ARTOGRAPH TASK 3.C

Analytical SQL Queries for EMR Insights

Requirements

• No special dependencies required for SQL-only execution.
• You can run the project using any of the following:
  • SQLite Online Editor
  • DB Browser for SQLite (desktop app)
  • Any local or cloud SQL-compatible tool (e.g., MySQL, PostgreSQL, SQLite)

Overview

This project showcases analytical SQL skills by querying a simulated EMR (Electronic Medical Records) database. It involves complex joins, filtering, and aggregation to extract actionable clinical insights such as disease tracking, patient imaging history, and visit volume analysis.

Features

• Multi-table JOINs across Patients, Visits, Diagnoses, and Medical_Images
• Filters based on date and condition codes
• Aggregation logic to count visits and images per patient
• Organized for quick local execution and testing with SQLite

Files

File	Description
setup.sql	Schema and dummy data for EMR simulation
query.sql	Contains the 3 key analytical queries with inline comments
README.md	Complete documentation to run and understand the project

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Database Schema

Patients(patient_id, name, dob)
Visits(visit_id, patient_id, visit_date)
Diagnoses(diagnosis_id, visit_id, condition_code)
Medical_Images(image_id, patient_id, image_type, generated_date)

Challenges Faced

Designing a compact schema representative of a real EMR system.

Ensuring SQL compatibility across platforms like SQLite.

Writing clear, efficient queries to simulate clinical reporting logic.