring HDO. We performed a retrospective analysis of the daily combined EMS transport census of two large urban academic EDs in San Diego, CA during the first week of a ransomware cyber-attack on a nearby multi-hospital HDO (May 2nd - 8th 2021). We then compared the mean to the previous 4 weeks. A one-way analysis of variance (ANOVA) and Tukey test were used to compare EMS transport numbers by week. The mean combined daily EMS census for weeks 1 - 4 (no cyber-attack) were: week 1 (April 4th – 10th) – 69 (SD 10), week 2 (April 11th – 17th) – 68 (SD 5), week 3 (April 18th – 24th) – 71 (SD 7), and week 4 (April 25th – May 1st) – 75 (SD 9). For week 5 (week of the cyber-attack) the mean was 116 (SD 24). There was a statistically significant higher EMS census during the cyber-attack when compared to the 4 weeks prior (Table 1), with most days experiencing double the normal volume (Range 78 -144). This study reports a significant increase in EMS ambulance arrivals at two EDs in proximity to a multi-hospital HDO under active cyber-attack. This result suggests cyber-attacks can have significant regional impacts on EMS volumes, and therefore, cyber disaster planning should take into consideration potentially large diversions of patients to other facilities via EMS.

108 Emergency Department Crowding Resulting from a Local Health System Cyberattack

Emergency department crowding continues to be a concern in many major urban emergency departments. The causes of ED crowding are complex and multi-factorial, and now cyber attach threats are contributing to this ongoing issue. We analyze the impact of a cyberattack on a large, multi-hospital health system on the already burdensome crowding in our emergency department. This is a retrospective study evaluating the boarding of admitting patients on two EDs from a single healthcare system during an ongoing ransomware cyberattack on a neighboring health system in San Diego. San Diego regional hospitals serve an estimated population of roughly 3.3 million residents across a 4,206-square-mile land mass; approximately 1.4 million reside within the City of San Diego. One ED is in an urban level one trauma center and the other is a suburban academic hospital. They have a combined annual census of ∼80, 000). Census and admitted patients from the two-week period before (April 17 through April 30, 2021) and two weeks after (May 2 through May 15, 2021) the incident started (May 1, 2021). In the study period, the number of admissions, discharges and LWBS statuses increased therefore increasing daily ED census. Between the pre- and post-cyberattack periods, the average number of admissions, discharges, LWBS and overall census increased by 46 (95% CI = 24.32, 67.68), 7 (95% CI = -1.93, 17.93), 25 (95% CI = 13.55, 36.45) and 8 (95% CI = 3.02, 12.98), respectively. The number of EDIPs increased in the post-period. The percent of admissions being held in the ED to ED census were roughly 13% in the pre-period and 14% in the post-period, demonstrating an increased in the percent % of EDIPs within daily ED volume. These results reveal the significant rise in ED visits and boarding as a result of the ransomware cyberattack on a neighboring health system. As these attacks likely become more common in the future, local and regional response strategies to consider the safe management of patients by other unaffected health systems may help reduce the impact on ED crowding and maintain flow in the department so a sustained surge doesn’t impact patient safety.

