Use of rational subgrouping to understand variation and opportunity for improvement in time to ultrasound

Keon Ma1, Jennifer Thull-Freedman2

  • 1Department of Pediatrics, University of Calgary, 28 Oki Drive NW, Calgary, AB, T3B 6A8, Canada. keon.ma@albertahealthservices.ca.

CJEM
|January 3, 2024
PubMed

Insights

Pediatric ultrasound wait times vary significantly by patient sex, age, and shift type. Identifying these variations using rational subgrouping helps improve emergency department efficiency.

Area of Science:

  • Pediatric Emergency Medicine
  • Healthcare Quality Improvement
  • Diagnostic Imaging

Background:

  • Variation in diagnostic imaging turnaround times impacts emergency department (ED) patient care.
  • Understanding factors contributing to delays in pediatric abdominal/pelvic ultrasounds is crucial for optimizing ED workflows.

Purpose of the Study:

  • To identify factors contributing to variations in the time to abdominal and/or pelvic ultrasound completion for pediatric patients in an ED.
  • To utilize rational subgrouping to pinpoint opportunities for improving ultrasound service delivery.

Main Methods:

  • Retrospective analysis of all pediatric abdominal and pelvic ultrasounds from May 2019 to April 2021 at Alberta Children's Hospital ED.
  • Electronic health records used to extract study order and completion times.
  • Statistical process control (SPC) I-charts and rational subgrouping applied to analyze variations by sex, age, and shift type.

Main Results:

  • Median order-to-completion time was 155 minutes.
  • Females (178 min) experienced longer wait times than males (131 min).
  • Older children (12+ years: 171 min) had longer wait times than younger age groups (0-3 years: 110 min).
  • Night shifts showed significantly longer completion times (269 min) compared to day (145 min) and evening (129 min) shifts.

Conclusions:

  • Significant variations in pediatric ultrasound completion times exist based on patient sex, age, and ED shift.
  • Rational subgrouping effectively identified sources of variation, supporting targeted quality improvement (QI) initiatives.
  • This methodology aids in making informed decisions for resource allocation and process optimization in QI efforts.
Abstract

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