Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives

Abstract

Background Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap.

Objectives This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process.

Methods We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories.

Results The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales.

Conclusion This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

 

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Keywords

evidence - quality improvement - standardized taxonomy - implementation barriers - implementation facilitators

Background and Significance

Standardized Taxonomies, Quality Improvement, and Implementation Science

Standardized terminologies or taxonomies (STs) compile agreed-upon terms to represent clinical knowledge.[1] They foster semantic interoperability in health care documentation and clinical practice,[2] [3] [4] improve knowledge representation, and support evidence-based (EB) interpretation of disease and well-being, thus aiding health care provision and clinical research.[5] Quality improvement (QI) involves continuous efforts to enhance patient outcomes, system performance, and professional development[6] that drive quality, efficiency, safety, timeliness, patient-centeredness, and equity improvement.[7] Implementation science addresses challenges in integrating research findings and EB practice into routine health care to improve quality and effectiveness.[8] [9] In the United States, where health systems have undergone full-scale digitalization,[10] [11] STs are crucial for achieving semantic and ontological interoperability and evidence translation into QI through implementation and innovation.

However, existing taxonomies have primarily concentrated on implementation outcomes[12] [13] and strategies,[14] [15] [16] [17] leaving gaps in capturing implementation barriers and facilitators. Implementation theories and frameworks often describe similar or identical barriers and facilitators as influential or determinant factors with different names,[18] [19] making it challenging to create digital solutions that facilitate consistent and efficient evidence translation within QI efforts.

 

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Semantic and Ontological Approaches and Use Cases

Semantic and ontology-driven approaches can digitally capture and represent concepts and processes to close these gaps. Semantic-based solutions that address data semantic heterogeneity and leverage conceptual attributes to formulate semantic meanings and relations with domain-specific solutions have improved health care effectiveness and efficiency.[20] [21] Ontology-oriented approaches aim to create shared knowledge representation that can be reused across systems and databases,[20] for example, the creation of an ontology toolkit for chronic disease management.[21]

Use cases, introduced in the late-1980s to represent specific user–system interactions,[22] have evolved into a unifying element for project activities.[23] They have found applications in health care to ensure consistent data feeds for clinical data, such as lab results, vital signs, and medication allergies.[24] These approaches hold promise for improving the implementation of EB practice through QI.

 

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Commonly Employed Quality Improvement and Implementation Science Vocabulary and Models

Utilizing established terminology and models from QI and implementation science can enhance semantic and ontological agreements. Without semantic interoperability, similar or identical concepts may be captured by different terms perceived or constructed as separate or multiple entities.[25] Well-known models and taxonomies like the QI Plan–Do–Study–Act (PDSA) model, Consolidated Framework for Implementation Research (CFIR), and cause-and-effect analysis can promote interoperability in managing and exchanging digital information through collective applications in use cases.

PDSA, which originated in industry with widespread applicability in health care, employs a standardized four-step iterative model for process improvement using STs.[26] Its model begins with developing a “plan” phase comprising articulated outcomes and assigned tasks, followed by a “do” phase to implement the plan. Data collection and analysis are performed in the next “study” phase. Based on evaluations, the final “act” phase is concluded with adoption, adaptation, or termination. CFIR is an implementation framework that systematically assesses barriers and facilitators to tailor implementation strategies and explain outcomes from design to evaluation.[27] [28] It consolidates implementation theories from different sources into 5 standardized domains and 39 constructs,[16] [19] [28] adaptable to research needs.[29] The five domains include innovation, outer setting, inner setting, individuals, and implementation process. Cause-and-effect analysis, represented as fishbone diagrams, offers customizable structured schemes and terminology for classifying root causes of quality-of-care issues.[27] [30] [31]

 

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Objectives

This interprofessional case report intended to introduce novel semantic and ontological approaches to construct a use-case-based taxonomy within a classification scheme, integrating existing taxonomies and models from QI and implementation science to capture implementation barriers and facilitators within a QI process. Employing accepted taxonomies and models from these fields can expedite the development of this new taxonomy. This approach helps bridge the gap in classifying, quantifying, qualifying, and codifying evidence with QI implementation, particularly in knowledge representation of barriers and facilitators.

