The Clinical Emergency Data Registry: Structure, Use, and Limitations for Research.

The Clinical Emergency Data Registry (CEDR) is a qualified clinical data registry that collects data from participating emergency departments (EDs) in the United States for quality measurement, improvement, and reporting purposes. This article aims to provide an overview of the data collection and validation process, describe the existing data structure and elements, and explain the potential opportunities and limitations for ongoing and future research use. CEDR data are primarily collected for quality reporting purposes and are obtained from diverse sources, including electronic health records and billing data that are de-identified and stored in a secure, centralized database. The CEDR data structure is organized around clinical episodes, which contain multiple data elements that are standardized using common data elements and are mapped to established terminologies to enable interoperability and data sharing. The data elements include patient demographics, clinical characteristics, diagnostic and treatment procedures, and outcomes. Key limitations include the limited generalizability due to the selective nature of participating EDs and the limited validation and completeness of data elements not currently used for quality reporting purposes, including demographic data. Nonetheless, CEDR holds great potential for ongoing and future research in emergency medicine due to its large-volume, longitudinal, near real-time, clinical data. In 2021, the American College of Emergency Physicians authorized the transition from CEDR to the Emergency Medicine Data Institute, which will catalyze investments in improved data quality and completeness for research to advance emergency care.

Patient sex, racial and ethnic disparities in emergency department triage: A multi-site retrospective study.

OBJECTIVES: There is limited evidence on sex, racial, and ethnic disparities in Emergency Department (ED) triage across diverse settings. We evaluated differences in the assignment of Emergency Severity Index (ESI) by patient sex and race/ethnicity, accounting for age, clinical factors, and ED operating conditions. METHODS: We conducted a multi-site retrospective study of adult patients presenting to high-volume EDs from January 2019-February 2020. Patient-level data were obtained and analyzed from three EDs (academic, metropolitan community, and rural community) affiliated with a large health system in the Southeastern United States. For the study outcome, ESI levels were grouped into three categories: 1-2 (highest acuity), 3, and 4-5 (lowest acuity). Multinomial logistic regression was used to compare ESI categories by patient race/ethnicity and sex jointly (referent = White males), adjusted for patient age, insurance status, ED arrival mode, chief complaint category, comorbidity score, time of day, day of week, and average ED wait time. RESULTS: We identified 186,840 eligible ED visits with 56,417 from the academic ED, 69,698 from the metropolitan community ED, and 60,725 from the rural community ED. Patient cohorts between EDs varied by patient age, race/ethnicity, and insurance status. The majority of patients were assigned ESI 3 in the academic and metropolitan community EDs (61% and 62%, respectively) whereas 47% were assigned ESI 3 in the rural community ED. In adjusted analyses, White females were less likely to be assigned ESI 1-2 compared to White males although both groups were roughly comparable in the assignment of ESI 4-5. Non-White and Hispanic females were generally least likely to be assigned ESI 1-2 in all EDs. Interactions between ED wait time and race/ethnicity-sex were not statistically significant. CONCLUSIONS: This retrospective study of adult ED patients revealed sex and race/ethnicity-based differences in ESI assignment, after accounting for age, clinical factors, and ED operating conditions. These disparities persisted across three different large EDs, highlighting the need for ongoing research to address inequities in ED triage decision-making and associated patient-centered outcomes.

The effects of emergency department crowding on triage and hospital admission decisions.

BACKGROUND: Emergency department (ED) crowding is a recognized issue and it has been suggested that it can affect clinician decision-making. OBJECTIVES: Our objective was to determine whether ED census was associated with changes in triage or disposition decisions made by ED nurses and physicians. METHODS: We performed a retrospective study using one year of data obtained from a US academic center ED (65,065 patient encounters after cleaning). Using a cumulative logit model, we investigated the association between a patient's acuity group (low, medium, and high) and ED census at triage time. We also used multivariate logistic regression to investigate the association between the disposition decision for a patient (admit or discharge) and the ED census at the disposition decision time. In both studies, control variables included census, age, gender, race, place of treatment, chief complaint, and certain interaction terms. RESULTS: We found statistically significant correlation between ED census and triage/disposition decisions. For each additional patient in the ED, the odds of being assigned a high acuity versus medium or low acuity at triage is 1.011 times higher (95% confidence interval [CI] for Odds Ratio [OR] = [1.009,1.012]), and the odds of being assigned medium or high acuity versus low acuity at triage is 1.009 times higher (95% CI for OR = [1.008,1.010]). Similarly, the odds of being admitted versus discharged increases by 1.007 times (95% CI for OR = [1.006,1.008]) per additional patient in the ED at the time of disposition decision. CONCLUSION: Increased ED occupancy was found to be associated with more patients being classified as higher acuity as well as higher hospital admission rates. As an example, for a commonly observed patient category, our model predicts that as the ED occupancy increases from 25 to 75 patients, the probability of a patient being triaged as high acuity increases by about 50% and the probability of a patient being categorized as admit increases by around 25%.

