Performance measures are essential components of public reporting and quality improvement. To date, few such measures exist to provide a comprehensive assessment of the quality of emergency department services for children.
Our goal was to use a systematic process to develop measures of emergency department care for children (0-19 years) that are (1) based on research evidence and expert opinion, (2) representative of a range of conditions treated in most emergency departments, (3) related to links between processes and outcomes, and (4) feasible to measure.
We presented a panel of providers and managers data from emergency department use to identify common conditions across levels of patient acuity, which could be targets for quality improvement. We used a structured panel process informed by a literature review to (1) identify condition-specific links between processes of care and defined outcomes and (2) select indicators to assess these process-outcome links. We determined the feasibility of calculating these indicators using an administrative data set of emergency department visits for Ontario, Canada.
The panel identified 18 clinical conditions for indicator development and 61 condition-specific links between processes of care and outcomes. After 2 rounds of ratings, the panel defined 68 specific clinical indicators for the following conditions: adolescent mental health problems, ankle injury, asthma, bronchiolitis, croup, diabetes, fever, gastroenteritis, minor head injury, neonatal jaundice, seizures, and urinary tract infections. Visits for these conditions account for 23% of all pediatric emergency department use. Using an administrative data set, we were able to calculate 19 indicators, covering 9 conditions, representing 20% of all emergency department visits by children.
Using a structured panel process, data on emergency department use, and literature review, it was possible to define indicators of emergency department care for children. The feasibility of these indicators will depend on the availability of high-quality data.