Creating a Concept Map

PICOT Question

For Marine Logistics Group medical team members and personnel involved in medical readiness workflows, will an AI education/training intervention focused on staff readiness, perceived barriers, training needs, and feasible AI use cases improve readiness for change for AI-supported workflows within 12 weeks?

Description of the Concept Map

My concept map shows the logic of my doctoral capstone project from left to right while also showing the contextual factors that may influence readiness for change. The map begins with the population: Marine Logistics Group medical team members and personnel involved in medical readiness workflows. This population is appropriate because 2d Marine Logistics Group provides tactical logistics support for II Marine Expeditionary Force, and 2nd Medical Battalion’s mission is to provide ready Health Service Support to II MEF in order to enable worldwide mission success (2nd Marine Logistics Group, 2026; 2nd Medical Battalion, 2026). The project then moves from baseline readiness assessment, to AI education and training, to post-intervention reassessment, and finally to the anticipated measurable outcome of improved readiness for change.

The lower section of the map shows the implementation logic behind that pathway. The medical readiness workflow environment, perceived barriers, staff confidence, training needs, trust, workflow relevance, and leader-facing planning insight are included because implementation literature shows that readiness is shaped by individual perceptions, organizational context, innovation fit, and the process used to prepare people for change (Damschroder et al., 2022; Shea et al., 2014; Weiner, 2009). The map is designed to make clear that this project is not an AI deployment project. It is an evidence-based educational readiness project that examines whether targeted education can improve readiness before AI-supported workflows are considered or scaled.

Variables Represented in the Map

Independent and Dependent Variables

The independent variable is the AI education and training intervention. This is the element being introduced during the practicum project. The dependent variable is the readiness-for-change score related to AI-supported workflows. The expected relationship is that the intervention may improve readiness by increasing knowledge, clarifying feasible use cases, reducing uncertainty, and helping participants identify barriers and training needs. This expectation aligns with organizational readiness theory, which argues that readiness is influenced by change commitment and change efficacy, or the extent to which members value the change and believe they have the capability to implement it (Weiner, 2009). It also aligns with the Organizational Readiness for Implementing Change measure, which treats readiness as something that can be assessed and compared across time (Shea et al., 2014).

The pre-intervention readiness score functions as the comparison condition, while the post-intervention readiness score functions as the outcome measure. Because the same participants are measured before and after the intervention, the project design supports a paired pre/post comparison rather than a comparison between unrelated groups. The concept map shows this methodological relationship with arrows from pretest to intervention to posttest to outcome, making the variable pathway easy to follow.

Key Concepts Illustrated in the Concept Map

The key concepts in the map are readiness for change, AI education and training, medical readiness workflow context, perceived barriers, staff confidence, training needs, workflow relevance, and measurable improvement. These concepts connect directly to the literature I have reviewed so far. Weiner’s theory of organizational readiness for change explains that successful change depends on the shared psychological state of organizational members, including whether they are committed to the change and believe they can carry it out (Weiner, 2009). Shea et al. (2014) support the measurable side of the map by showing that readiness can be operationalized through a structured readiness measure.

The updated Consolidated Framework for Implementation Research also supports the map because it organizes implementation around domains such as innovation, outer setting, inner setting, individuals, and implementation process (Damschroder et al., 2022). In this capstone, the AI education and training intervention is the innovation-related activity, the medical readiness environment represents the inner setting, and the participant factors include confidence, AI knowledge, trust, perceptions of burden, and training needs. The map also reflects current military health priorities because the MHS Digital Transformation Strategy identifies digital workforce competence and AI/data management as important lines of effort for modernizing health care delivery and readiness support (Military Health System, 2025).

Relationship to the Literature Reviews Conducted So Far

The concept map reflects the direction of my literature review by connecting military medical readiness, AI readiness, implementation science, and workforce education. The MLG-specific references establish why this population is operationally relevant: 2d Marine Logistics Group supports II MEF through tactical logistics capabilities, while 2nd Medical Battalion provides ready Health Service Support to II MEF (2nd Marine Logistics Group, 2026; 2nd Medical Battalion, 2026). The DoD medical readiness policy context also matters because individual medical readiness is a formal readiness concern within the Department of Defense (Department of Defense, 2022). Together, these sources support the project’s focus on medical readiness personnel rather than a generic health care workforce.

