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Control, compare and communicate: designing control charts to summarise efficiently data from multiple quality

B Guthrie1, T Love, T Fahey

  • 1Department of Community Health Sciences, University of Dundee, Dundee DD2 4BF, UK. b.guthrie@chs.dundee.ac.uk

Quality & Safety in Health Care
|December 6, 2005
PubMed
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Summarizing complex healthcare quality data is difficult. This study proposes using control charts and small multiples graphics for better quality improvement and governance insights.

Area of Science:

  • Health Services Research
  • Statistical Process Control
  • Data Visualization

Background:

  • Summarizing complex quality indicator data for diverse stakeholders (patients, clinicians, managers, policymakers) presents a significant challenge.
  • Traditional aggregation methods like star ratings and balanced scorecards may obscure crucial details needed for effective quality improvement initiatives.
  • There is a need for improved methods to present multiple quality indicators that support both quality improvement and governance.

Purpose of the Study:

  • To propose and discuss an alternative approach for summarizing and presenting multiple cross-sectional quality indicators.
  • To enhance the utility of quality data for patients, clinicians, managers, and policymakers.
  • To provide methods suitable for both quality improvement and healthcare governance.

Related Experiment Videos

Main Methods:

  • Discussion of control charts for repeated measurements of single processes, drawing from industrial statistical process control (SPC).
  • Exploration of control charts for cross-sectional comparison of multiple institutions on a single quality indicator, a method less common in industry but proposed for healthcare.
  • Introduction of small multiples graphics as a technique combining control chart signal extraction with efficient graphical presentation for multiple indicators.

Main Results:

  • The paper outlines three distinct methods for data summarization and presentation.
  • Control charts and small multiples graphics offer potential advantages over simple aggregation for quality improvement.
  • The proposed methods aim to retain detail while providing clear signals for action.

Conclusions:

  • The proposed methods, including specific applications of control charts and small multiples graphics, offer a valuable alternative to traditional data aggregation for quality indicators.
  • These approaches can facilitate more effective quality improvement by preserving necessary detail and providing clear signals.
  • The discussed techniques are suitable for both ongoing quality improvement efforts and broader healthcare governance frameworks.