Emergency Department Disposition Decisions Research Articles

Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. We developed random forest machine learning (ML) models to estimate needs for critical care within 24h and inpatient care within 72h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Among8032 patientswith laboratory-confirmed influenza,incidence of critical care needs was 6.3% and incidence of inpatient care needswas 19.6%.Themost common reasons for ED visitwere symptoms of respiratory tract infection,fever,and shortness of breath. Model AUCswere0.89 (95% CI 0.86-0.93)for prediction of criticalcare and0.90(95% CI0.88-0.93)for inpatient care needs;Brier scores were0.026 and0.042, respectively.Importantpredictorsincludedshortness of breath,increasingrespiratory rate,and ahigh number of comorbid diseases. ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.

An Investigation into Demographic Disparities in Emergency Department Disposition Decisions

We investigate the presence of health disparities in emergency department (ED) disposition decisions and if crowding levels might have an exacerbating role. Using data from a large, academic ED, we find statistically significant associations between ED disposition decisions and patient sex, race, as well as ethnicity, with male, Caucasian, and non-Hispanic patients being more likely to be admitted to the hospital compared with, respectively, female, African-American, and Hispanic patients. In line with earlier findings in other studies, we find that longer waiting times, suggesting higher levels of ED crowding, is associated with higher rates of admission. Moreover, longer ED wait times modified sex differences, suggesting that the disposition disparity in female patients might be exacerbated when the ED is more crowded.

Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department.

Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.

Patients with complicated gallstone disease in the emergency department: clinical impact and cost-effectiveness of emergency department disposition decision.

ObjectiveThe objective of this study was to assess the 1‐year outcomes of emergency department (ED) patients with complicated gallstone disease, including surgery rates, initial admission rates, ED revisits, repeat hospitalizations, and cost.MethodsUsing 3 linked statewide databases from the Maryland Healthcare Cost and Utilization Project, we identified patients with a primary diagnosis of complicated gallstone disease treated in an ED between 2016 and 2018. We measured the healthcare use and direct costs in the ambulatory surgery, inpatient, and ED settings for 1 year after the initial ED visit. Finally, we performed a multivariate logistic regression analysis comparing initially admitted versus discharged patients.ResultsOf the 8751 patients analyzed, 86.8% were admitted to the hospital and 13.2% were discharged on their initial ED visit. Of the admitted patients, 78.7% received a cholecystectomy during the initial hospitalization plus 6.1% at a later date; of the discharged patients, 41.5% received a cholecystectomy. Admitted patients demonstrated lower recurrent gallbladder complications compared with those discharged (7.5% vs 44.5%), fewer ED revisits (4% vs 20.3%), and fewer repeat hospitalizations (4.5% vs 16.7%). Despite this, the 1‐year cost in the admitted patients was higher ($9448 vs $2933). Obesity, age, and mood disorders but not race, ethnicity, or zip code were associated with admission at initial ED visit.ConclusionsIn our single‐state analysis of ED patients with complications of gallstone disease, most patients are admitted on the initial visit and receive a cholecystectomy during that hospitalization. The discharged group had higher rates of 1‐year complications, ED revisits, and repeat hospitalizations but lower cost.

Older Adults’ Perceptions of Disposition Decisions from the Emergency Department

Patient-centered care strives to improve older adult outcomes from the emergency department (ED). Appropriate disposition decisions from the ED for older adults are becoming increasingly complex and challenging. The purpose of this study was to explore the perceptions of older adults as to their disposition from the emergency department, the decision making process, and their engagement in that process. The Three-Talk Shared Decision Making (SDM) model guided the study. A qualitative approach was used to interview seven older adults two days after being treated in the ED. Transcribed data were thematically analyzed using MAXQDA to identify codes, patterns, and themes. Analysis revealed that the Three-Talk SDM model was not being used. Participants identified only one option regarding their disposition from the ED and perceived they had little voice in decision making. They reported a variety of emotional reactions, feelings of helplessness and empathy regarding the decision making process. Three factors that participants perceived as vital to them before making a disposition decision were safety, pain relief, and a definitive diagnosis. The findings of this small sample are clinically meaningful. These older adults wanted to be heard regarding their treatment and disposition decisions. Findings indicate the need for provider education about the use of a model such as the Three -Talk SDM. Further research is needed to look at both the older adult and provider’s perception of the ED disposition decision. Additional strategies and skills are warranted to enhance shared decision making in the ED with the growing aging population.

