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Making quality improvement data more accessible and understandable: Analyst, designer, and storyteller
1Department of Medicine for the Elderly, NHS Greater Glasgow and Clyde, Glasgow Royal Infirmary, Glasgow, UK.
Paediatric Anaesthesia
|February 23, 2022
Summary
Enhance data accessibility in quality improvement by using diverse skills like analysis and storytelling. Co-designing data experiences and mindful visualization improve engagement and decision-making.
Area of Science:
- Healthcare Quality Improvement
- Data Science in Medicine
- Health Informatics
Background:
- Effective data utilization is crucial for quality improvement (QI) initiatives.
- Traditional methods of data training may not suffice for all improvement practitioners.
- Barriers such as data literacy, past negative experiences, and technology access can hinder team engagement with data.
Purpose of the Study:
- To explore diverse approaches for making data more accessible and understandable in quality improvement.
- To identify strategies that empower improvement practitioners to effectively use data for decision-making.
- To address challenges that limit team engagement with measurement and data analysis.
Main Methods:
- Adopting a multidisciplinary approach, incorporating skills from analysts, designers, and storytellers.
- Employing co-design principles to ensure data relevance, inclusive language, and manageable processes.
- Integrating intrinsic motivators into measurement strategy design.
- Applying principles of thoughtful data visualization, including simplification and amplification of key concepts.
- Utilizing storytelling techniques to engage diverse audiences with data.
Main Results:
- Co-designing data experiences makes them more meaningful, inclusive, and manageable.
- Considering intrinsic motivators enhances collective data analysis and project sustainability.
- Thoughtful data visualization, simplification, and amplification improve data comprehension.
- Storytelling techniques can effectively engage wider audiences with data, inspiring change.
Conclusions:
- Diverse skillsets beyond technical measurement are essential for effective data use in QI.
- Co-design, mindful visualization, and storytelling are key strategies to improve data accessibility and engagement.
- Addressing individual motivators and perceptual challenges leads to more sustainable and impactful data-driven improvement.
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