The AI and digital transformation literature supports the intervention side of the concept map. The DoD Data, Analytics, and Artificial Intelligence Adoption Strategy emphasizes the need to accelerate responsible data and AI adoption across the Department while aligning people, processes, and technology (Department of Defense, 2023). The MHS Digital Transformation Strategy further emphasizes a digitally competent medical workforce and the integration of AI and data management to support readiness and health outcomes (Military Health System, 2025). Adirim and Madsen (2025) also describe AI in the U.S. Military Health System as requiring governance, education and training, testing, and responsible preparation before clinical and operational AI tools are implemented.

The broader health care AI literature supports the barriers and training-needs portion of the map. Hassan et al. (2024) identified barriers and facilitators related to AI adoption in health care, including trust, knowledge, workflow integration, governance, and stakeholder readiness. These concepts are represented in the map because readiness for AI-supported workflows cannot be assumed simply because the technology exists. The literature suggests that successful AI adoption is not only a technical issue; it is also an education, trust, governance, workflow integration, and implementation-readiness issue (Damschroder et al., 2022; Hassan et al., 2024; Weiner, 2009).

How the Concept Map Illustrates the Overall Capstone Project Design

The overall design shown in the concept map is a pretest/posttest educational intervention project. The population receives a baseline readiness assessment. Participants then receive the AI education and training intervention. After the intervention, participants complete a post-intervention readiness assessment. The expected measurable outcome is improvement in readiness-for-change scores within the 12-week project period. This design is appropriate because the PICOT Question asks whether an educational intervention can improve readiness for change, and the concept map visually links each PICOT element to the project method.

The methodological approaches depicted in the map include population identification, baseline assessment, intervention delivery, post-intervention assessment, and paired comparison of readiness scores. The map also shows an applied interpretation process. If readiness improves, the results may support future AI education, adoption planning, governance discussion, and feasible use-case development. If readiness does not improve, the results may still identify unresolved barriers, workflow concerns, training gaps, or trust issues that leaders should address before pursuing AI-supported workflow changes. That interpretation is consistent with implementation science because readiness and context shape whether a change can move from concept to practice (Damschroder et al., 2022; Weiner, 2009).

Relationship Among the PICOT Question Elements

The concept map delineates the relationship among the PICOT Question elements. The population is Marine Logistics Group medical team members and personnel involved in medical readiness workflows. The intervention is the AI education and training activity. The comparison is the pre-intervention readiness level. The outcome is the post-intervention readiness-for-change score. The time frame is the 12-week practicum period. These elements are connected by arrows because the project follows a clear sequence: identify the participants, measure readiness, deliver the intervention, measure readiness again, and interpret the change.

The key concepts relate to the anticipated measurable outcome because readiness for change is the outcome being measured, while barriers, confidence, training needs, trust, and workflow relevance are concepts that may help explain why readiness changes or does not change. If participants understand AI better, see feasible workflow use cases, identify realistic barriers, and clarify training needs, their readiness-for-change scores may improve. If scores do not improve, the findings may indicate that barriers such as trust, governance concerns, workflow burden, or insufficient training remain unresolved. In either case, the concept map illustrates how the PICOT Question, intervention, variables, methods, and measurable outcomes fit together.

Summary of the Capstone Logic

Overall, the concept map shows that the capstone project is built around one practical question: can targeted AI education and training improve readiness for change among personnel involved in medical readiness workflows? The map connects the operational population to the evidence-based intervention, the pre/post method, the dependent outcome, and the practical leader-facing interpretation. It also shows that the project is grounded in both site-relevant military medical readiness context and implementation science literature, which is important because AI-supported workflow change requires prepared people, not just available technology (Adirim & Madsen, 2025; Department of Defense, 2023; Military Health System, 2025; Weiner, 2009).