Predicting Adverse Events Among Patients With COPD Exacerbations in the Emergency Department.

COPD exacerbations lead to excessive health care utilization, morbidity, and mortality. The Ottawa COPD Risk Scale (OCRS) was developed to predict short-term serious adverse events (SAEs) among patients in the emergency department (ED) with COPD exacerbations. We assessed the utility of the OCRS, its component elements, and other clinical variables for ED disposition decisions in a United States population. We compared the OCRS and other factors in predicting SAEs among a retrospective cohort of ED patients with COPD exacerbations. We followed subjects for 30 d, and the primary outcome, SAE, was defined as any death, admission to monitored unit, intubation, noninvasive ventilation, major procedure, myocardial infarction, or revisit with hospital admission. A total of 246 subjects (median 61-y old, 46% male, total admission rate to ward 52%) were included, with 46 (18.7%) experiencing SAEs. Median OCRS scores did not differ significantly between those with and without an SAE (difference: 0 [interquartile range 0-1)]. The OCRS predicted SAEs poorly (Hosmer-Lemeshow goodness of fit [H-L GOF] P ≤ .001, area under the receiver operating characteristic [ROC] curve 0.519). Three variables were significantly related to SAEs in our final model (H-L GOF P = .14, area under the ROC curve 0.808): Charlson comorbidity index (odds ratio [OR] 1.3 [1.1-1.5] per 1-point increase); triage venous PCO2 (OR 1.7 [1.2-2.4] per 10 mm Hg increase); and hospitalization within previous year (OR 9.1 [3.3-24.8]). The OCRS did not reliably predict SAEs in our population. We found 3 risk factors that were significantly associated with 30-d SAE in our United States ED population: triage [Formula: see text] level, Charlson comorbidity index, and hospitalization within the previous year. Further studies are needed to develop generalizable decision tools to improve safety and resource utilization for this patient population.

Trauma Response for Elderly Anticoagulated Patients: An Initiative to Reduce Trauma Resource Utilization in the Emergency Department.

Trauma centers are challenged to have appropriate criteria to identify injured patients needing a trauma activation; one population that is difficult to triage is injured elderly patients taking anticoagulation or antiplatelet (ACAP) medications with suspected head injury. The study purpose was to evaluate a hospital initiative to improve the trauma triage response for this population. A retrospective study at a Level I trauma center evaluated revised trauma response criteria. In Phase 1 (June 2017 to April 2018; n = 91), a limited activation occurred in the trauma bay for injured patients 55 years and older, taking ACAP medications with evidence of head injury. In Phase 2 (June 2018 to April 2019; n = 142), patients taking ACAP medications with evidence of head injury received a rapid emergency department (ED) response. Primary outcomes were timeliness of ED interventions and hospital admission rates. Differences between phases were assessed with Kruskal-Wallis tests. An ED rapid response significantly reduced trauma team involvement (100%-13%, p < .001). Compared with Phase 1, patients in Phase 2 were more frequently discharged from the ED (48% vs. 68%, p = .003), and ED disposition decision was made more quickly (147 vs. 120 min, p = .01). In Phase 2, time to ED disposition decision was longer for patients who required hospital admission (108 vs. 179 min, p < .001); however, there were no significant differences between phases in reversal intervention (6% vs. 11%, p = .39) or timeliness of reversal intervention (49 vs. 118 min, p = .51). The ED rapid response delivered safe, timely evaluation to injured elderly patients without overutilizing trauma team activations.

Prediction of emergency department patient disposition decision for proactive resource allocation for admission.

We investigate the capability of information from electronic health records of an emergency department (ED) to predict patient disposition decisions for reducing "boarding" delays through the proactive initiation of admission processes (e.g., inpatient bed requests, transport, etc.). We model the process of ED disposition decision prediction as a hierarchical multiclass classification while dealing with the progressive accrual of clinical information throughout the ED caregiving process. Multinomial logistic regression as well as machine learning models are built for carrying out the predictions. Utilizing results from just the first set of ED laboratory tests along with other prior information gathered for each patient (2.5h ahead of the actual disposition decision on average), our model predicts disposition decisions with positive predictive values of 55.4%, 45.1%, 56.9%, and 47.5%, while controlling false positive rates (1.4%, 1.0%, 4.3%, and 1.4%), with AUC values of 0.97, 0.95, 0.89, and 0.84 for the four admission (minor) classes, i.e., intensive care unit (3.6% of the testing samples), telemetry unit (2.2%), general practice unit (11.9%), and observation unit (6.6%) classes, respectively. Moreover, patients destined to intensive care unit present a more drastic increment in prediction quality at triage than others. Disposition decision classification models can provide more actionable information than a binary admission vs. discharge prediction model for the proactive initiation of admission processes for ED patients. Observing the distinct trajectories of information accrual and prediction quality evolvement for ED patients destined to different types of units, proactive coordination strategies should be tailored accordingly for each destination unit.

