Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Area of Science:

• Health informatics
• Implementation science
• Knowledge management

Background:

• Standardized taxonomies (STs) are crucial for health care knowledge representation and interoperability.
• A gap exists in classifying evidence translation to quality improvement (QI) implementation barriers and facilitators.
• This report introduces a novel semantic and ontological approach to address this gap.

Observation:

• Synthesized implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems.
• Employed a semantic and ontological approach for data synthesis.
• Utilized cause-and-effect analysis and qualitative thematic analysis.

Findings:

• Developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme.
• QIIT is a hierarchical and relational scheme linked to QI Phases, Implementation Domains, and Conceptual Determinants.
• The taxonomy incorporates customizable descriptors and attribute scales.

Implications:

• The QIIT standardizes the description of evidence translation to QI implementation barriers and facilitators.
• This classification scheme reduces redundancy and promotes semantic agreement.
• Integrating existing taxonomies enhances reuse and supports comparable, shareable QI evaluations for sustainable, clinically informed solutions.