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Development of measures of the quality of emergency department care for children using a structured panel process

Astrid Guttmann1, Asma Razzaq, Patty Lindsay

  • 1Institute for Clinical Evaluative Sciences, G Wing, Sunnybrook and Women's College Health Sciences Centre, 2075 Bayview Ave, Toronto, Ontario, Canada M4N 3M5. astrid.guttmann@ices.on.ca

Pediatrics
|July 5, 2006
PubMed

Insights

Developing quality indicators for pediatric emergency care is crucial. This study defined 68 clinical indicators for 18 conditions, with 19 indicators calculated for 9 common pediatric emergency department visits.

Area of Science:

  • Pediatric Emergency Medicine
  • Healthcare Quality Improvement
  • Health Services Research

Background:

  • Performance measures are vital for public reporting and quality improvement in healthcare.
  • Currently, limited comprehensive measures exist for assessing the quality of pediatric emergency department (ED) services.

Purpose of the Study:

  • To systematically develop evidence-based and expert-endorsed measures for pediatric ED care (ages 0-19).
  • Measures aim to cover common conditions, link processes to outcomes, and be feasible for data collection.

Main Methods:

  • A panel of healthcare providers and managers identified common pediatric ED conditions.
  • A literature review and structured panel process were used to define process-outcome links and select indicators.
  • Feasibility was assessed using administrative data from Ontario, Canada.

Main Results:

  • The panel identified 18 clinical conditions and 61 process-outcome links.
  • 68 specific clinical indicators were defined for conditions including asthma, mental health, and infections.
  • 19 indicators covering 9 conditions, representing 20% of pediatric ED visits, were calculable from administrative data.

Conclusions:

  • A structured process successfully defined indicators for pediatric emergency care.
  • The feasibility of implementing these quality measures depends on the availability of high-quality administrative data.
Abstract

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