Analysis of How Emergency Physicians’ Decisions to Hospitalize or Discharge Patients With Acute Heart Failure Match the Clinical Risk Categories of the MEESSI-AHF Scale

The Multiple Estimation of Risk Based on the Emergency Department Spanish Score in Patients With Acute Heart Failure (MEESSI-AHF) is a validated clinical decision tool that characterizes risk of mortality in emergency department (ED) acute heart failure patients. The objective of this study is to compare the distribution of risk categories between hospitalized and discharged ED patients with acute heart failure. We included consecutive acute heart failure patients from 34 Spanish EDs. Patients were retrospectively classified according to MEESSI-AHF risk categories. We calculated the odds of hospitalization (versus direct discharge from the ED) across MEESSI-AHF risk categories. Next, we assessed the following 30-day postdischarge outcomes: ED revisit, hospitalization, death, and their combination. We used Cox hazards models to determine the adjusted association between ED disposition decision and the outcomes among patients who were stratified into low- and increased-risk categories. We included 7,930 patients (80.5 years [SD 10.1 years]; women 54.7%; hospitalized 75.3%). Compared with that for low-risk MEESSI-AHF patients, odds ratios for hospitalization of patients in intermediate-, high-, and very-high-risk categories were 1.83 (95% confidence interval [CI] 1.64 to 2.05), 3.05 (95% CI 2.48 to 3.76), and 3.98 (95% CI 3.13 to 5.05), respectively. However, almost half (47.6%) of all discharged patients were categorized as being at increased risk by MEESSI-AHF, and 19.0% of all the increased-risk patients were discharged from the ED. Among the low-risk MEESSI-AHF patients, the 30-day postdischarge mortality did not differ by ED disposition (hazard ratio [HR] for discharged patients with respect to hospitalized ones 0.65; 95% CI 0.70 to 1.11), nor did it differ in the increased-risk group (HR 0.88; 95% CI 0.63 to 1.23). The discharged low-risk MEESSI-AHF patients had higher risks of 30-day ED revisit and hospitalization (HR 1.86, 95% CI 1.57 to 2.20; and HR 1.92, 95% CI 1.54 to 2.40, respectively) compared with the admitted patients, as did the discharged patients in the increased-risk group (HR 1.62, 95% CI 1.39 to 1.89; and HR 1.40, 95% CI 1.16 to 1.68, respectively), with similar results for the combined endpoint. The disposition decisions made in current clinical practice for ED acute heart failure patients calibrate with MEESSI-AHF risk categories, but nearly half of the patients currently discharged from the ED fall into increased-risk MEESSI-AHF categories.

Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis.

BackgroundIn children below the age of 2 years, bronchiolitis is the most common reason for hospitalization. Each year in the United States, bronchiolitis causes 287,000 emergency department visits, 32%-40% of which result in hospitalization. Due to a lack of evidence and objective criteria for managing bronchiolitis, clinicians often make emergency department disposition decisions on hospitalization or discharge to home subjectively, leading to large practice variation. Our recent study provided the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis and showed that 6.08% of emergency department disposition decisions for bronchiolitis were inappropriate. An accurate model for predicting appropriate hospital admission can guide emergency department disposition decisions for bronchiolitis and improve outcomes, but has not been developed thus far.ObjectiveThe objective of this study was to develop a reasonably accurate model for predicting appropriate hospital admission.MethodsUsing Intermountain Healthcare data from 2011-2014, we developed the first machine learning classification model to predict appropriate hospital admission for emergency department patients with bronchiolitis.ResultsOur model achieved an accuracy of 90.66% (3242/3576, 95% CI: 89.68-91.64), a sensitivity of 92.09% (1083/1176, 95% CI: 90.33-93.56), a specificity of 89.96% (2159/2400, 95% CI: 88.69-91.17), and an area under the receiver operating characteristic curve of 0.960 (95% CI: 0.954-0.966). We identified possible improvements to the model to guide future research on this topic.ConclusionsOur model has good accuracy for predicting appropriate hospital admission for emergency department patients with bronchiolitis. With further improvement, our model could serve as a foundation for building decision-support tools to guide disposition decisions for children with bronchiolitis presenting to emergency departments.International Registered Report Identifier (IRRID)RR2-10.2196/resprot.5155

Clinical validation of a risk scale for serious outcomes among patients with chronic obstructive pulmonary disease managed in the emergency department.

