MLG Medical Readiness Personnel
Personnel closest to medical readiness workflows and the planned audience for the educational intervention.
Creating a Concept Map
A concept map from AI readiness to measurable change.
Examining whether targeted AI education and training can improve readiness for change among personnel involved in medical readiness workflows.
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?
PMLG medical readiness personnel
IAI education and training intervention
CPre-intervention readiness score
OPost-intervention readiness-for-change score
T12-week practicum period
Concept Map
Variable Relationship Map
baseline context→
Baseline Readiness
Initial readiness-for-change score before AI education; the comparison point for improvement.
targets→
AI Education & Training
Instruction on AI concepts, perceived barriers, training needs, and feasible AI-supported workflow use cases.
intervention pathway continues↓
Readiness Reassessment
Follow-up readiness-for-change score collected after the intervention during the 12-week period.
produces→
Improved Readiness for Change
The measurable outcome: change in readiness-for-change scores related to AI-supported workflows.
Method and implementation concepts that explain the pathway
Workflow Environment
Operational setting, staff demands, digital transformation pressure, and readiness mission.
shapes→
Barriers, Confidence, Training Needs
Perceived barriers, AI confidence, trust, workflow relevance, and training gaps help explain change.
examined through practicum methods↓
Pre/Post Practicum Design
Same-participant pretest and posttest structure supports paired comparison of readiness scores.
translates to→
Leader-Facing Planning Insight
Findings guide future education, adoption planning, governance, and realistic AI use-case development.
Concept map logic: population and context establish the baseline, the education intervention targets readiness barriers, the posttest measures change, and the findings translate into planning insight for AI-supported medical readiness workflows.