Abstract
Objective:
Quality of care delivered to adult patients in the emergency department (ED) is often associated with demographic and clinical factors such as a patient’s race/ethnicity and insurance status. We sought to determine whether the quality of care delivered to children in the ED was associated with a variety of patient-level factors.
Methods:
This was a retrospective, observational cohort study. Pediatric patients (<18 years) who received care between January 2011 and December 2011 at one of 12 EDs participating in the Pediatric Emergency Care Applied Research Network (PECARN) were included. We analyzed demographic factors (including age, sex, and payment source) and clinical factors (including triage, chief complaint, and severity of illness). We measured quality of care using a previously validated implicit review instrument using chart review with a summary score that ranged from 5 to 35. We examined associations between demographic and clinical factors and quality of care using a hierarchical multivariable linear regression model with hospital site as a random effect.
Results:
In the multivariable model, among the 620 ED encounters reviewed, we did not find any association between patient age, sex, race/ethnicity, and payment source and the quality of care delivered. However, we did find that some chief complaint categories were significantly associated with lower than average quality of care, including fever (−0.65 points in quality, 95% confidence interval [CI] = −1.24 to −0.06) and upper respiratory symptoms (−0.68 points in quality, 95% CI = −1.30 to −0.07).
Conclusion:
We found that quality of ED care delivered to children among a cohort of 12 EDs participating in the PECARN was high and did not differ by patient age, sex, race/ethnicity, and payment source, but did vary by the presenting chief complaint.
The quality of care delivered to patients in the United States is highly variable.1 Health services researchers continue to find relationships between the quality of care delivered to patients and a variety of patient-level factors, including age, sex, race/ethnicity, and insurance status. In the emergency department (ED), investigators have found such patient demographic factors among pediatric patients to be associated with disparities in triage,2 diagnostic testing,3–5 medication prescriptions,6 wait times,7,8 length of stay,8,9 admission rate,10 leaving without being seen,11 and readmission.12 Few studies, however, have examined whether or not demographic and other patient-level factors among children presenting to the ED are associated with overall measures of quality of care.
One of the major barriers to identifying differences in the quality of care delivered to children receiving care in the ED is the lack of general instruments that can be applied to the diverse case mix of children typically treated in EDs. Outcome measures such as mortality, length of stay, recidivism, appropriateness of admission, and health-related quality of life may not be reliable if the outcomes are uncommon or not sensitive to changes in processes of care. Peer review continues to play an important role in ascertaining quality of care both at the individual provider and at the team-based levels.13–15 Implicit review is a type of peer review in which assessments of quality of care are based on expert reviewers’ judgment of care16 and has been used in both outpatient17 and inpatient settings.18,19 Structured implicit review of medical records to assess quality of care has been shown to have high face validity14 and offers better inter-rater reliability14,20 than unstructured review.20
Recently, we tested and validated an ED-specific implicit review instrument on a large sample of children treated in 12 EDs participating in the Pediatric Emergency Care Applied Research Network (PECARN).21,22 This peer-review instrument encompasses four dimensions of care including the physician’s initial data gathering, integration of information and development of appropriate diagnoses, initial treatment plans and physician orders, and plan for disposition and follow-up, as well as one item assessing the overall quality of care. We found that this instrument has high construct validity and the summary score (range = 5 to 35) correlated well with condition-specific, criterion-based explicit quality measures. Specifically, we found that a difference of 1.0 in the summary quality of care score was significantly associated with differences in quality as measured by these four condition-specific quality measures.21,22
The purpose of this study was to examine the association between the quality of care measured using this implicit review instrument and a variety of patient-level factors among a cohort of children receiving care in the ED. We hypothesized that some demographic factors such as age, sex, race/ethnicity, and payment source, and some clinical factors such as chief complaints and severity of illness would be associated with differences of greater than 1.0 in the summary quality of care scores. Based on previous research,7,23–27 we specifically hypothesized that racial/ethnic minority patients and those patients with either no insurance or public insurance would receive lower quality of care.
METHODS
Study Design and Hospital Sample
This was a retrospective, observational cohort study of children presenting to 12 EDs participating in PECARN. PECARN is the only federally funded pediatric emergency medicine research collaborative in the United States, and at the time of the study, was composed of four geographically distinct research nodes with 22 participating EDs. For the purposes of this study, we included three EDs from each of the four nodes for equal nodal representation. The three EDs were specifically selected to maximize clinician and patient diversity with differences between hospital size (large and small), treating physicians (general emergency medicine [EM] and pediatric EM), and patient populations (including racial/ethnic diversity).
