Digital Twin (DT) technology has the potential to transform cardiac care by simulating patient-specific
physiological conditions to inform clinical decisions. Despite its promise, real-world implementation of digital twins
in cardiology faces challenges related to required knowledge, integration, equity, and scalability. This study
proposes a lean Six Sigma-based framework to design and deploy an equitable, clinically useful digital twin for
making high-stakes cardiovascular decisions, such as choosing between percutaneous coronary intervention (PCI)
and coronary artery bypass grafting (CABG). Using the DMAIC framework, we start by defining a relevant clinical
use case and mapping stakeholders through SIPOC and Voice of the Customer (VOC) tools. In the Measure and
Analyze phases, patient-level data (e.g., age, race, clinical indicators, outcomes) will be collected and stratified to
identify disparities and inefficiencies across treatment pathways. Predictive modeling and root cause analysis (e.g.,
fishbone diagrams, 5 Whys) will help identify factors driving variation. During the Improve phase, a digital twin
prototype will be developed to simulate individual risks and outcomes using real-world data. The Control phase will
focus on deploying the digital twin in clinical settings through pilot testing, statistical process control, and equity
assessments to ensure long-term reliability and fairness. This approach promotes the creation of transparent,
inclusive, and clinically relevant digital twins. Equity metrics will be embedded throughout to benefit underserved
populations. The framework illustrates how industrial engineering principles can accelerate digital twin adoption in
cardiology, fostering personalized, equitable, and scalable solutions for real-world clinical impact.