The Ottawa chronic obstructive pulmonary disease (COPD) Risk Scale (OCRS), which consists of 10 criteria, was previously derived to identify patients in the emergency department with COPD who were at high risk for short-term serious outcomes. We sought to validate, prospectively and explicitly, the OCRS when applied by physicians in the emergency department. We conducted this prospective cohort study involving patients in the emergency departments at 6 tertiary care hospitals and enrolled adults with acute exacerbation of COPD from May 2011 to December 2013. Physicians evaluated patients for the OCRS criteria, which were recorded on a data form along with the total risk score. We followed patients for 30 days and the primary outcome, short-term serious outcomes, was defined as any of death, admission to monitored unit, intubation, noninvasive ventilation, myocardial infarction (MI) or relapse with hospital admission. We enrolled 1415 patients with a mean age of 70.6 (SD 10.6) years and 50.2% were female. Short-term serious outcomes occurred in 135 (9.5%) cases. Incidence of short-term serious outcomes ranged from 4.6% for a total score of 0 to 100% for a score of 10. Compared with current practice, an OCRS score threshold of greater than 1 would increase sensitivity for short-term serious outcomes from 51.9% to 79.3% and increase admissions from 45.0% to 56.6%. A threshold of greater than 2 would improve sensitivity to 71.9% with 47.9% of patients being admitted. In this clinical validation of a risk-stratification tool for COPD in the emergency department, we found that OCRS showed better sensitivity for short-term serious outcomes compared with current practice. This risk scale can now be used to help emergency department disposition decisions for patients with COPD, which should lead to a decrease in unnecessary admissions and in unsafe discharges.

Appropriateness of Hospital Admission for Emergency Department Patients with Bronchiolitis: Secondary Analysis.

BackgroundBronchiolitis is the leading cause of hospitalization in children under 2 years of age. Each year in the United States, bronchiolitis results in 287,000 emergency department visits, 32%-40% of which end in hospitalization. Frequently, emergency department disposition decisions (to discharge or hospitalize) are made subjectively because of the lack of evidence and objective criteria for bronchiolitis management, leading to significant practice variation, wasted health care use, and suboptimal outcomes. At present, no operational definition of appropriate hospital admission for emergency department patients with bronchiolitis exists. Yet, such a definition is essential for assessing care quality and building a predictive model to guide and standardize disposition decisions. Our prior work provided a framework of such a definition using 2 concepts, one on safe versus unsafe discharge and another on necessary versus unnecessary hospitalization.ObjectiveThe goal of this study was to determine the 2 threshold values used in the 2 concepts, with 1 value per concept.MethodsUsing Intermountain Healthcare data from 2005-2014, we examined distributions of several relevant attributes of emergency department visits by children under 2 years of age for bronchiolitis. Via a data-driven approach, we determined the 2 threshold values.ResultsWe completed the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis. Appropriate hospital admissions include actual admissions with exposure to major medical interventions for more than 6 hours, as well as actual emergency department discharges, followed by an emergency department return within 12 hours ending in admission for bronchiolitis. Based on the definition, 0.96% (221/23,125) of the emergency department discharges were deemed unsafe. Moreover, 14.36% (432/3008) of the hospital admissions from the emergency department were deemed unnecessary.ConclusionsOur operational definition can define the prediction target for building a predictive model to guide and improve emergency department disposition decisions for bronchiolitis in the future.

Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department.