This report had two objectives. The first objective was to utilize semantic and ontological approaches to synthesize barrier and facilitator data from the use case of implementing employee wellness QI initiatives across multisite of Veteran Affairs (VA) health care systems. The second objective was to propose an original framework for this use-case-based taxonomy on implementation barriers and facilitators within a QI process.

 

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Methods

Datasets, Sources, and Procedures

The datasets originated from a program evaluation of implementation barriers and facilitators to QI employee wellness initiatives performed by the VA Quality Scholars (VAQS) programs' 2022 incoming fellows. This 2-year advanced interprofessional program focuses on improvement research and initiatives, implementation science, operation leadership, and quality and safety training.[32] [33] [34] Fellows from eight sites collected and analyzed barrier and facilitator data for employee wellness initiatives' implementation. Seventy-one semistructured interviews were conducted using an adaptable interview guide with key stakeholders at various levels of local leadership in different departments. This use case combined the datasets from all sites synthesized and coded by two fellows (E.C.K. and K.S.) using Excel QI Macros. This process involved QI cause-and-effect process analysis and qualitative thematic analysis (see [Fig. 1]). This report did not require Institutional Review Board review and approval as it was related to QI in operations.

Fig. 1 Use case dataset generation process for analysis.

 

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Semantic and Ontological Approaches for the Proposed Taxonomy Classification Scheme

The employee wellness initiatives were launched across VA in 2017 to provide collaborative EB resources for employee wellness.[35] Since this initiative spanned all phases of a QI process related to EB wellness implementation, we created an ontological knowledge representation by selecting relevant, well-established QI and implementation models to cover the spectrum from evidence translation through QI to implementation barriers and facilitators. We employed a semantic approach to extract structured terms and concepts from these models and an ontological approach to represent knowledge exchange (see [Fig. 2]). This led to developing the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme, which incorporated elements from the QI PDSA phases, CFIR domains, and fishbone cause-and-effect analysis categories.

Fig. 2 Models and taxonomies for quality improvement, implementation, and barriers and facilitators.

To structure the QI process, we adopted PDSA phases. We integrated implementation domains from the five CFIR domains: innovation, outer setting, inner setting, individuals, and implementation process. We delineated barriers and facilitators as conceptual determinants and grouped them into six categories using the fishbone cause-and-effect diagram: people, process, policy, management, materials, and environment. Notably, we applied cause-and-effect categories, typically used for root cause problem analysis, in a novel way to classify influencing factors as both barriers and facilitators. We connected conceptual determinants to a customizable descriptor, further modified using binary and Likert scales. The binary scale captured barriers or facilitators, while the Likert scale quantified the varying barrier or facilitator degrees.

 

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Results

The QIIT followed a two-level hierarchical and relational classification scheme (see [Fig. 3]). This taxonomy originated from Evidence-Based Use Case of QI employee wellness initiatives focusing on implementation barriers and facilitators characterized by Population, Setting, Topic, and Level of Evidence. These characteristics were directly linked to QI Phases, Implementing Domains, Conceptual Determinants modified by customizable Descriptors, and Binary or Likert Attribute Scales at the first level.

Fig. 3 Quality Improvement Implementation Taxonomy Classification Scheme.

At the second level, it branched into four components of Plan, Do, Study, Act of the PDSA phases, five components of Innovation, Outer Setting, Inner Setting, Individuals, and Implementation Process of the CFIR five domains, and six components of People, Process, Policy, Management, Materials, and Environment following the fishbone diagram groupings under Conceptual Determinants.

Conceptual Determinants were further connected to two types of modifiers at a sublevel. The first type was a customizable Descriptor that further delineated a Determinant. The second type was an Attribute Scale calibrated in a binary Positive “ + ” or Negative “–” configuration or a Likert scale. The “–” symbol indicates barriers or attributes with negative connotations, representing what hinders or decreases. In contrast, the “ + ” symbol signifies facilitators or attributes with positive connotations, suggesting what promotes or increases. A Likert Attribute Scale, ranging from “0” to “5,” denoted varying degrees of facilitators and barriers from least (1) to greatest (5), with “0” as “none.”

The synthesized barriers covered all six cause-and-effect categories (see [Table 1]), for example, “Employee burnout” and “Shortage of time” under “People” and “Lack of protected time” under “Policy.” Facilitators were found in five of the six categories, excluding “People.” Facilitators included “Employee rest and protection” under “Policy” and “Wellness staff and leaders” with knowledge of institutional needs under “Management.” Interestingly, some synthesized facilitators overlapped with barriers, such as “Culture,” which was found to promote wellness at some sites and hinder at others.