159 Modified Early Warning Score (MEWS)-Enhanced Emergency Department Flow Process

Emergency Department (ED) crowding is a longstanding problem in the US. Admission to the inpatient floors can be a bottleneck in ED patient flow, due to the need for hospitalist assessment of patient clinical stability. Early Warning Systems (EWS) have been proposed that use vital signs to detect early signs of clinical deterioration. We set out to determine whether the use of a modified version of the EWS score (MEWS) could be used to discriminate patients in which ED streamlined admission orders could be placed prior to being seen by the hospitalist-- with a goal of improving ED length of stay (LOS) without increasing adverse events. We conducted an IRB approved retrospective, observational, cross-sectional analysis of inpatient admissions from the ED at a Level 1 Trauma Center in NE PA with an annual census of approximately 90,000 visits. Subjects were included if they were ≥18 yo and admitted to the hospital medicine service. The study design compared a time period prior to implementation to a MEWS-enhanced ED admission patient flow process (Pre-MEWS: 2/19/2017-2/19/2019) with the post implementation period (Post-MEWS: 2/19/2019-2/19/2020). Post-MEWS implementation, patients with a low MEWS score (0-1) were admitted with an abbreviated order set to expedite the admission process. Those with higher MEWS scores remained in the ED until they were seen by the admitting team. Metrics such as demographics, ED LOS (minutes) and need for a Rapid Response Team (RRT) were collected. A total of 44,986 admissions occurred; 28,624 (63.63%) were Pre-MEWS while 16, 362 (36.37%) were Post-MEWS. The average patient age was 68.53±17.27 with a younger mean age among those in the Pre-MEWS as compared to Post-MEWS (68.66±17.31 vs 68.30±17.21; p=0.03). Over half (51.8%) the patients were female without a significant difference; Pre- vs Post-MEWS periods (51.78% vs 51.85%; p=0.88). The median ED LOS was 371 minutes (Inter-Quartile Range 0(IQR): 288 to 509) with a statistically significantly shorter median ED LOS in the post-MEWS (358; IQR, 273 to 478) as compared with Pre-MEWS period (379; IQR, 288 to 509). The ED LOS in the post-MEWS did not differ when comparing those with MEWS > 1 (354; IQR, 271 to 476) with MEWS ≤ 1(360; IQR, 275 to 479) (p=0.12) while in the Pre-MEWS period the ED LOS was significantly longer among those with MEWS ≤ 1 (384(IQR, 292 to 514) vs 372(IQR, 282 to 501); p<0.0001). There was total of 681 Rapid Responses events of which 236 (34.65%) were within the first 24 hours of admission (RRT-24hr). When comparing the pre-MEWS to post-MEWS periods there was no significant difference in RRT-24hr (143(33.26%) vs 93(37.05%); p=0.32). The use of a modified MEWS enhanced admission process to the hospital medicine service was associated with a significant decrease in ED LOS without a significant increase in adverse outcome as measured by RRT events within 24 hours of admission.

314 Association Between the Shorter Length of Stay in Emergency Department and the Use of Propofol Among Patients Required Procedural Sedation and Analgesia

Procedural sedation and analgesia (PSA) is essential for emergency physicians when patients need painful or uncomfortable procedures. Propofol, midazolam and ketamine are commonly used in the emergency department (ED) for PSA. Painful or uncomfortable procedures become difficult when PSA is inadequate because patients tend to be agitated due to pain or distress. In such cases, additional sedative agents are often administrated. If additional sedative agents are administrated repeatedly, it could cause over-sedation. Over-sedation results in extended ED stay and or any other adverse events such as desaturation. Previous studies showed that propofol could shorten the length of sedation compared to other sedative agents including midazolam and ketamine. However, whether it affects the length of stay (LOS) in ED is unclear. Our hypothesis was that the propofol use for PSA is associated with shorter LOS in ED than other agents. We conducted a single-center, retrospective cohort study. We included patients who were proceeded PSA in the ED from May 2017 to November 2019. Excluded criteria were data missing, required admission, and use of 2 or more sedative agents. Eligible patients were divided into two groups, the propofol group, and other agents (midazolam and ketamine) group. Covariables were age, physical status before PSA, targeted depth of sedative level, the indication of sedation (reduction of dislocation or fracture, suture, defibrillating shock, Imaging test), and practitioner (attending or resident). A total of 124 patients were eligible for cases. Their median age was 30.5 years old. Forty-six cases were included in the propofol group and 76 were included in other agents’ group. Their median LOS in ED was 84 minutes (Interquartile Range (IQR) 70-105) and 103 minutes (IQR 77-151). After adjusting for five potential confounders, PSA by propofol was significantly associated with the shorter LOS in ED (hazard ratio 2.21, 95% confidential interval 1.34-3.65, p-value <0.01). The use of propofol for PSA was associated with shorter LOS in ED. This result could resolve ED crowding.