Comparison of emergency department crowding scores: a discrete-event simulation approach.

According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. Using data from a large academic hospital in North Carolina, we evaluate three crowding scores, namely, EDWIN, NEDOCS, and READI by assessing strengths and weaknesses of each score, particularly their predictive power. We perform these evaluations by first building a discrete-event simulation model of the ED, validating the results of the simulation model against observations at the ED under consideration, and utilizing the model results to investigate each of the three ED crowding scores under normal operating conditions and under two simulated outbreak scenarios in the ED. We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.

Impact of an Antibiotic-specific Sepsis Bundle on Appropriate and Timely Antibiotic Administration for Severe Sepsis in the Emergency Department.

BACKGROUND: Guidelines recommend initiation of appropriate antimicrobial therapy within 1 h of severe sepsis diagnosis. Few sepsis bundles exist in the literature emphasizing initiation of specific antibiotic therapy. OBJECTIVE: To determine the impact of an antibiotic-specific sepsis bundle on the timely initiation of appropriate antibiotics. METHODS: For this before-and-after interventional study, the sepsis bundle at this 803-bed academic tertiary-care facility was redesigned to include specific antibiotic selection and dosing, based on suspected source of infection and susceptibility patterns. Protocol education and advertising was completed and bundle-specific antibiotics were put in the automated medication cabinet. RESULTS: Stepwise analysis of timely initiation of appropriate antibiotics included: 1) Was the initial antibiotic appropriate? 2) If so, was it initiated within 1 h of diagnosis? 3) If so, were all necessary appropriate antibiotics started? and 4) If so, were they started within 3 h of diagnosis? In comparing the 3-month-before group and 3-month-after group (n = 124), the appropriate initial antibiotic was started in 33.9% vs. 54.8% of patients (odds ratio [OR] 0.42, 95% confidence interval [CI] 0.19-0.93, p = 0.03) and within 1 h in 22.6% vs. 14.5% of patients (OR 1.71, 95% CI 0.62-4.92, p = 0.36), respectively. All necessary appropriate antibiotics were initiated in 16.1% vs. 12.9% of patients (OR 1.30, 95% CI 0.42-4.10, p = 0.80), and within 3 h in 14.5% vs. 9.7% of patients, respectively (OR 1.58, 95% CI 0.46-5.78, p = 0.58). CONCLUSIONS: An updated antibiotic-specific sepsis bundle, with antibiotics put in an automated medication cabinet, can result in improvements in the initiation of appropriate initial antibiotic therapy for severe sepsis in the emergency department.

Reflect urine culture cancellation in the emergency department.

BACKGROUND: The yield of urine culture testing in the emergency department (ED) is often low, resulting in wasted laboratory and ED resources. Use of a reflex culture cancellation protocol, in which urine cultures are canceled when automated urinalysis results predict that culture yield will be low, may help to conserve these resources. STUDY OBJECTIVES: To identify a reflex culture cancellation protocol consisting of urinalysis-based criteria to limit urine culture over-utilization. METHODS: We studied patients aged 5 years and older whose ED evaluation included both an automated urinalysis and urine culture. Logistic regression models incorporating individual urinalysis components were used to predict culture growth. Receiver operating characteristic curves corresponding to each model were constructed, and the area under the curve was used to identify the model that best predicted positive urine culture growth. RESULTS: There were 1546 ED patients who met study inclusion criteria. Of these, 314 (20%) had positive urine cultures. Restriction of culture testing to samples with white blood cells > 10 per high-power field, positive nitrites, positive leukocyte esterase, or positive bacteria provided a sensitivity of 96.5% (95% confidence interval [CI] 93.6-98.1%) and specificity of 48.1% (95% CI 45.3-51.0%) for positive urine culture. Implementation of a reflex culture cancellation protocol based on these criteria would have eliminated 604 of 1546 cultures (39%); 11 of 314 positive cultures (3.5%) would have been missed. CONCLUSION: These results suggest that a substantial reduction in urine culture testing might be achievable by implementing this protocol. Confirmation of these findings in a validation cohort is necessary.

Optimizing emergency department front-end operations.

