Context:
A payer organization sought to understand the primary drivers of healthcare costs among patients with cardiovascular disease. The objective was to identify high cost patients, evaluate key predictors of healthcare expenditure, and design effective cost containment strategies while maintaining quality of care.

Resolution:
Our team developed structured R Markdown reports that included regression tables and cost distribution plots to analyze expenditure patterns. We then created an interactive Shiny dashboard that enabled payers to simulate cost drivers, explore expenditure trends, and identify high risk patients. The predictive modeling framework allowed proactive identification of patients likely to exceed predefined cost thresholds, supporting data driven intervention planning.

Result:
The payer implemented targeted care management programs, including telehealth support and nurse follow ups, for high risk patients. As a result, hospitalizations were reduced by 8 percent and overall healthcare costs decreased by 10 percent in the following year. The initiative improved patient outcomes while lowering total expenditures.