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Cost & Utilization Dashboard: Healthcare Claims Analysis

  • Writer: Edgard Ledea
    Edgard Ledea
  • Nov 19, 2025
  • 2 min read

This dashboard analyzes a synthetic but realistic healthcare claims dataset that includes medical, pharmacy, and utilization records. Users can explore total cost, PMPM, service category spend, chronic condition impact, ED utilization, and high-cost member patterns. Filters for Region, Plan Type, and Risk Tier allow customized population views.


I built this project to practice SQL and Tableau together and to show how claims data can be turned into a clear population story using clean visuals and my custom brand palette.


What This Dashboard Lets You Do

  • Break down total cost, PMPM, total members, and total claims

  • View spend across service categories including Rx, Inpatient, Outpatient, and ED

  • Analyze cost by chronic condition such as CHF, Diabetes, HTN, Renal Disease, and COPD

  • Identify members with more than two ED visits

  • Compare spending between the top five percent of members and the remaining population

  • Filter the entire view by Region, Plan Type, and Risk Tier


Key Insights From the Example Shown

  • Rx is the highest cost category at approximately 2.68 million

  • CHF members represent the highest chronic condition spend at 1.93 million

  • Only forty-one members had more than two ED visits, which shows that ED use is not the primary cost driver

  • The top five percent of members account for almost ninety percent of total spend, which reflects typical cost concentration in health plan populations


Tools Used

  • Tableau (Calculated Fields, Parameters, Custom Color Palette)

  • SQL (Data modeling, Spend calculations, Combining medical and pharmacy data)

  • Excel (Initial structure planning)


Data Sources

AI-generated synthetic claims dataset modeled after health plan data structures


Process Overview

  1. Generated a structured claims dataset including member demographics, chronic conditions, plan type, service categories, and claim amounts

  2. Ingested the data into SQL and created views for important metrics.

  3. Prepared clean tables for Tableau analysis

  4. Recreated views in Tableau due to Tableau public

  5. Designed the dashboard using my custom color palette and layout

  6. Published the interactive version to Tableau Public for easy viewing

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