CPMAI - Readmissions Prediction Project
Project Project Purpose
“The Remarc Readmissions Project shows how healthcare leaders can reduce costly readmissions by starting small with a minimum viable product (MVP) and scaling responsibly. Using the platform-agnostic CPMAI methodology, we transform a pressing business challenge into a structured, measurable AI solution — ensuring governance, explainability, and alignment with both clinical and financial priorities.”
CPMAI Phase 1 - Business Understanding
In Phase 1 we lay the foundation for a successful AI initiative by defining the problem, setting measurable goals, identifying stakeholders, and aligning the project with organizational strategy before any data related work begins.
CPMAI Phase 2 - Data Understanding
In this phase, we perform initial data exploration, quality checks, and align data sources to the business goals established in Phase I.
CPMAI Phase 3 - Data Preparation
Here we use data cleaning, transformation, and feature engineering techniques to make the data ready for modeling
CPMAI Phase 4 - Model Development
In Phase IV, we build and refine machine learning models using proven techniques to capture the patterns discovered in your data. This stage transforms the data preparation work into predictive power.
CPMAI Phase 5 - Model Evaluation
Phase V: Model Evaluation identifies the best-performing model by comparing model performance metrics, while ensuring it meets business objectives and delivers real-world impact before deployment.
CPMAI Phase 6 - Model Deployment
Scheduled to be released 9/16/2025