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

Area of Science:

• Health Services Research
• Information Science
• Knowledge Management

Background:

• Standardized taxonomies (STs) are crucial for knowledge representation and semantic interoperability in healthcare.
• A gap exists in classifying and codifying the intersection of evidence and quality improvement (QI) implementation.
• This report utilizes a novel semantic and ontological approach to address this gap.

Purpose of the Study:

• To synthesize implementation barrier and facilitator data from employee wellness QI initiatives using a semantic and ontological approach.
• To introduce an original framework, the Quality Improvement and Implementation Taxonomy (QIIT), for classifying QI implementation barriers and facilitators.

Main Methods:

• Synthesized terms from implementation barrier and facilitator datasets using qualitative thematic analysis and QI cause-and-effect analysis.
• Developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme.
• Built the QIIT framework upon existing terms and models, including QI Plan-Do-Study-Act phases, Consolidated Framework for Implementation Research domains, and fishbone categories.

Main Results:

• The QIIT is a hierarchical and relational classification scheme.
• The taxonomy links to four QI Phases, five Implementing Domains, and six Conceptual Determinants.
• Customizable Descriptors and Binary or Likert Attribute Scales further refine the QIIT.

Conclusions:

• Introduces a novel approach to standardize the taxonomy for describing evidence translation to QI implementation barriers and facilitators.
• The QIIT classification scheme reduces redundancy and enables semantic agreement and ontological knowledge representation.
• Integrating existing taxonomies enhances efficiency, promoting comparable QI evaluations for sustainable implementation and innovative solutions.