2017 AAAEM Benchmarking Survey: Comparing Pediatric and Adult Academic Emergency Departments.

OBJECTIVES: The Academy of Administrators in Academic Emergency Medicine Benchmark Survey of academic emergency departments (EDs) was conducted in 2017. We compared operational measures between pediatric and adult (defined as fewer than 5% pediatric visits) EDs based on survey data. Emergency departments in dedicated pediatric hospitals were not represented. METHODS: Measures included: (1) patient volumes, length of stay, and acuity; and 2) faculty staffing, productivity, and percent effort in academics. t Tests were used to compare continuous measures and inferences for categorical variables were made using Pearson χ2 test. RESULTS: The analysis included 17 pediatric and 52 adult EDs. We found a difference in the number of annual visits between adult (median, 66,275; interquartile range [IQR], 56,184-77,702) and pediatric EDs (median, 25,416; IQR, 19,840-29,349) (P < 0.0001). Mean "arrivals per faculty clinical hour" and "total arrivals per treatment space" showed no differences. The proportion of visits (1) arriving by emergency medical services and (2) for behavioral health were significantly higher in adult EDs (both P < 0.0001). The mean length of stay in hours for "all" patients was significantly longer in adult (5.4; IQR, 5.0-6.6) than in pediatric EDs (3.5; IQR, 2.9-4.3; P = 0.017). A similar difference was found for "discharged" patients (P = 0.004). Emergency severity indices, professional evaluation and management codes, and hospitalization rates all suggest higher acuity in adult EDs (all P < 0.0001). There were no differences in mean work relative value units per patient or in the distribution of full time equivalent effort dedicated to academics. CONCLUSIONS: In this cohort, significant differences in operational measures exist between academic adult and pediatric EDs. No differences were found when considering per unit measures, such as arrivals per faculty clinical hour or per treatment space.

Patient Characteristics and Clinical Process Predictors of Patients Leaving Without Being Seen from the Emergency Department.

INTRODUCTION: Delays in patient flow in the emergency department (ED) result in patients leaving without being seen (LWBS). This compromises patient experience and quality of care. Our primary goal was to develop a predictive model by evaluating associations between patients LWBS and ED process measures and patient characteristics. METHODS: This was a cross-sectional study in a 95,000 annual visit adult ED comparing patients LWBS, with controls. Data were drawn from four seasonally adjusted four-week periods (30,679 total visits). Process measures included 1) arrivals per hour; 2) "door-to-provider" time; and the numbers of 3) patients in the waiting room; 4) boarding ED patients waiting for an inpatient bed; 5) providers and nurses (RN); and 6) patients per RN. Patient characteristics collected included 1) age; 2) gender; 3) race/ethnicity; 4) arrival mode (walk-in or via emergency medical services [EMS]); and 5) acuity based on Emergency Severity Index (ESI). Univariable analyses included t-tests and Pearson's chi-square tests. We split the data randomly into derivation and validation cohorts. We used backward selection to develop the final derivation model, and factors with a p-value ≤ 0.05 were retained. Estimates were applied to the validation cohort and measures of discrimination (receiver operating characteristic) and model fit were assessed. RESULTS: In the final model, the odds of LWBS increased with the number of patients in the waiting room (odds ratio [OR] 1.05; 95% confidence interval [CI], 1.03 to 1.06); number of boarding patients (OR 1.02; 95% CI, 1.01 to 1.03); arrival rate (OR 1.04; 95% CI, 1.02 to 1.05) and longer "door-to-provider" times (test of linear trend in the adjusted OR was p = 0.002). Patient characteristics associated with LWBS included younger age (OR 0.98; 95% CI, 0.98 to 0.99), and lower acuity (higher ESI category) (OR 2.01; 95% CI, 1.84 to 2.20). Arrival by EMS was inversely associated with LWBS (OR 0.29; 0.23 to 0.36). The area under the curve for the final model in the validation cohort was 0.85 (95% CI, 0.84 to 0.86). There was good agreement between the observed and predicted risk. CONCLUSION: Arrival rate, "door-to-provider time," and the numbers of patients in the waiting room and ED boarders are all associated with patients LWBS.

Key Players in Key Roles: The Baystate Patient Progress Initiative to Improve Emergency Department Efficiency and Productivity.

BACKGROUND: The percentage of patients who leave the emergency department without being seen by a provider is a measure of efficiency and presents risk-management concerns. The number of patients actually "seen" by a provider is a measure of productivity. The opening of our new emergency department in December 2012, resulted in increases in both demand and the percentage of patients who left without being seen. Operational nursing leadership managed ED patient flow, but the structure was loosely organized on an ad hoc basis. METHODS: Operational nursing leadership roles were re-assigned to personnel with management aptitude and interest. The charge nurse coordinated care throughout all sections (pods) of the department while the pod lead nurse coordinated care in each pod. The flow coordinator nurse accepted transfers and emergency medical services arrivals. Nursing and physician staffing remained unchanged, and measures were calculated over a 3-year period (December 3, 2012, to December 2, 2015). The number of patients seen per day was analyzed using simple linear regression. The percentage of patients who left without being seen was analyzed using fractional logistic regression; P< 0.05 was considered statistically significant. RESULTS: The weekly mean number of patients seen per day rose 13% from 265 to 299 patients. The weekly mean percentage of patients who left without being seen declined 45% from 8.2% to 4.5%. The regression lines for both measures were significant at P < 0.001. CONCLUSION: Measures of efficiency and productivity can be improved significantly with a dedicated operational nursing leadership structure without adding nursing or physician staffing.

Can the initial history predict whether a child with a head injury has been abused?

OBJECTIVE: Previous studies of child abuse have used the presenting history as part of the case definition of abuse. Thus, data from these studies cannot be used to determine the diagnostic utility of historical features for identifying cases of abuse. The objective of this study was to determine the diagnostic utility of certain historical features for identifying cases of abusive head trauma. METHODS: We retrospectively studied all children, aged 0 to 3 years, who had acute traumatic intracranial injury and were admitted to a tertiary care pediatric hospital from 1993 to 2000. Cases were categorized as either "definite abuse" or "not definite abuse" on the basis of radiologic, ophthalmologic, and physical examination findings, without regard to the presenting history. RESULTS: Forty-nine (30%) of 163 children met the criteria for definite abuse. Having no history of trauma had a high specificity (0.97) and positive predictive value (PPV; 0.92) for abuse. Among the subgroup of patients with persistent neurologic abnormality at hospital discharge (n = 34), having a history of no or low-impact trauma had a specificity of 1.0 and a PPV of 1.0 for definite abuse. Injuries were blamed on home resuscitative efforts in 12% of definite abuse cases and 0% of not definite abuse cases. The initial history of trauma was changed in 9% of definite abuse cases, as compared with 0% of not definite abuse cases. CONCLUSION: Among young children with a head injury, certain historical features have high specificity and PPV for diagnosing child abuse.