Table 1 Synthesized barriers and facilitators

Category	Barrier	Facilitator
People	Employee burnout
	Shortage of time
	Staffing shortages
	Knowledge
	Turnover
Process	Complicated process	Analysis and feedback
	Variable analysis	Communication strategies
	Communication
	Lack of feedback
Policy	COVID-19 limitations	Employee rest and protection
	Lack of protected time	Local initiatives
	No unified policy
Management	Variable leadership	Wellness leaders
	Communication
	Variable knowledge of initiatives
	Priorities
Material	Variable resources	Varying local resources
	Lack of infrastructure	Analytics
	Communication
Environment	Burnout	Culture
	Culture
	COVID-19

Abbreviation: COVID-19, coronavirus disease 2019.

Identical QI Phases, Implementing Domains, and Conceptual Determinants captured structured processes and vocabulary that illustrated synthesized barriers and facilitators specific to this use case (see [Table 2]). The Binary Attribute Scale differentiated between barriers and facilitators associated with the same terms. Reused terms like “Communication” stemming from different “Conceptual Determinants” of “Management,” “Material,” or “Process” depicted different QI and implementation process barriers. Common terms like “lack” typically suggested barriers and were given a “–” designation. The neutral term “variable” was used to indicate various degrees of barriers and facilitators not explicitly defined by the Likert Attribute Scale.

Table 2 Synthesized barriers and facilitators illustrated in Quality Improvement Implementation Taxonomy

QI Phase	Implementing Domain	Conceptual Determinant	Customizable Descriptor	Attribute Scale—Binary	Attribute Scale—Likert
Do	Individuals	People	Employee burnout	−	Not specified
Do	Individuals	People	Shortage of time	−	Not specified
Do	Individuals	People	Staffing shortages	−	Not specified
Plan, Do	Individuals	People	Knowledge	−	Not specified
Do	Individuals	People	Turnover	−	Not specified
Plan, Do	Implementing Process	Process	Complicated process	−	Not specified
Study	Implementing Process	Process	Variable analysis	−	Not specified
Plan, Do, Study, Act	Implementing Process	Process	Communication	−	Not specified
Study	Implementing Process	Process	Lack of feedback	−	Not specified
Study	Implementing Process	Process	Analysis and feedback	+	Not specified
Plan, Do, Study, Act	Implementing Process	Process	Communication strategies	+	Not specified
Plan, Do	Outer Setting	Policy	COVID-19 limitations	−	Not specified
Do	Inner Setting	Policy	Lack of protected time	−	Not specified
Plan, Do, Study, Act	Innovation	Policy	No unified policy	−	Not specified
Act	Innovation	Policy	Employee rest and protection	+	Not specified
Plan, Do, Study, Act	Innovation	Policy	Local initiatives	+	Not specified
Plan, Do, Study, Act	Individuals	Management	Variable leadership	−	Not specified
Plan, Do, Study, Act	Individuals	Management	Communication	−	Not specified
Do	Individuals	Management	Variable knowledge of initiatives	−	Not specified
Plan, Do	Innovation	Management	Priorities	−	Not specified
Plan, Do, Study, Act	Individuals	Management	Wellness leaders	+	Not specified
Plan, Do, Study, Act	Inner Setting	Material	Variable resources	−	Not specified
Plan, Do, Study, Act	Innovation	Material	Lack of infrastructure	−	Not specified
Plan, Do, Study, Act	Inner Setting	Material	Varying local resources	+	Not specified
Study	Inner Setting	Material	Analytics	+	Not specified
Plan, Do	Innovation	Material	Communication	−	Not specified
Plan, Do	Individuals	Environment	Burnout	−	Not specified
Plan, Do, Study, Act	Inner Setting	Environment	Culture	−	Not specified
Plan, Do, Study, Act	Outer Setting	Environment	COVID-19	−	Not specified
Plan, Do, Study, Act	Inner Setting	Environment	Culture	+	Not specified

Abbreviations: COVID-19, coronavirus disease 2019; QI, quality improvement.

