Evaluating the transitions in care for children presenting with acute asthma to emergency departments: a retrospective cohort study

Background Acute asthma is a common presentation to emergency departments (EDs) worldwide and, due to overcrowding, delays in treatment often occur. This study deconstructs the total ED length of stay into stages and estimates covariate effects on transition times for children presenting with asthma. Methods We extracted ED presentations in 2019 made by children in Alberta, Canada for acute asthma. We used multivariable Cox regressions in a multistate model to model transition times among the stages of start, physician initial assessment (PIA), disposition decision, and ED departure. Results Data from 6598 patients on 8270 ED presentations were extracted. The individual PIA time was longer (i.e., HR < 1) when time to the crowding metric (hourly PIA) was above 1 h (HR = 0.32; 95% CI:0.30,0.34), for tertiary (HR = 0.65; 95% CI:0.61,0.70) and urban EDs (HR = 0.77; 95% CI:0.70,0.84), for younger patients (HR = 0.99 per year; 95% CI:0.99,1.00), and for patients triaged less urgent/non-urgent (HR = 0.89; 95% CI:0.84,0.95). It was shorter for patients arriving by ambulance (HR = 1.22; 95% CI:1.04,1.42). Times from PIA to disposition decision were longer for tertiary (HR = 0.47; 95% CI:0.44,0.51) and urban (HR = 0.69; 95% CI:0.63,0.75) EDs, for patients triaged as resuscitation/emergent (HR = 0.51; 95% CI:0.48,0.54), and for patients arriving by ambulance (HR = 0.78; 95% CI:0.70,0.87). Times from disposition decision to ED departure were longer for patients who were admitted (HR = 0.16; 95% CI:0.13,0.20) or transferred (HR = 0.42; 95% CI:0.35,0.50), and for tertiary EDs (HR = 0.93; 95% CI:0.92,0.94). Conclusions All transition times were impacted by ED presentation characteristics. The sole key patient characteristic was age and it only impacted time to PIA. ED crowding demonstrated strong effects of time to PIA but not for the transition times involving disposition decision and ED departure stages.

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

Background The 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. Methods This 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. Results Of 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). Conclusions Four 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.

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.

Associations between emergency department crowding and perceptions of interpersonal care in patients presenting with suspected acute coronary syndrome

BackgroundEmergency department (ED) crowding is associated with numerous healthcare issues, but little is known about its effect on psychosocial aspects of patient-provider interactions or interpersonal care. We examined whether ED crowding was associated with perceptions of interpersonal care in patients evaluated for acute coronary syndrome (ACS).MethodsPatients presenting to a quaternary academic medical centre ED in New York City for evaluation of suspected ACS were enrolled between November 2013 and December 2016. ED crowding was measured using the ED Work Index (EDWIN), which incorporates patient volume, triage category, physician staffing and bed availability. Patients completed the 18-item Interpersonal Processes of Care (IPC) survey, which assesses communication, patient-centred decision-making and interpersonal style. Regression analyses examined associations between EDWIN and IPC scores, adjusting for demographics, comorbidities and depression.ResultsAmong 933 included patients, 11% experienced ED overcrowding (EDWIN score >2) at admission, 11% experienced ED overcrowding throughout the ED stay and 30% reported suboptimal interpersonal care (defined as per-item IPC score <5). Higher admission EDWIN score was associated with modestly lower IPC score in both unadjusted (β=–1.70, 95% CI –3.15 to –0.24, p=0.02) and adjusted models (β = –1.77, 95% CI –3.31 to –0.24, p=0.02). EDWIN score averaged over the entire ED stay was not significantly associated with IPC score (unadjusted β=–1.30, 95% CI –3.19 to 0.59, p=0.18; adjusted β=–1.24, 95% CI –3.21 to 0.74, p=0.22).ConclusionIncreased crowding at the time of ED admission was associated with poorer perceptions of interpersonal care among patients with suspected ACS.

