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US Healthcare Analysis Power BI Dashboard – Step-by-Step Tutorial

Introduction

Welcome to this step-by-step Power BI tutorial where we’ll explore how to build a US Healthcare Analysis Dashboard that delivers deep, actionable insights into the healthcare industry.

In this tutorial, you’ll learn how to use Power BI to analyze patient demographics, hospital performance, payer-provider relationships, and regional healthcare trends.

By the end, you’ll understand how to:

  • Design a data model using a star schema
  • Build interactive visuals and filters
  • Derive insights for strategic healthcare decision-making

This project is perfect for BI professionals, data analysts, and healthcare administrators aiming to turn healthcare data into meaningful insights.

🧭 Project Objective

The goal of this Power BI project is to perform a comprehensive analysis of the US healthcare industry using interactive dashboards.

By integrating multiple data sources into a single BI model, we enable healthcare decision-makers to:

  • Track patient care trends
  • Monitor financial performance
  • Optimize payer-provider operations
  • Identify regional healthcare impacts

Ultimately, this dashboard empowers hospitals, payers, and policy stakeholders to make data-driven decisions that improve patient care quality and financial sustainability.

🧩 Data Model Design (Star Schema)

A strong data model is the backbone of any BI solution.

For this healthcare analytics project, we use a Star Schema, where the Fact Table is surrounded by Dimension Tables that describe patients, physicians, payers, and more.

Fact Table (FactTable)

Stores all key financial and operational metrics:

  • GrossCharge – Total charge for medical services
  • Payment – Payment received from payer or patient
  • Adjustment – Write-offs or contractual adjustments
  • Accounts Receivable (AR) – Outstanding revenue yet to be collected
  • CPTUnits – Number of times a medical procedure (CPT code) was performed

Linked through foreign keys to multiple dimension tables such as dimPatient, dimPhysician, and dimDate.

🧠 Dimension Tables Overview

1️⃣ Patient Table (dimPatient)

Contains patient details:

  • Name, Gender, Age, Height
  • Lifestyle factors: Tobacco, Alcohol, Exercise, Diet
  • Geographic data: City, State, Region, Zip Code

Helps analyze demographic and behavioral trends.

2️⃣ Physician Table (dimPhysician)

Captures provider details such as:

  • ProviderNPI – Unique physician identifier
  • ProviderSpecialty – Area of specialization
  • ProviderFTE – Time spent working weekly

Enables physician performance and workload analysis.

3️⃣ Specialty, Payer, and Transaction Tables

  • dimSpecialty: Defines provider specialties and types
  • dimPayer: Lists payer categories and insurance details
  • dimTransaction: Clarifies whether the transaction was a charge, payment, or adjustment

These help assess payer-provider interactions and financial efficiency.

4️⃣ Diagnosis & CPT Code Tables

  • dimDiagnosisCode: Includes diagnosis codes and groups
  • dimCPTCode: Contains procedure codes and their descriptions

Used to analyze treatment types and procedure frequency.

5️⃣ Date Table (dimDate)

Essential for time intelligence functions such as trend analysis.
Includes:

  • Year, Month, Day, DayName
  • MonthPeriod (YYYYMM format)
  • MonthYear (e.g., “March 2024”)

📊 Dashboard Development in Power BI

Step 1: Load and Transform Data

Use Power Query to load all dimension and fact tables.
Ensure relationships are correctly defined:

  • One-to-many from each dimension to the FactTable
  • Proper date formats for time intelligence

Step 2: Build the Data Model

Use Manage Relationships to link tables using their respective keys (e.g., dimPatientPK, dimDateServicePK).

Step 3: Create Measures

Use DAX to calculate KPIs such as:

Total Payment = SUM(FactTable[Payment])
Total Charges = SUM(FactTable[GrossCharge])
AR Ratio = DIVIDE(SUM(FactTable[AR]), SUM(FactTable[GrossCharge]))

Step 4: Design Interactive Visuals

Add visuals such as:

  • Bar & Column charts for hospital and payer comparisons
  • Pie charts for patient gender and region distribution
  • Line charts for trend analysis
  • Cards for displaying KPIs like Total Payment and Gross Charges

Step 5: Add Filters and Bookmarks

Enable slicers for:

  • Region, Hospital, Payer, Provider, and Patient demographics

Add bookmarks for key views like:

  • Executive Summary
  • Hospital Performance
  • Payer-Provider Insights

📈 Key Analytical Insights

✅ 1. Regional Impact Analysis

Identify which regions are most affected by high patient counts or medical costs — supporting better resource allocation.

✅ 2. Hospital Performance Evaluation

Highlight hospitals with high accounts receivable or insurance payment ratios for financial optimization.

✅ 3. Trend Analysis

Visualize monthly and yearly trends in healthcare expenses and admissions to spot seasonality or growth patterns.

✅ 4. Payer Contribution Assessment

Understand which payers contribute most to healthcare expenditures for better contract and reimbursement management.

✅ 5. Procedure Dominance Recognition

Determine the most common CPT codes or procedures performed — helpful for capacity planning and service optimization.

🧾 Conclusion

The US Healthcare Analysis Power BI Dashboard is a powerful BI solution that demonstrates how data visualization can transform healthcare decision-making.

By integrating diverse healthcare datasets into a unified analytical model, stakeholders gain visibility into:

  • Financial performance
  • Patient demographics
  • Treatment patterns
  • Payer-provider relationships

This project exemplifies how Power BI can drive data-informed strategies, enhance patient outcomes, and optimize operational efficiency across the healthcare ecosystem.

🏷️ SEO Keywords

Power BI Healthcare Dashboard, US Healthcare Analysis, Power BI Tutorial, Healthcare BI Project, Patient Demographics Dashboard, Hospital Performance Power BI, Payer Provider Analysis, Power BI Data Modeling, Healthcare Data Visualization, DAX for Healthcare Analytics

🧰 Tools Used

  • Microsoft Power BI
  • Power Query
  • DAX (Data Analysis Expressions)
  • SQL Server / Healthcare Data Warehouse

🧑‍💼 About the Author

Mian Kaleem, founder of BusinessIntelligence.Bi, is a Microsoft-certified Business Intelligence professional with over 20 years of experience transforming data into business insights. He specializes in Power BI, data warehousing, and advanced analytics for industries including healthcare, manufacturing, and finance.

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