Hospital-level factors associated with pediatric emergency department return visits
BACKGROUND
Return visits (RVs) and RVs with admission (RVAs) are commonly used emergency department quality measures. Visit- and patient-level factors, including several social determinants of health, have been associated with RV rates, but hospital-specific factors have not been studied.
OBJECTIVE
To identify what hospital-level factors correspond with high RV and RVA rates.
SETTING
Multicenter mixed-methods study of hospital characteristics associated with RV and RVA rates.
DATA SOURCE
Pediatric Health Information System with survey of emergency department directors.
MEASUREMENTS
Adjusted return rates were calculated with generalized linear mixed-effects models. Hospitals were categorized by adjusted RV and RVA rates for analysis.
RESULTS
Twenty-four hospitals accounted for 1,456,377 patient visits with an overall adjusted RV rate of 3.7% and RVA rate of 0.7%. Hospitals with the highest RV rates served populations that were more likely to have government insurance and lower median household incomes and less likely to carry commercial insurance. Hospitals in the highest RV rate outlier group had lower pediatric emergency medicine specialist staffing, calculated as full-time equivalents per 10,000 patient visits: median (interquartile range) of 1.9 (1.5-2.1) versus 2.9 (2.2-3.6). There were no differences in hospital population characteristics or staffing by RVA groups.
CONCLUSION
RV rates were associated with population social determinants of health and inversely related to staffing. Hospital-level variation may indicate population-level economic factors outside the control of the hospital and unrelated to quality of care. Journal of Hospital Medicine 2017;12:536-543. © 2017 Society of Hospital Medicine
© 2017 Society of Hospital Medicine
PHIS Database
PHIS, an administrative database managed by Truven Health Analytics, includes data from ED, ambulatory surgery, observation, and inpatient encounters across Children’s Hospital Association member children’s hospitals in North America. Data are subjected to validity checks before being included in the database.16 PHIS assigns unique patient identifiers to track individual patient visits within participating institutions over time.
Hospitals were described by percentages of ED patients in several groups: age (<1, 1-4, 5-9, 10-14, and 15-18 years)17; sex; race/ethnicity; insurance type (commercial, government, other); ED International Classification of Diseases, Ninth Edition (ICD-9) diagnosis code–based severity classification system score (1-2, low severity; 3-5, high severity)18; complex
ED Medical Director Survey
A web-based survey was constructed in an iterative process based on literature review and expert opinion to assess hospital-level factors that may impact ED RV rates.3,7,24-26 The survey was piloted at 3 institutions to refine its structure and content.
The survey included 15 close-ended or multiple-choice questions on ED environment and operations and 2 open-ended questions, “What is the largest barrier to reducing the number of return visits within 72 hours of discharge from a previous ED visit?” and “In your opinion, what is the best way of reducing the number of the return visits within 72 hours of previous ED visit ?” (questionnaire in Supplemental material). Hospital characteristics from the survey included total clinical time allotment, or full-time equivalent (FTE), among all physicians, pediatric emergency medicine (PEM) fellowship-trained physicians, and all other (non-PEM) physicians. The data were standardized across sites by calculating FTE-per-10,000-visits values for each hospital; median duration of ED visit for admitted and discharged patients; median time from arrival to ED physician evaluation; rate of leaving without being seen; discharge educational material authorship and age specificity; follow-up visit scheduling procedure; and percentage of ED patients for whom English was a second language.
Responses to the 2 open-ended questions were independently categorized by Drs. Pittsenbarger and Alpern. Responses could be placed in more than 1 category if multiple answers to the question were included in the response. Categorizations were compared for consistency, and any inconsistencies were resolved by the consensus of the study investigators.
Outcome Measures From PHIS Database
All ED visits within a 12-month period (July 1, 2013–June 30, 2014) by patients younger than 18 years at time of index ED visit were eligible for inclusion in the study. An index visit was defined as any ED visit without another ED visit within the preceding 72 hours. The 72-hour time frame was used because it is the most widely studied time frame for ED RVs.5 Index ED visits that led to admission, observation status, death, or transfer were excluded.
The 2 primary outcomes of interest were (1) RVs within 72 hours of index ED visit discharge and (2) RVs within 72 hours that resulted in hospital admission or observation status at the next ED visit (RVA).7,9,27-30 For patients with multiple ED revisits within 72 hours, only the first was assessed. There was a 72-hour minimum between index visits for the same patient.
Statistical Analyses
To determine hospital groups based on RV and RVA rates, we adjusted RV and RVA rates using generalized linear mixed-effects models, controlling for clustering and allowing for correlated data (within hospitals), nonconstant variability (across hospitals), and non-normally distributed data, as we did in a study of patient-level factors associated with ED RV and RVA.3 For each calculated rate (RV, RVA), the hospitals were then classified into 3 groups based on whether the hospital’s adjusted RV and RVA rates were outside 2 SDs from the mean, below the 5th or above the 95th percentile, or within that range. These groups were labeled lowest outliers, highest outliers, and average-performing hospitals.
After the groups of hospitals were determined, we returned to using unadjusted data to statistically analyze them. We summarized continuous variables using minimum and maximum values, medians, and interquartile ranges (IQRs). We present categorical variables using counts and percentages. To identify hospital characteristics with the most potential to gain from improvement, we also analyzed associations using 2 collapsed groups: hospitals with RV (or RVA) rates included in the average-performing and lowest outlier groups and hospitals within the highest outlier group. Hospital characteristics and hospital’s patient population characteristics from the surveys are summarized based on RV and RVA rate groups. Differences in distributions among continuous variables were assessed by Kruskal-Wallis 1-way analysis of variance. Chi-square tests were used to evaluate differences in proportions among categorical variables. All statistical analyses were performed with SAS Version 9.4 (SAS Institute); 2-sided P < 0.05 was considered statistically significant.