Study Setting and Population
Children younger than 18 years of age who presented to any of the 12 study EDs for evaluation from January through December 2011 were eligible for inclusion. We randomly sampled patient visits from the ED logs at each of the study hospitals using a two-stage date and patient sampling scheme generated by the PECARN Data Coordinating Center. First, the study year was stratified into six 2-month blocks (January—February; March—April; etc.) to ensure an equal distribution of patient encounters throughout the calendar year. The sampling scheme then provided a list of random dates and an associated list of random numbers. For each randomly selected date, a patient encounter was identified from the ordered ED log according to the associated random number for that date. If the patient encounter did not qualify, the next randomly sampled patient from that date was evaluated, until an eligible patient encounter was identified. The sampling scheme did not exclude medical records of patients that might have been previously selected, but did exclude medical records of children who were seen in the ED for scheduled procedures (e.g., suture removal), those transiently evaluated in the ED in the process of direct admission to the hospital, and those who left the ED without being seen by an attending physician. Based on previously reported sample size calculations used for the purposes of validating the implicit review instrument,22 a minimum of 50 records were obtained and reviewed from each participating ED.
Study Protocol
After removing all patient, hospital, and physician identifiers, the research coordinator at each participating hospital photocopied medical records of sampled patients. Essential components of the medical record included ED physician notes, triage nurse notes, ED nurse notes, all physician orders, all medication orders, laboratory results, and discharge instructions. Nonessential elements that were photocopied when available included radiology results and consultation reports. The research coordinator abstracted relevant patient data from each medical record and uploaded the deidentified record to a secure server at the PECARN Data Coordinating Center for review.
Quality of Care Score and Measurement
The quality of care provided to each child in the ED was assessed using the previously published and validated implicit review instrument (Table 1).21,22 Briefly, this five-item instrument includes four items assessing different dimensions of care and one item assessing the overall quality of care. The four dimension-specific items focus on processes of care and include the initial data gathering about acute problems, the integration of information and development of appropriate diagnoses, the initial treatment plan and orders, and the plan for disposition and follow-up. All five items were assessed on a seven-point ordered adjectival scale ranging from “extremely inappropriate” to “extremely appropriate.” We then calculated a summary quality of care score, which was the sum of the five item-specific scores from each record, resulting in a score ranging from 5 to 35 for each patient.21 In a recent publication, we demonstrated that the instrument had good internal consistency, moderate inter-rater reliability, and high inter-rater agreement. We also demonstrated evidence supporting validity in that the summary quality of care score correlated well with four condition-specific, criterion-based explicit quality of care instruments for asthma, febrile seizure, diarrhea and dehydration, and head trauma.22 Each deidentified medical record was randomly assigned to four of the eight physician reviewers for independent assessments of quality21,28 who did not review records from their own institution. Prior to reviewing the medical records, all of the reviewers met for a 1 day, in-person training session to review the manual of operations. The group discussed general principles of structured implicit review, how the instrument should be applied, outlined anchors for the adjectival scale, and reviewed several sample medical records both individually and as a group. Each reviewer was board certified in pediatric EM.
Table 1.
Structured, Implicit Review Quality-of-care Instrument
| Extremely Inappropriate | Very Inappropriate | Somewhat Inappropriate | Intermediate | Somewhat Appropriate | Very Appropriate | Extremely Appropriate | |
|---|---|---|---|---|---|---|---|
| Initial data gathering by physician about acute problems | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Physician’s integration of information and development of appropriate diagnoses | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Physician’s initial treatment plan and initial orders | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Physician’s plan for disposition and follow-up | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Assess the overall quality of care provided to the patient | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
Patient- and Presentation-level Factors
Data abstracted from ED records included patient age, sex, race, ethnicity, triage category, illness severity scores (PRISA II29 and RePEAT30), payment source/insurance type, chief complaint, time of ED arrival, day of presentation, and disposition of care. Race and ethnicity were recategorized into a single variable (race/ethnicity) using a previously described method.5 PRISA II and RePEAT scores were categorized into tertiles for ease in interpreting associations with the quality measure. Chief complaints were categorized into Pediatric Emergency Reason for Visit Clusters (PERCs; Data Supplement S1, available as supporting information in the online version of this paper, which is available at http://onlinelibrary.wiley.com/doi/10.1111/acem.13347/full).31 Each PERC was further collapsed into eight broad chief complaint categories (Data Supplement S2, available as supporting information in the online version of this paper, which is available at http://onlinelibrary.wiley.com/doi/10.1111/acem.13347/full). Time of arrival was dichotomized into daytime (7:01 AM to 6:59 PM) and nighttime (7:00 PM to 7:00 AM). Day of presentation was dichotomized into weekday (Monday through Friday) and weekend (Saturday and Sunday).