46 External Validation of a Singapore Electronic Health Record-Based Emergency Department Admission Prediction Model in a North American Setting

Models for predicting admission of patients from an emergency department (ED) have been suggested as a device for reducing ED crowding; however, such models are not widely implemented. The objective of this study is to validate an existing ED admission model on data from an outside hospital, as a first step to identifying a generalizable means to develop such models. Previously, an ED admission model was developed on retrospective observational data at a large urban hospital in Singapore. Data from the electronic health record (EHR) of a tertiary medical center in Durham, North Carolina (NC ED) were curated and transformed to align closely with the features of the Singapore model. A stepwise logistic regression model was built on these features to predict the probability of inpatient admission for patients presenting to the NC ED at the time of triage. A randomly selected 30% subset of the data was withheld from training and used to validate the model. Performance metrics were compared to those attained by the Singapore team. A total of 60, 064 encounters from January to December 2019 were included in the analysis; these patients had an admission rate of 28.2%. Variables used for modeling were first identified by the Singapore team and included age, sex, day of week, Emergency Severity Index (ESI), race, shift time, mode of arrival, time of year, postal code, fever status, and number of prior ED visits. The model we trained on the NC ED EHR data, using the features of the Singapore team’s model, had an area under the ROC curve (AUROC) of 0.821 (95% CI, 0.804-0.837), comparable to the AUROC of 0.825 (95% CI, 0.824-0.827) obtained by the Singapore team’s on their data. The most significant features associated with discharge included an ESI Score above 3 and ED arrival by law enforcement; the most significant features associated with admission included ESI Score below 3, fever status, and advanced age. We have been able to map elements from the NC ED EHR to the Singapore triage model elements to develop a model that performs comparably. Thus, a general method for building simple, effective ED admission models at various independent hospitals is attainable and scalable.

Adverse events and preventive measures related to COVID-19 vaccines.

The coronavirus disease 2019 (COVID-19) vaccines are categorized according to the manufacturing technique, including mRNA vaccines and adenovirus vector vaccines. According to previous studies, the reported efficacy of the COVID-19 vaccine is excellent regardless of the type of vaccine, and the majority of studies have shown similar results for safety. Most of the adverse reactions after vaccination were mild or moderate grade, and severe reactions were reported in a very small proportion. However, the adverse reactions that might occur after nationwide vaccinations can contribute to crowding of emergency departments, and this can further lead to significant obstacles to providing necessary treatment for life-threatening conditions. Therefore, as emergency physicians, we would like to present some concerns and suggestions to prevent these predictable problems.

Identifying and Addressing the Underlying Core Problems in Healthcare Environments: An Illustration Using an Emergency Department Game.

The emergency department (ED) crowding is a critical healthcare issue worldwide that leads to long waits and poorer healthcare outcomes. Goldratt’s theory of constraints (TOC) has been used effectively to improve such problematic environments for more than three decades. While most TOC solutions are simple, with many viewing them as purely common sense, they represent paradigm shifts in how to manage complex, uncertain, and silo environments. Goldratt used a simple dice game with a straight flow (I-shape) to illustrate the impact of dependent resources and statistical fluctuations in managing resources. Additionally, games help to overcome resistance to change and gain ownership by having participants develop their solutions. This new cooperative game illustrates an ED environment where patients may follow different care pathways according to their clinical needs, timeliness of care is measured in minutes, the demand is highly uncertain, and treatment must frequently start almost immediately. A Monte Carlo simulation validated the TOC solution to this ED game, achieving results similar to the real TOC’s implementations. Moreover, this article provides a thorough process to Socratically introduce TOC to healthcare professionals and others to recognize that the EDs’ (like other healthcare systems’) core problem is the traditional approach to managing them.

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS.

BackgroundEmergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding.ObjectiveThe aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding.MethodsWe searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies.ResultsPotential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments.ConclusionsThere is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

Universal SARS-CoV-2 Testing of Emergency Department Admissions Increases Emergency Department Length of Stay

Study objectiveOur institution experienced a change in SARS-CoV-2 testing policy as well as substantial changes in local COVID-19 prevalence, allowing for a unique examination of the relationship between SARS-CoV-2 testing and emergency department (ED) length of stay.MethodsThis was an observational interrupted time series of all patients admitted to an academic health system between March 15, 2020, and September 30, 2020. Given testing limitations from March 15 to April 24, all patients receiving SARS-CoV-2 tests were symptomatic. On April 24, testing was expanded to all ED admissions. The primary and secondary outcomes were ED length of stay and number needed to test to obtain a positive, respectively.ResultsA total of 70,856 patients were cared for in the EDs during the 7-month period. The testing change increased admission length of stay by 1.89 hours (95% confidence interval 1.39 to 2.38). The number needed to test was 2.5 patients and was highest yield on April 1, 2020, when the state positivity rate was 39.7%; however, the number needed to test exceeded 170 patients by Sept 1, 2020, at which point the state positivity rate was 0.5%.ConclusionAlthough universal SARS-CoV-2 testing of ED admissions may meaningfully support mitigation and containment efforts, the clinical cost of testing all admissions amid low community positivity is notable. In our system, universal ED SARS-CoV-2 testing was associated with a 24% increase in admission length of stay alongside the detection of only 1 positive case every other day. Given the known harms and risks of ED boarding and crowding, solutions must be developed to support regular operational flow while balancing infection prevention needs.

Bed-to-Bed Transfer Program Among Patients Who Need Hospitalization in a Crowded Emergency Department in Taiwan.

Emergency department (ED) crowding is a universal issue. In Taiwan, patients with common medical problems prefer to visit ED of medical centers, resulting in overcrowding. Thus, a bed-to-bed transfer program has been implemented since 2014. However, there was few studies that compared clinical outcomes among patients who choose to stay in medical centers to those being transferred to regional hospitals. The aim of this study was to explore the transfer rate, delineate the factors related to patient transfer, and clarify the influence upon the program outcomes. A retrospective cohort study was conducted using demographic and clinical disease factors from the patient electronic referral system, electronic medical records (EMRs) of a medical center in Taipei, and response to referrals from regional hospitals. The study included adult patients who were assessed as appropriate for transfer in 2016. We analyzed the outcomes (length of stay and mortality rate) between the referrals were accepted and refused using propensity score matching. Of the 1759 patients eligible for transfer to regional hospitals, 420 patients (24%) accepted the referral. Medical records were obtained from the regional hospitals for 283 patients (67%). After propensity score matching, the results showed that interhospital transfer resulted in similar median total length of stay (8.7 days in the medical center vs 7.9 days in regional hospitals; P=.245). In-hospital mortality was low for both groups (3.1% in the medical center vs 1.3% in regional hospitals; P=.344). Transfer from an overcrowded ED in a medical center to regional hospitals in eligible patients results in non-significant outcome of total length of stay. With the caveat of an underpowered sample, we did not find statistically significant differences in in-hospital mortality. This healthcare delivery model may be used in other cities facing similar problems of ED overcrowding.

Provider Bias in prescribing opioid analgesics: a study of electronic medical Records at a Hospital Emergency Department

BackgroundPhysicians do not prescribe opioid analgesics for pain treatment equally across groups, and such disparities may pose significant public health concerns. Although research suggests that institutional constraints and cultural stereotypes influence doctors’ treatment of pain, prior quantitative evidence is mixed. The objective of this secondary analysis is therefore to clarify which institutional constraints and patient demographics bias provider prescribing of opioid analgesics.MethodsWe used electronic medical record data from an emergency department of a large U.S hospital during years 2008–2014. We ran multi-level logistic regression models to estimate factors associated with providing an opioid prescription during a given visit while controlling for ICD-9 diagnosis codes and between-patient heterogeneity.ResultsA total of 180,829 patient visits for 63,513 unique patients were recorded during the period of analysis. Overall, providers were significantly less likely to prescribe opioids to the same individual patient when the visit occurred during higher rates of emergency department crowding, later times of day, earlier in the week, later years in our sample, and when the patient had received fewer previous opioid prescriptions. Across all patients, providers were significantly more likely to prescribe opioids to patients who were middle-aged, white, and married. We found no bias towards women and no interaction effects between race and crowding or between race and sex.ConclusionsProviders tend to prescribe fewer opioids during constrained diagnostic situations and undertreat pain for patients from high-risk and marginalized demographic groups. Potential harms resulting from previous treatment decisions may accumulate by informing future treatment decisions.