Intensive care unit (ICU) capacity strain refers to the potential limits placed on an ICU's ability to provide high-quality care for all patients who may need it at a given time. Few studies have investigated how fluctuations in ICU capacity strain might influence care outside the ICU. To determine whether ICU capacity strain is associated with initial level of inpatient care and outcomes for emergency department (ED) patients hospitalized for sepsis. We performed a retrospective cohort study of patients with sepsis admitted from the ED to a medical ward or ICU at three hospitals within the University of Pennsylvania Health System between 2012 and 2015. Patients were excluded if they required life support therapies, defined as invasive or noninvasive ventilatory support or vasopressors, at the time of admission. The exposures were four measures of ICU capacity strain at the time of the ED disposition decision: ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy. The primary outcome was the decision to admit to a ward or to an ICU. Secondary analyses assessed the association of ICU capacity strain with in-hospital outcomes, including mortality. Among 77,142 hospital admissions from the ED, 3,067 patients met the study's eligibility criteria. The ICU capacity strain metrics varied between and within study hospitals over time. In unadjusted analyses, ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy were all negatively associated with ICU admission. In the fully adjusted model including patient-level covariates, only ICU occupancy remained associated with ICU admission (odds ratio, 0.87; 95% confidence interval, 0.79-0.96; P = 0.005), such that a 10% increase in ICU occupancy (e.g., one additional patient in a 10-bed ICU) was associated with a 13% decrease in the odds of ICU admission. Among the subset of patients admitted initially from the ED to a medical ward, ICU occupancy at the time of admission was associated with increased odds of hospital mortality (odds ratio, 1.61; 95% confidence interval, 1.21-2.14; P = 0.001). The odds that patients in the ED with sepsis who do not require life support therapies will be admitted to the ICU are reduced when those ICUs experience high occupancy but not high levels of other previously explored measures of capacity strain. Patients with sepsis admitted to the wards during times of high ICU occupancy had increased odds of hospital mortality.

Emergency Department disposition decisions and associated mortality and costs in ICU patients with suspected infection

BackgroundFollowing emergency department (ED) assessment, patients with infection may be directly admitted to the intensive care unit (ICU) or alternatively admitted to hospital wards or sent home. Those admitted to the hospital wards or sent home may experience future deterioration necessitating ICU admission.MethodsWe used a prospectively collected registry from two hospitals within a single tertiary care hospital network between 2011 and 2014. Patient information, outcomes, and costs were stored in the hospital data warehouse. Patients were categorized into three groups: (1) admitted directly from the ED to the ICU; (2) initially admitted to the hospital wards, with ICU admission within 72 hours of initial presentation; or (3) sent home from the ED, with ICU admission within 72 hours of initial presentation. Using multivariable logistic regression, we sought to compare outcomes and total costs between groups. Total costs were evaluated using a generalized linear model.ResultsA total of 657 patients were included; of these, 338 (51.4%) were admitted directly from the ED to the ICU, 246 (37.4%) were initially admitted to the wards and then to the ICU, and 73 (11.1%) were initially sent home and then admitted to the ICU. In-hospital mortality was lowest among patients admitted directly to the ICU (29.5%), as compared with patients admitted to the ICU from wards (42.7%) or home (61.6%) (P < 0.001). As compared with direct ICU admission, disposition to the ward was associated with an adjusted OR of 1.75 (95% CI, 1.22–2.50; P < 0.01) for mortality, and disposition home was associated with an adjusted OR of 4.02 (95% CI, 2.32–6.98). Mean total costs were lowest among patients directly admitted to the ICU ($26,748), as compared with those admitted from the wards ($107,315) and those initially sent home ($71,492) (P < 0.001). Cost per survivor was lower among patients directly admitted to the ICU ($37,986) than either those initially admitted to the wards ($187,230) or those sent home ($186,390) (P < 0.001).ConclusionsIn comparison with direct admission to the ICU, patients with suspected infection admitted to the ICU who have previously been discharged home or admitted to the ward are associated with higher in-hospital mortality and costs.

Literature Review: Prediction Modeling of Emergency Department Disposition Decisions for Children with Acute Asthma Exacerbations

Emergency department (ED) visits and hospitalization are common, costly and the greatest disease burden to children with asthma. Admission rates for asthma vary substantially by site and ED provider. Disease heterogeneity and ED provider uncertainty surrounding likelihood of successful discharge contribute to potentially avoidable hospitalizations. New approaches to improve ED disposition decision-making could improve patient care and reduce waste associated with unnecessary hospitalizations. Several asthma severity scores and asthma prediction models have been evaluated for their ability to predict ED disposition for childhood asthma. Our objective is to review prior studies that modeled predictors of disposition outcomes for children treated in ED settings for acute asthma exacerbations.

