AI Driven Electronic Health Records Data Management Course Syllabus

Overview: This course provides a comprehensive introduction to AI-driven electronic health records (EHR) and data management, designed for beginners interested in healthcare data systems. Through six structured modules, learners will explore foundational computing concepts, AI techniques, and practical applications in healthcare data environments. The course blends theory, hands-on labs, and real-world case studies to build skills in data management, compliance, and AI integration. Estimated time to complete: 15–20 hours.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Introduction to key concepts in foundations of computing & algorithms
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Assessment: Quiz and peer-reviewed assignment

Module 2: Neural Networks & Deep Learning

Estimated time: 2 hours

• Understand core AI concepts including neural networks and deep learning
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Hands-on exercises applying AI system design & architecture techniques
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Module 4: Natural Language Processing

Estimated time: 4 hours

• Review of tools and frameworks commonly used in practice
• Hands-on exercises applying natural language processing techniques
• Implement prompt engineering techniques for large language models
• Assessment: Quiz and peer-reviewed assignment

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

• Introduction to key concepts in computer vision & pattern recognition
• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Assessment: Quiz and peer-reviewed assignment

Module 6: Deployment & Production Systems

Estimated time: 3 hours

• Review of tools and frameworks commonly used in practice
• Hands-on exercises applying deployment & production systems techniques
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Basic understanding of healthcare systems or data concepts
• Familiarity with fundamental computing principles
• Interest in health data management or AI applications in healthcare

What You'll Be Able to Do After

• Apply computational thinking to solve complex engineering problems in healthcare
• Implement intelligent systems using modern AI frameworks and libraries
• Build and deploy AI-powered applications for real-world healthcare use cases
• Evaluate model performance using appropriate metrics and benchmarks
• Manage electronic health records with AI-driven data optimization and compliance strategies