🏥 Healthcare Claims Analysis

📌 Project Overview

This project analyzes healthcare claims data to uncover insights on denial rates, provider performance, payment trends, and patient demographics.
The analysis is supported with SQL queries, Python (pandas, matplotlib, plotly), and professional visualizations.

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📊 Key Analyses & Visualizations

• Denial Rates by Hospital Accreditation Level
• Providers with the Highest Denial Rates
• Average Payment per Day by Specialty
• Monthly Claims Trend
• Average Length of Stay by Specialty
• Copay Share by Insurance Plan
• Top CPT Procedures by Total Paid
• Top DRGs by Specialty
• Denial Rates by City & Specialty
• Denial Rates by Gender & Age Groups

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🗂️ Data Dictionary (Summary)

p5_claims

• claim_id → Unique claim ID
• patient_id, provider_id → Foreign keys
• admit_dt, discharge_dt → Episode dates
• drg → Diagnosis group
• paid_amt, copay_amt → Payment info
• denied_flag → Denial indicator

p5_patients

• patient_id → Unique patient ID
• age_band → {0-17, 18-34, 35-49, 50-64, 65+}
• gender → {F, M}
• plan_type → {HMO, PPO, EPO}

p5_procedures

• claim_id → Claim reference
• cpt → Procedure code
• paid_amt, units → Billing details

p5_providers

• provider_id → Unique provider ID
• specialty → {Cardiology, Orthopedics, General, Pediatrics, Oncology, Obstetrics, Neurology}
• city → {Cairo, Giza, Alexandria, Riyadh, Jeddah, Dubai, AbuDhabi}
• accreditation_level → {A, B, C}

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