MP32: The Pulmonary Embolism Severity Index (PESI) score and disposition decisions in Calgary emergency departments

Introduction: The Pulmonary Embolism Severity Index (PESI) score predicts short-term mortality from pulmonary embolism and low-risk patients suitable for home therapy. However, it is unknown if it is a driver for disposition decisions for emergency department (ED) patients. The primary objective of this study was to define the relationship between disposition decisions and the PESI score in Calgary zone hospitals. Methods: The PESI score was calculated retrospectively for 576 patients presenting to one of four Calgary zone hospitals for pulmonary embolism over the last 2 years. The calculated PESI score allowed the mortality risk of each patient to be estimated for very low risk (Class I, 0-1.6% 30-day mortality rate), low risk (Class II, 1.7-3.5% 30-day mortality rate), intermediate risk (Class III, 3.2-7.1% 30-day mortality rate), high risk (Class IV, 4.0-11.4% 30-day mortality rate), and very high risk (Class V, 10.0- 24.5% 30- day mortality rate). The patients were grouped based on being admitted to the hospital for inpatient care, or discharged for outpatient care. Descriptive statistics were used to describe the data. Results: Of the 576 patients, 317 (55%) were discharged and 259 (45%) were admitted to the hospital for inpatient care. Among admitted patients, 20.5% were considered Class I, 29.3% were Class II, 24.3% were Class III, 17.6% were Class IV, and 8.1% were Class V. Among discharged patients, 53.9 % were Class I, 25.6% were Class II, 15.5% were Class III, 4.4% were Class IV, and 0.6% were Class V. Of the 25 very high-risk (Class V) patients, 2 (8.0%) were discharged from the ED and treated as outpatients. Of the 223 very low risk (Class I) patients, 171 (76.7%) were discharged and 52 (23.3%) were admitted to hospital. Conclusion: A significant percentage of pulmonary embolism patients admitted to Calgary Zone hospital wards are PESI low risk (29.3%) or very low risk (20.5%). Implementation of a PESI score-based disposition pathway could improve the safety, cost-effectiveness and quality of ED disposition decisions for PE.

Patterns of Emergency Department Use Among Long-Stay Nursing Home Residents With Differing Levels of Dementia Severity

ObjectivesTo describe emergency department (ED) utilization among long-stay nursing home residents with different levels of dementia severity. DesignRetrospective cohort study. SettingPublic Health System. ParticipantsA total of 4491 older adults (age 65 years and older) who were long-stay nursing home residents. MeasurementsPatient demographics, dementia severity, comorbidities, ED visits, ED disposition decisions, and discharge diagnoses. ResultsForty-seven percent of all long-stay nursing home residents experienced at least 1 transfer to the ED over the course of a year. At their first ED transfer, 36.4% of the participants were admitted to the hospital, whereas 63.1% of those who visited the ED were not. The median time to first ED visit for the participants with advanced stage dementia was 258 days, whereas it was 250 days for the participants with early to moderate stage dementia and 202 days for the participants with no dementia (P = .0034). Multivariate proportional hazard modeling showed that age, race, number of comorbidities, number of hospitalizations in the year prior, and do not resuscitate status all significantly influenced participants’ time to first ED visit (P < .05 for all). After accounting for these effects, dementia severity (P = .66), years in nursing home before qualification (P = .46), and gender (P = .36) lost their significance. ConclusionsThis study confirms high rates of transfer of long-stay nursing home residents, with nearly one-half of the participants experiencing at least 1 ED visit over the course of a year. Although dementia severity is not a predictor of time to ED use in our analyses, other factors that influence ED use are readily identifiable. Nursing home providers should be aware of these factors when developing strategies that meet patient care goals and avoid transfer from the nursing home to the ED.

Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods.

BackgroundIn young children, bronchiolitis is the most common illness resulting in hospitalization. For children less than age 2, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the United States, 287,000 emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32%-40%. Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital admission or discharge to home) are often made subjectively, resulting in significant practice variation. Studies reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited health care resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen outcomes. Existing clinical guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient level of detail, and do not account for differences in patient and illness characteristics including co-morbidities. Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and improve clinicians’ decision making. Used in real time, predictive models can present objective criteria supported by historical data for an individualized disease management plan and guide admission decisions. However, existing predictive models for ED patients with bronchiolitis have limitations, including low accuracy and the assumption that the actual ED disposition decision was appropriate. To date, no operational definition of appropriate admission exists. No model has been built based on appropriate admissions, which include both actual admissions that were necessary and actual ED discharges that were unsafe.ObjectiveThe goal of this study is to develop a predictive model to guide appropriate hospital admission for ED patients with bronchiolitis.MethodsThis study will: (1) develop an operational definition of appropriate hospital admission for ED patients with bronchiolitis, (2) develop and test the accuracy of a new model to predict appropriate hospital admission for an ED patient with bronchiolitis, and (3) conduct simulations to estimate the impact of using the model on bronchiolitis outcomes.ResultsWe are currently extracting administrative and clinical data from the enterprise data warehouse of an integrated health care system. Our goal is to finish this study by the end of 2019.ConclusionsThis study will produce a new predictive model that can be operationalized to guide and improve disposition decisions for ED patients with bronchiolitis. Broad use of the model would reduce iatrogenic risk, patient and parental distress, health care use, and costs and improve outcomes for bronchiolitis patients.