As administrators evaluate potential approaches to improve cost, quality, and throughput efficiencies in the emergency department (ED), "front-end" operations become an important area of focus. Interventions such as immediate bedding, bedside registration, advanced triage (triage-based care) protocols, physician/practitioner at triage, dedicated "fast track" service line, tracking systems and whiteboards, wireless communication devices, kiosk self check-in, and personal health record technology ("smart cards") have been offered as potential solutions to streamline the front-end processing of ED patients, which becomes crucial during periods of full capacity, crowding, and surges. Although each of these operational improvement strategies has been described in the lay literature, various reports exist in the academic literature about their effect on front-end operations. In this report, we present a review of the current body of academic literature, with the goal of identifying select high-impact front-end operational improvement solutions.

A Patient Reported Approach to Identify Medical Errors and Improve Patient Safety in the Emergency Department.

OBJECTIVE: Medical errors in the emergency department (ED) occur frequently. Yet, common adverse event detection methods, such as voluntary reporting, miss 90% of adverse events. Our objective was to demonstrate the use of patient-reported data in the ED to assess patient safety, including medical errors. METHODS: Analysis of patient-reported survey data collected over a 1-year period in a large, academic emergency department. All patients who provided a valid e-mail or cell phone number received a brief electronic survey within 24 hours of their ED encounter by e-mail or text message with Web link. Patients were asked about ED safety-related processes. RESULTS: From Aug 2012 to July 2013, we sent 52,693 surveys and received 7103 responses (e-mail response rate 25.8%), including 2836 free-text comments (44% of respondents). Approximately 242 (8.5%) of 2836 comments were classified as potential safety issues, including 12 adverse events, 40 near-misses, 23 errors with minimal risk of harm, and 167 general safety issues (eg, gaps in care transitions). Of the 40 near misses, 35 (75.0%) of 40 were preventable. Of the 52 adverse events or near misses, 5 (9.6%) were also identified via an existing patient occurrence reporting system. CONCLUSIONS: A patient-reported approach to assess ED-patient safety yields important, complementary, and potentially actionable safety information.

Efficacy of High-Sensitivity Troponin T in Identifying Very-Low-Risk Patients With Possible Acute Coronary Syndrome.

Importance: Physicians need information on how to use the first available high-sensitivity troponin (hsTnT) assay in the United States to identify patients at very low risk for 30-day adverse cardiac events (ACE). Objective: To determine whether a negative hsTnT assay at 0 and 3 hours following emergency department presentation could identify patients at less than 1% risk of a 30-day ACE. Design, Setting, and Participants: A prospective, observational study at 15 emergency departments in the United States between 2011 and 2015 that included individuals 21 years and older, presenting to the emergency department with suspected acute coronary syndrome. Of 1690 eligible individuals, 15 (no cardiac troponin T measurement) and 320 (missing a 0-hour or 3-hour sample) were excluded from the analyses. Exposures: Serial hsTnT measurements (fifth-generation Roche Elecsys hsTnT assay). Main Outcomes and Measures: Serial blood samples from each patient were collected after emergency department presentation (once identified as a potential patient with acute coronary syndrome) and 3 hours, 6 to 9 hours, and 12 to 24 hours later. Adverse cardiac events were defined as myocardial infarction, urgent revascularization, or death. The upper reference level for the hsTnT assay, defined as the 99th percentile, was established as 19 ng/L in a separate healthy US cohort. Patients were considered ruled out for acute myocardial infarction if their hsTnT level at 0 hours and 3 hours was less than the upper reference level. Gold standard diagnoses were determined by a clinical end point committee. Evaluation of assay clinical performance for acute myocardial infarction rule-out was prespecified; the hypothesis regarding 30-day ACE was formulated after data collection. Results: In 1301 healthy volunteers (50.4% women; median age, 48 years), the upper reference level was 19 ng/L. In 1600 patients with suspected acute coronary syndrome (48.4% women; median age, 55 years), a single hsTnTlevel less than 6 ng/L at baseline had a negative predictive value for AMI of 99.4%. In 974 patients (77.1%) with both 0-hour and 3-hour hsTnT levels of 19 ng/L or less, the negative predictive value for 30-day ACE was 99.3% (95% CI, 99.1-99.6). Using sex-specific cutpoints, C statistics for women (0.952) and men (0.962) were similar for acute myocardial infarction. Conclusions and Relevance: A single hsTnT level less than 6 ng/L was associated with a markedly decreased risk of AMI, while serial levels at 19 ng/L or less identified patients at less than 1% risk of 30-day ACE.