Data Analysis
The mean summary quality-of-care score across reviewers was the main dependent variable in our analyses. For univariable analyses, we compared mean quality of care scores using the Student’s t-test or analysis of variance for categorical variables and compared mean quality-of-care scores for continuous variables using linear regression, testing for significance using likelihood ratio tests. Pairwise comparisons for categorical variables with more than two levels were conducted using Tukey’s studentized range (HSD) test. Considering clinical and statistical associations from the univariable analyses, we also compared the association between the mean summary quality of care scores with age, sex, race/ethnicity, payment source, and triage in a hierarchical multivariable linear regression model with hospital site as a random effect to account for clustering of observations by the source hospital. These demographic and clinical patient-level factors were chosen for inclusion a priori, based on our hypotheses. All analyses were performed using SAS Version 9.4 (SAS Institute). p-values < 0.05 were considered to be significant. This study was approved by the institutional review board at each participating hospital.
RESULTS
A total of 620 ED encounters (all unique patients) were included in the study. Approximately 50 medical records (range = 47–55) were reviewed from each of the 12 participating EDs. As shown in Table 2, in the univariable analyses, the mean summary quality of care scores were significantly higher for boys and for patients with non-Hispanic white race/ethnicity compared to patients with non-Hispanic black race/ethnicity. There was no statistically significant association between patient age and the mean summary quality-of-care score. Children with private insurance had significantly higher mean quality-of-care scores than those with public insurance or no insurance. In terms of clinical factors, the mean summary quality-of-care scores were positively correlated with the patient’s triage level, with those patients triaged as urgent and emergent receiving higher quality than those triaged as nonurgent. Some of the chief complaint categories were positively and negatively associated with the mean summary quality-of-care score. Children with the chief complaint of trauma had significantly higher mean summary quality of care scores (31.2) than children with upper respiratory symptoms (30.2), fever (30.2), and abdominal pain (29.6). We did not find any clinically or statistically significant associations between the mean quality-of-care scores and the time of arrival to the ED, day of presentation to the ED, PRISA II scores, or RePEAT scores. Higher mean quality-of-care scores were recorded for patients who were hospitalized from the ED or transferred to another hospital compared to patients who were discharged home (Table 2).
Table 2.
Association of Mean Summary Quality-of-care Scores With Patient-level Factors
| Patient Characteristics (N = 620) | N (%) | Summary Quality-of-care Scores, Mean (±SD) | p-value |
|---|---|---|---|
| Age category (y) | |||
| 0 to <2 | 241 (38.9) | 30.5 (±2.2) | 0.49 |
| ≥2 to <8 | 225 (36.3) | 30.7 (±2.1) | |
| ≥8 | 153 (24.7) | 30.7 (±2.3) | |
| Sex | |||
| Female | 276 (44.6) | 30.4 (±2.3) | 0.02 |
| Male | 343 (55.4) | 30.8 (±2.0) | |
| Race/ethnicity | |||
| Hispanic | 159 (25.7) | 30.5 (±2.0) | 0.002* |
| White, non-Hispanic or Latino | 203 (32.8) | 31.0 (±2.1) | |
| Black, non-Hispanic or Latino | 175 (28.3) | 30.2 (±2.3) | |
| Other | 82 (13.2) | 30.9 (±2.2) | |
| Primary payment source | |||
| Public insurance | 384 (62.0) | 30.4 (±2.1) | <0.001† |
| Private insurance | 204 (33.0) | 31.1 (±2.1) | |
| Uninsured | 31 (5.0) | 29.9 (±2.5) | |