Impact of Coronavirus Disease 2019 Pandemic on Crowding: A Call to Action for Effective Solutions to "Access Block".

IntroductionHealthcare patterns change during disease outbreaks and pandemics. Identification of modified patterns is important for future preparedness and response. Emergency department (ED) crowding can occur because of the volume of patients waiting to be seen, which results in delays in patient assessment or treatment and impediments to leaving the ED once treatment is complete. Therefore, ED crowding has become a growing problem worldwide and represents a serious barrier to healthcare operations.MethodsThis observational study was based on a retrospective review of the epidemiologic and clinical records of patients who presented to the Foundation IRCCS Policlinic San Matteo in Pavia, Italy, during the coronavirus disease 2019 (COVID-19) outbreak (February 21–May 1, 2020, pandemic group). The methods involved an estimation of the changes in epidemiologic and clinical data from the annual baseline data after the start of the COVID-19 pandemic.ResultsWe identified reduced ED visits (180 per day in the control period vs 96 per day in the pandemic period; P < 0.001) during the COVID-19 pandemic, irrespective of age and gender, especially for low-acuity conditions. However, patients who did present to the ED were more likely to be hemodynamically unstable, exhibit abnormal vital signs, and more frequently required high-intensity care and hospitalization. During the pandemic, ED crowding dramatically increased primarily because of an increased number of visits by patients with high-acuity conditions, changes in patient management that prolonged length of stay, and increased rates of boarding, which led to the inability of patients to gain access to appropriate hospital beds within a reasonable amount of time. During the pandemic, all crowding output indices increased, especially the rates of boarding (36% vs 57%; P < 0.001), “access block” (24% vs 47%; P < 0.001), mean boarding time (640 vs 1,150 minutes [min]; P 0.001), mean “access block” time (718 vs 1,223 min; P < 0.001), and “access block” total time (650,379 vs 1,359,172 min; P < 0.001).ConclusionCrowding in the ED during the COVID-19 pandemic was due to the inability to access hospital beds. Therefore, solutions to this lack of access are required to prevent a recurrence of crowding due to a new viral wave or epidemic.

Incidence and treatment of hand and wrist injuries in Dutch emergency departments

PurposeThe purpose of this study was to describe the epidemiology, treatment and costs of hand and wrist injuries presenting to the Dutch ED. With increasing medical costs and crowding of emergency departments (ED), a more detailed description of emergency department attendance of hand and wrist injuries and their treatment may help to facilitate more adequate allocation of health care services.MethodsThe Dutch Injury Surveillance System obtained a total of 160,250 hand and wrist injuries. Patient characteristics, incidence rates, type of injury, treatment, and costs were described.ResultsThe incidence of hand and wrist injuries in the Netherlands in 2016 was 11 per 1000 in males and 8 per 1000 in females. This is about 25% of all injuries presented at the ED. Of all hand injuries, only 3% was directly admitted to the hospital or received emergency surgery. Thirty percent did not need further treatment in the hospital.ConclusionThe current data suggest that a substantial proportion of the hand and wrist injuries needed no subsequent specialized treatment. Although the severity of the injury could not be deduced from our data, the data suggest a ground for a more extensive role of primary health care (general) practitioners in the primary triage and treatment of hand and wrist injuries. This may reduce health care cost and help decongest the ED departments. Prospective studies are needed to confirm these preliminary conclusions.Level of evidenceIII.