Acute Abdomen in the Emergency Department: Is CT a Time-Limiting Factor?

The purpose of this study was to quantify and integrate key emergency department (ED) and radiology department workflow time intervals within the ED length of stay (LOS) for patients presenting with acute abdomen who require CT. An 11-month retrospective review was performed of all patients presenting to the ED with an acute abdomen who required abdominal CT. Nine key time points associated with ED LOS and CT workflow were collected: triage, physician assessment, CT request, porter schedule, CT start, CT complete, provision of first CT report, ED disposition decision, and physical discharge. The median and 90th percentile times for each interval were reported. Ninety-six percent (2194/2292) of ED encounters during the study period met the inclusion criteria. The median ED LOS was 9.22 hours (90th percentile, 15.7 hours). Intervals associated with CT workflow accounted for 29% of the total LOS. Radiology turnaround time accounted for 32% of the entire CT workflow interval. Timeline analysis found three unique patterns of ED disposition: disposition after initial imaging report, disposition before report, and disposition before CT. To our knowledge, this study is the first to quantify the contribution of CT-related workflow time intervals within the context of ED LOS. We have shown that patients do not have identical ED transit pathways, and this may under- or overestimate time interval calculations. These results show the importance of site-specific ED LOS timeline analysis to identify potential targets for quality improvement and serve as baseline targets for measuring future quality improvement initiatives.

Factors associated with decision to hospitalize emergency department patients with skin and soft tissue infection.

IntroductionEmergency department (ED) hospitalizations for skin and soft tissue infection (SSTI) have increased, while concern for costs has grown and outpatient parenteral antibiotic options have expanded. To identify opportunities to reduce admissions, we explored factors that influence the decision to hospitalize an ED patient with a SSTI.MethodsWe conducted a prospective study of adults presenting to 12 U.S. EDs with a SSTI in which physicians were surveyed as to reason(s) for admission, and clinical characteristics were correlated with disposition. We employed chi-square binary recursive partitioning to assess independent predictors of admission. Serious adverse events were recorded.ResultsAmong 619 patients, median age was 38.7 years. The median duration of symptoms was 4.0 days, 96 (15.5%) had a history of fever, and 46 (7.5%) had failed treatment. Median maximal length of erythema was 4.0cm (IQR, 2.0–7.0). Upon presentation, 39 (6.3%) had temperature >38°C, 81 (13.1%) tachycardia, 35 (5.7%), tachypnea, and 5 (0.8%) hypotension; at the time of the ED disposition decision, these findings were present in 9 (1.5%), 11 (1.8%), 7 (1.1%), and 3 (0.5%) patients, respectively. Ninety-four patients (15.2%) were admitted, 3 (0.5%) to the intensive care unit (ICU). Common reasons for admission were need for intravenous antibiotics in 80 (85.1%; the only reason in 41.5%), surgery in 23 (24.5%), and underlying disease in 11 (11.7%). Hospitalization was significantly associated with the following factors in decreasing order of importance: history of fever (present in 43.6% of those admitted, and 10.5% discharged; maximal length of erythema >10cm (43.6%, 11.3%); history of failed treatment (16.1%, 6.0%); any co-morbidity (61.7%, 27.2%); and age >65 years (5.4%, 1.3%). Two patients required amputation and none had ICU transfer or died.ConclusionED SSTI patients with fever, larger lesions, and co-morbidities tend to be hospitalized, almost all to non-critical areas and rarely do they suffer serious complications. The most common reason for admission is administration of intravenous antibiotics, which is frequently the only reason for hospitalization. With the increasing outpatient intravenous antibiotic therapy options, these results suggest that many hospitalized patients with SSTI could be managed safely and effectively as outpatients.