| Triage category | |||
| Nonurgent | 38 (6.1) | 29.8 (±2.6) | 0.04‡ |
| Urgent | 437 (70.6) | 30.6 (±2.2) | |
| Emergent | 144 (23.3) | 30.8 (±1.9) | |
| Chief complaint category | |||
| Trauma | 135 (21.8) | 31.2 (±2.3) | <0.001§ |
| Abdominal pain | 26 (4.2) | 29.6 (±2.0) | |
| Asthma/wheezing | 76 (12.3) | 30.9 (±1.8) | |
| Seizures/neurologic issues | 60 (9.7) | 30.2 (±2.3) | |
| Upper respiratory symptoms | 69 (11.1) | 30.2 (±2.3) | |
| Gastroenteritis | 70 (11.3) | 30.5 (±2.0) | |
| Fever | 86 (13.9) | 30.2 (±1.8) | |
| Other | 97 (15.7) | 30.8 (±2.3) | |
| Time of presentation to ED | |||
| Daytime | 311 (50.2) | 30.6 (±2.2) | 0.52 |
| Nighttime | 308 (49.8) | 30.7 (±2.2) | |
| Day of presentation | |||
| Weekday | 458 (74.0) | 30.7 (±2.2) | 0.23 |
| Weekend | 161 (26.0) | 30.4 (±2.2) | |
| PRISA II score | |||
| −2 to 0 | 251 (40.5) | 30.6 (±2.1) | 0.59 |
| 0 to 6 | 185 (29.9) | 30.7 (±2.3) | |
| 6 to 40 | 183 (29.9) | 30.5 (±2.1) | |
| RePEAT score | |||
| 0.250 to 0.977 | 212 (34.2) | 30.6 (±2.4) | 0.48 |
| 0.977 to 1.307 | 200 (32.3) | 30.5 (±2.2) | |
| 1.307 to 2.621 | 207 (33.4) | 30.8 (±1.9) | |
| Disposition | |||
| Discharged home | 527 (85.1) | 30.5 (±2.2) | 0.001|| |
| Admitted to observation unit | 11 (1.8) | 31.0 (±2.6) | |
| Admitted/transferred | 81 (13.1) | 31.4 (±1.7) | |
Mean summary quality-of-care scores were significantly higher for white, non-Hispanic or Latino compared to black, non-Hispanic or Latino.
Mean summary quality-of-care scores were significantly higher for private insurance compared to public insurance and uninsured.
Mean summary quality-of-care scores were significantly higher for urgent and emergent compared to nonurgent.
Mean summary quality-of-care scores were significantly higher for trauma compared to upper respiratory symptoms, fever, and abdominal pain.
Mean summary quality-of-care scores were significantly higher for admitted/transferred compared to discharged home.
In the hierarchical multivariable analysis, some of the chief complaint categories remained significantly associated with mean summary quality of care (Table 3); specifically those children presenting with fever and upper respiratory symptoms had lower quality-of-care scores by an adjusted mean of −0.65 points (95% confidence interval [CI] = −1.24 to −0.06) and −0.68 points (95% CI = −1.30 to −0.07), respectively. Other patient-level factors including age, sex, insurance type, race/ethnicity, and triage level were not significantly associated with mean quality-of-care scores after adjusting for other covariates (Table 3).
Table 3.
Multivariable Analysis Examining Association Between the Mean Summary Quality-of-care Scores With Patient-level Factors
| Patient Characteristics | Estimate | 95% CI | p-value |
|---|---|---|---|
| Age (y) | 0.01 | −0.02 to 0.04 | 0.53 |
| Sex | |||
| Female | −0.31 | −0.63 to 0.01 | 0.05 |
| Male | Ref. | ||
| Race/ethnicity | |||
| Black, non-Hispanic | 0.02 | −0.45 to 0.50 | 0.97 |
| Hispanic | −0.06 | −0.55 to 0.43 | |
| Other | 0.07 | −0.46 to 0.61 | |
| White, non-Hispanic | Ref. | ||
| Payment type | |||
| Public insurance | −0.23 | −0.62 to 0.16 | 0.21 |
| Uninsured | −0.70 | −1.53 to 0.14 | |
| Private insurance | Ref. | ||
| Triage | |||
| Emergent/critical | 0.16 | −0.60 to 0.93 | 0.91 |
| Urgent | 0.15 | −0.54 to 0.84 | |
| Nonurgent | Ref. | ||
| Chief complaint category | |||
| Abdominal pain | −0.85 | −1.73 to 0.02 | < 0.01 |
| Asthma or wheezing | 0.08 | −0.52 to 0.69 | |
| Fever* | −0.65 | −1.24 to −0.06 | |
| Gastroenteritis | −0.25 | −0.87 to 0.38 | |
| Seizures/neurologic symptoms | −0.45 | −1.10 to 0.20 | |
| Trauma | 0.41 | −0.11 to 0.93 | |
| Upper respiratory symptoms* | −0.68 | −1.30 to −0.07 | |
| Other | Ref. | ||
p < 0.05.
