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

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.

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.