Establishing a paediatric critical care core quality measure set using a multistakeholder, consensus-driven process

Jessica A Schults1,2,3,4,5, Karina R Charles1,2,3,4, Johnny Millar6,7

  • 1Herston Infectious Diseases Institute, Metro North Hospital and Health Service, Queensland, Australia.

Insights

A 51-item core quality measure set for paediatric critical care was developed, aligning with patient and clinician priorities. This aims to improve healthcare quality monitoring and reduce data collection burdens.

Area of Science:

  • Healthcare quality improvement
  • Paediatric critical care research
  • Health services research

Background:

  • Monitoring healthcare quality in paediatric critical care is complex due to varied measures and data collection challenges.
  • Establishing consistent quality metrics is vital for improving patient outcomes and care standards.

Purpose of the Study:

  • To develop a core set of quality measures for paediatric critical care.
  • To ensure measures are meaningful to both consumers (patients/families) and clinicians.
  • To promote aligned use and collection of quality measures across the field.

Main Methods:

  • A multi-stakeholder Delphi study combined with a consumer prioritisation survey.
  • Involved Australian and New Zealand paediatric critical care survivors, families, clinicians, and policymakers.
  • Triangulation of survey data preceded a consensus meeting to finalize the measure set.

Main Results:

  • Consumers prioritized nurse-patient ratios, visible patient goals, and long-term follow-up.
  • Clinicians agreed on a 51-item core measure set, prioritizing nurse-patient ratios, staff turnover, and long-term follow-up.
  • Feasibility was rated low due to data collection burden and lack of standardized definitions.

Conclusions:

  • A 51-item core measurement set for paediatric critical care has been defined, reflecting clinician and consumer priorities.
  • Implementation and evaluation of this set are recommended for quality programs.
  • Future work should address measure feasibility and consider retiring redundant measures.
Abstract

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