DISCUSSION
We evaluated whether the quality of care delivered to children receiving treatment in the ED was associated with patient-level characteristics, including age, sex, race/ethnicity, and payment source among a cohort of 12 EDs participating in the PECARN. While racial and ethnic minorities and those with public or no health insurance had lower mean quality-of-care scores in univariable analyses, after adjusting for other demographic and clinical confounders, we found that these associations were neither clinically nor statistically significant. Unlike studies of adult patients receiving care in the ED, our results do not suggest disparities or biases in the quality of care based on patient demographic and insurance factors, after adjusting for other important factors and confounders.
In our study, we did find that quality of care was most significantly associated with a patient’s chief complaint. Most notably we found lower-than-average quality of care delivered to children presenting with fever and upper respiratory symptoms. Differences in quality of care provided to patients with different medical conditions has been noted previously.32 The finding that some chief complaints were significantly associated with quality of care is consistent with this previous literature and could be explained, in part, by differences in the availability of standardized treatment protocols and clinical pathways for various pediatric conditions. The lack of standardized treatment protocols and/or the lack of adoption of these treatment pathways might lead to greater variability in diagnostic evaluations and treatments of children with chief complaints such as fever and upper respiratory symptoms. This rationale is supported by previous studies showing improved health care delivery and outcomes based on adherence to treatment protocols and evidence-based pathways.33–35 In addition, other nonclinical factors that may not have been documented in the medical record, such as parental preferences, may have influenced the ED physician’s medical decision making, which could have impacted the reviewer’s quality-of-care scores for certain conditions.5,23
Our finding that physician-directed quality of care was not associated with a patient’s race/ethnicity and insurance status in the multivariable analysis is consistent with some literature in EM that has found fewer disparities among these factors for children compared to adult patients.36 However, other literature in EM has found significant differences in care processes between children based on their race/ethnicity, particularly around the administration of analgesia and imaging in injury.5,6,23,25,37 These persistent differences document the continued need for efforts to reduce these disparities among children based on their sex, insurance status, and race/ethnicity.
LIMITATIONS
Our study has several limitations. First, the instrument used to measure quality of care focuses on physicianled decision making, which may not capture other differences in the quality related to processes of care. For example, there may be differences in patient wait times, patient/family satisfaction of care, quality of nursing care, and other non—physician-directed aspects of care quality. Furthermore, it is difficult to relate the magnitude of the differences observed in the quality-of-care scores to differences in clinical quality and outcomes. The implicit review instrument we used does not consider measures of final discharge diagnoses and ultimate patient outcomes, such as whether or not the patients’ conditions improved after treatment. While our instrument was shown to correlate well with condition-specific, criterion-based explicit measures of care, it is difficult to quantify these differences or to correlate them with more familiar measures of quality. In addition, the quality-of-care scores estimated by the implicit review instrument are based on retrospective review of medical records and not all patient-level factors were blinded (e.g., age, sex, race/ethnicity, and payment source); therefore, reviews were limited by the completeness and accuracy of the source documents, and potential reviewer implicit biases may have affected reviewers’ perceptions of quality of care. While our sample was derived from children treated at 12 children’s hospital EDs across the country, it may not accurately reflect the patient population and/or physician-directed quality of care for children receiving treatment at nonchildren’s hospitals, including community and critical access hospitals. For example, our sample included a relatively high number of encounters with a chief complaint of trauma, asthma, and seizures and the overall sample had relatively high mean summary quality-of-care scores likely as a result of only including PECARN EDs. Because of this, we recommend that future studies include patients treated at non-PECARN EDs. Finally, because we used the chief complaint to categorize the patient’s clinical condition, the final discharge diagnosis could have been different than the chief complaint and could have affected our results.
While our study has limitations, it also has strengths. First, we used a previously validated implicit review instrument that is widely applicable to a variety of conditions in the ED compared to disease-specific measures. The peer review process used in implicit review ensures that quality of care is evaluated using the most current knowledge of physicians and is considered a robust means of grading processes and quality of care, in aggregate. Of note, implicit review instruments are typically used for research and administrative evaluations rather than for evaluating individual clinical assessments or for disseminating quality data to the public. Last, we evaluated the medical records of children presenting to 12 children’s hospital EDs across the country and included the implicit review evaluations from eight different pediatric EM physicians from eight different institutions.