Relationship between emergency department and inpatient occupancy and the likelihood of an emergency admission: a retrospective hospital database study

BackgroundWe investigate whether admission from a consultant-led ED is associated with ED occupancy or crowding and inpatient (bed) occupancy.MethodsWe used general additive logistic regression to explore the relationship between the probability of an ED patient being admitted, ED crowding and inpatient occupancy levels. We adjust for patient, temporal and attendance characteristics using data from 13 English NHS Hospital Trusts in 2019. We define quintiles of occupancy in ED and for four types of inpatients: emergency, overnight elective, day case and maternity.ResultsCompared with periods of average occupancy in ED, a patient attending during a period of very high (upper quintile) occupancy was 3.3% less likely (relative risk (RR) 0.967, 95% CI 0.958 to 0.977) to be admitted, whereas a patient arriving at a time of low ED occupancy was 3.9% more likely (RR 1.039 95% CI 1.028 to 1.050) to be admitted. When the number of overnight elective, day-case and maternity inpatients reaches the upper quintile then the probability of admission from ED rises by 1.1% (RR 1.011 95% CI 1.001 to 1.021), 3.8% (RR 1.038 95% CI 1.025 to 1.051) and 1.0% (RR 1.010 95% CI 1.001 to 1.020), respectively. Compared with periods of average emergency inpatient occupancy, a patient attending during a period of very high emergency inpatient occupancy was 1.0% less likely (RR 0.990 95% CI 0.980 to 0.999) to be admitted and a patient arriving at a time of very low emergency inpatient occupancy was 0.8% less likely (RR 0.992 95% CI 0.958 to 0.977) to be admitted.ConclusionsAdmission thresholds are modestly associated with ED and inpatient occupancy when these reach extreme levels. Admission thresholds are higher when the number of emergency inpatients is particularly high. This may indicate that riskier discharge decisions are taken when beds are full. Admission thresholds are also high when pressures within the hospital are particularly low, suggesting the potential to safely reduce avoidable admissions.

The Effects of Implementing a “Waterfall” Emergency Physician Attending Schedule

Introduction: Increases in emergency department (ED) crowding and boarding are a nationwide issue resulting in worsening patient care and throughput. To compensate, ED administrators often look to modifying staffing models to improve efficiencies. Methods: This study evaluates the impact of implementing the waterfall model of physician staffing on door-to-doctor time (DDOC), door-to-disposition time (DDIS), left without being seen (LWBS) rate, elopement rate, and the number of patient sign-outs. We examined 9,082 pre-intervention ED visits and 8,983 post-intervention ED visits. Results: The change in DDOC, LWBS rate, and elopement rate demonstrated statistically significant improvement from a mean of 65.1 to 35 minutes (P <0.001), 1.12% to 0.92% (P = 0.004), and 3.96% to 1.95% (P <0.001), respectively. The change in DDIS from 312 to 324.7 minutes was not statistically significant (P = 0.310). The number of patient sign-outs increased after the implementation of a waterfall schedule (P <0.001). Conclusion: Implementing a waterfall schedule improved DDOC time while decreasing the percentage of patients who LWBS and eloped. The DDIS and number of patient sign-outs appears to have increased post implementation, although this may have been confounded by the increase in patient volumes and ED boarding from the pre- to post-intervention period.

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

Introduction: Healthcare 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. Methods: This 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. Results: We 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). Conclusion: Crowding 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.

THE EFFECT OF A BOARDING RESTRICTION PROTOCOL ON EMERGENCY DEPARTMENT CROWDING

ABSTRACT Background Access block due to a lack of hospital beds causes emergency department (ED) crowding. We initiated the boarding restriction protocol that limits ED length of stay (LOS) for patients awaiting hospitalization to 24 hours from arrival. This study aimed to determine the effect of the protocol on ED crowding. Method This was a pre-post comparative study to compare ED crowding before and after protocol implementation. The primary outcome was the red stage fraction with more than 71 occupying patients in the ED (severe crowding level). LOS in the ED, treatment time and boarding time were compared. Additionally, the pattern of boarding patients staying in the ED according to the day of the week was confirmed. Results Analysis of the number of occupying patients in the ED, measured at 10-minute intervals, indicated a decrease from 65.0 (51.0-79.0) to 55.0 (43.0-65.0) in the pre- and post-periods, respectively (p<0.0001). The red stage fraction decreased from 38.9% to 15.1% of the pre- and post-periods, respectively (p<0.0001). The proportion beyond the goal of this protocol of 24 hours decreased from 7.6% to 4.0% (p<0.0001). The ED LOS of all patients was similar: 238.2 (134.0-465.2) and 238.3 (136.9-451.2) minutes in the pre- and post-periods, respectively. In admitted patients, ED LOS decreased from 770.7 (421.4-1587.1) to 630.2 (398.0-1156.8) minutes (p<0.0001); treatment time increased from 319.6 (198.5-482.8) to 344.7 (213.4-519.5) minutes (p<0.0001); and boarding time decreased from 298.9 (109.5-1149.0) to 204.1 (98.7-545.7) minutes (p<0.0001). In the pre-period, boarding patients accumulated in the ED on weekdays, with the accumulation resolved on Fridays; this pattern was alleviated in the post-period. Conclusions The protocol effectively resolved excessive ED crowding by alleviating the accumulation of boarding patients in the ED on weekdays. Additional studies should be conducted on changes this protocol brings to patient flow hospital-wide.