 

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Discussion

This case report introduces a novel approach to standardize the process and taxonomy for describing evidence translation into QI implementation. The results reveal recurring and overlapping terms that describe barriers and facilitators across various QI phases and implementation domains. The QIIT framework, conceptualized according to semantic and ontological approaches, facilitates semantic agreements, simplifies knowledge representation, and encourages efficient reuse of well-developed taxonomies and relationship modeling. This framework serves as a prototype for a digital solution to document barriers and facilitators in evidence translation into QI project implementation.

While prior works have primarily focused on taxonomies for implementation outcomes[12] [13] and strategies,[14] [15] [16] [17] our work emphasizes terminology consistency and offers a taxonomy for implementation barriers and facilitators. We observed descriptor variations of similar concepts and interchangeable use of certain terms, highlighting the need for a common terminology in characterizing the health care field efficiently.

Improving terminology clarity through heuristic definitions yields a functional implementation taxonomy.[13] The semantic and ontological approach in developing the QIIT's working taxonomy promotes conceptual consistency and reduces redundancy, aligning with the QI Lean tenets of standardization, waste reduction, and process improvement.[36] The hierarchical relational scheme ensures a consistent taxonomy of “Determinants” for barriers and facilitators applicable and reusable across “QI Phases” and “Implementing Domains.” Using symbols to denote facilitators and barriers for identical terms simplifies the language and eliminates redundancy, thereby offering an economical representation of knowledge.

By framing both barriers and facilitators as effects in cause-and-effect analysis, we shift from problem-based analysis to encompass root cause analysis of both problems and assets.[37] This approach allows the taxonomy to capture multiple contextual dimensions efficiently. For example, the term “Culture,” representing both a barrier and a facilitator, can be captured once with denotations of “ + ” for the facilitator and “–” for the barrier.

The use of the term “variable” to represent different levels of barriers and facilitators lacked a clear definition in the comprehensive cause-and-effect analysis across all sites. Consequently, it could not be quantified using the Likert Attribute Scale, as shown in [Table 2]. This underscores the need for a standardized method to quantify variations of barriers and facilitators in QI implementation.

It is essential to acknowledge that our work is limited to the data from a single local environment involving multisite QI initiatives focusing on employee wellness. Further work is needed for a more comprehensive evaluation and testing the prespecified domains of the QIIT framework on a larger scale.

 

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Conclusion

The absence of STs for tracking and measuring the implementation of EB practices in QI initiatives hinders the efficiency of QI efforts. Standardization is vital for improving knowledge representation in health care, leading to increased efficiency, quality, and timeliness.[13] [14] Employing STs can create comparable and sharable evaluations of QI-related outcomes for forecast, contributing to sustainable QI implementation with clinically informed innovative solutions.

Further evaluation is imperative and should refine the taxonomy and establish rules for an organized and interconnected framework, enabling comparable and sharable assessments of both barriers and facilitators across and beyond enterprises. Furthermore, we should explore the potential integration of this approach with STs in designing and executing implementation strategies and outcomes.

 

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Clinical Relevance Statement

The proposed framework and taxonomy hold significant clinical relevance by addressing the need for a comprehensive system to classify, quantify, qualify, and codify the intersection of evidence and QI implementation. This framework is valuable for efficiently representing the implementation barriers and facilitators of evidence translation through QI efforts in clinical practice. Ultimately, it contributes to the achievement of health care quality and outcomes.

 

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Multiple Choice Questions

1. What is one benefit of adopting a semantic approach to taxonomy development?

   • Improving quality

   • Enhancing meanings

   • Reducing redundancies

   • Deconstructing structure

   Correct answer: c.

   Rationale: A semantic approach in taxonomy development seeks semantic agreements in meanings and thus helps reduce terminology redundancies.

2. What QI tenet is reflected in the reuse of existing QI taxonomies?

   • Model for improvement

   • Plan, Do, Study, Act model

   • Root cause analysis

   • Lean

   Correct answer: d.

   Rationale: The reuse of existing QI taxonomies demonstrates the Lean QI tenet to reduce waste.

 

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Conflict of Interest

None declared.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the VAQS program or the Department of VA. Special thanks to VAQS 2022 incoming fellows and the VAQS coordinating center.

Protection of Human and Animal Subjects

No human subjects were involved in this project.

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Publication History

Received: 24 August 2023

Accepted: 07 November 2023

Accepted Manuscript online:
09 November 2023

Article published online:
31 January 2024