CONCLUSIONS
In conclusion, we did not find specific patient-level demographic factors, including age, sex, race/ethnicity, and insurance status, to be associated with the physician-directed quality of care delivered to a large cohort of pediatric patients presenting to 12 children’s hospital EDs. We did find, however, that a patient’s chief complaint was associated with the quality of care delivered, possibly reflecting lack of availability and/or the variable adherence to evidence-based treatment guidelines. Further research is warranted on the mechanisms by which chief complaints affect the process of care delivery. Disparities in quality can then be addressed with interventions that could lead to more effective, safe, efficient, timely, equitable, and patient centered care. Identification of patient-level factors that impact quality of care will assist health policy makers to generate specific policy recommendations with regard to training, staffing, and practice guidelines.
Supplementary Material
Acknowledgments
Funded by grant 1R01HS019712 from the Agency for Healthcare Research and Quality. This project was also supported in part by the Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB), Emergency Medical Services for Children (EMSC) Network Development Demonstration Program under cooperative agreements U03MC00008, U03MC00001, U03MC00003, U03MC00006, U03MC00007, U03MC22684, and U03MC22685. This information or content and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by, HRSA, HHS, or the U. S. Government.
Footnotes
The authors have no potential conflicts to disclose.
Preliminary results of this study were presented at the poster session of the 6th Annual UC Davis Healthcare Quality Forum at the University of California Davis School of Medicine, Sacramento, CA, Mar 16, 2016.
Supporting Information
The following supporting information is available in the online version of this paper available at http://onlinelibrary.wiley.com/doi/10.1111/acem.13347/full
Data Supplement S1. Twenty Most Frequent Pediatric Emergency Reason for Visit Clusters (PERCs).
Data Supplement S2. Classification of Pediatric Emergency Reason for Visit Clusters (PERCs) into Chief Complaint Categories.
Contributor Information
James P. Marcin, Department of Pediatrics, University of California, Davis, School of Medicine, Sacramento, CA.
Patrick S. Romano, Department of Pediatrics, University of California, Davis, School of Medicine, Sacramento, CA; Department of Internal Medicine, University of California, Davis, School of Medicine, Sacramento, CA.
Parul Dayal, Department of Pediatrics, University of California, Davis, School of Medicine, Sacramento, CA.
Madan Dharmar, Department of Pediatrics, University of California, Davis, School of Medicine, Sacramento, CA.
James M. Chamberlain, Division of Emergency Medicine, Children’s National Health System, Washington, DC.
Nanette Dudley, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT.
Charles G. Macias, Department of Pediatrics and Center for Clinical Effectiveness, Baylor College of Medicine, Houston, TX.
Lise E. Nigrovic, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA.
Elizabeth C. Powell, Department of Pediatrics, Northwestern University’s Feinberg School of Medicine, Chicago IL.
Alexander J. Rogers, Departments of Emergency Medicine and Pediatrics, University of Michigan, Ann Arbor, MI.
Meridith Sonnett, Department of Pediatrics, Columbia University Medical Center, Columbia University College of Physicians and Surgeons, New York, NY.
Leah Tzimenatos, Department of Emergency Medicine, University of California, Davis, School of Medicine, Sacramento, CA.
Elizabeth R. Alpern, Department of Pediatrics, The Children’s Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, PA; Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL.
Rebecca Andrews-Dickert, Department of Emergency Medicine, DeVos Children’s Hospital, Michigan State University College of Human Medicine, Grand Rapids, MI.
Dominic A. Borgialli, Department of Emergency Medicine, Hurley Medical Center and University of Michigan, Flint, MI.
Erika Sidney, Division of Emergency Medicine, Children’s Hospital Colorado, University of Colorado, Aurora, CO..
T. Charles Casper, Department of Pediatrics, University of Utah and PECARN Data Coordinating Center, Salt Lake City, UT.
J. Michael Dean, Department of Pediatrics, University of Utah and PECARN Data Coordinating Center, Salt Lake City, UT.
Nathan Kuppermann, Department of Pediatrics, University of California, Davis, School of Medicine, Sacramento, CA; Department of Emergency Medicine, University of California, Davis, School of Medicine, Sacramento, CA.
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