Effects of Emergency Department Crowding and Time to Antibiotics in Pneumonia

Objective: To evaluate the relationship between emergency department (ED) crowding and time to antibiotic treatment in pneumonia patients. The secondary objective was to look for other factors related to delayed antibiotic treatment. Materials and Methods: The present study was a retrospective medical chart review between February 1 and June 30, 2015 of the patients aged 18 years and older with the ED diagnoses of pneumonia. The present study was performed in the ED of a tertiary care teaching hospital. One hundred seventy patients met the enrollment criteria. The patients were divided into ED crowded or non-crowded using the National Emergency Department Overcrowding Study tool for the main outcome of ED crowding and time to antibiotic treatment in pneumonia. Results: In the 170 pneumonia patients, 117 patients (68.8%) came to the ED during a crowded shift. The characteristics of the patients were similar in both the crowded and non-crowded shifts. Of the 170 pneumonia patients, 51.8% had CURB-65 scores of 1 or 2. Patients who came to the ED during the crowded shift and non-crowded shift received antibiotics at the median times of 125 and 110 minutes, respectively (p=0.125). Delayed antibiotic treatments of more than four hours occurred in 19 patients (16.2%) during the crowded shift and in three patients (5.7%) in the non-crowded shift (p=0.098). Other factors related to time to antibiotics were the first doctor to see the patient (p=0.05), severity of disease (p<0.01), and admission type (p=0.01). Conclusion: ED crowding was not related to time to antibiotic treatment in pneumonia patients. However, if the clinical conditions of the patients looked severe or the doctor who cared for the patients was an emergency medicine resident, the patients received early administration of antibiotics. Keywords: Emergency department crowding, Pneumonia, Time to antibiotics

Emergency department crowding and mortality in 14 Swedish emergency departments, a cohort study leveraging the Swedish Emergency Registry (SVAR)

Objectives There is evidence that emergency department (ED) crowding is associated with increased mortality, however large multicenter studies of high quality are scarce. In a prior study, we introduced a proxy-measure for crowding that was associated with increased mortality. The national registry SVAR enables us to study the association in a more heterogenous group of EDs with more recent data. The aim is to investigate the association between ED crowding and mortality. Methods This was an observational cohort study including visits from 14 EDs in Sweden 2015–2019. Crowding was defined as the mean ED-census divided with expected ED-census during the work-shift that the patient arrived. The crowding exposure was categorized in three groups: low, moderate and high. Hazard ratios (HR) for mortality within 7 and 30 days were estimated with a cox proportional hazards model. The model was adjusted for age, sex, triage priority, arrival hour, weekend, arrival mode and chief complaint. Subgroup analysis by county and for admitted patients by county were performed. Results 2,440,392 visits from 1,142,631 unique patients were analysed. A significant association was found between crowding and 7-day mortality but not with 30-day mortality. Subgroup analysis also yielded mixed results with a clear association in only one of the three counties. The estimated HR (95% CI) for 30-day mortality for admitted patients in this county was 1.06 (1.01–1.12) in the moderate crowding category, and 1.11 (1.01–1.22) in the high category. Conclusions The association between crowding and mortality may not be universal. Factors that influence the association between crowding and mortality at different EDs are still unknown but a high hospital bed occupancy, impacting admitted patients may play a role.