Chapters

The Modeling Healthcare with Graphs Textbook is organized into 12 chapters covering 200 concepts.

Chapter Overview

Graph Theory and Database Foundations - Introduces fundamental graph theory concepts, nodes, edges, labeled property graphs, directed acyclic graphs, graph traversal, and relational database concepts that serve as prerequisites for healthcare graph modeling.
Introduction to Healthcare Systems - Covers healthcare system fundamentals including cost models (fee-for-service vs value-based care), healthcare stakeholders (patients, providers, payers), medical coding systems (ICD, CPT, HCPCS, Drug Codes), electronic health records, and healthcare interoperability.
Graph Query Languages and Technologies - Explores graph database query languages (Cypher, GQL, GSQL), graph pattern matching, query optimization, indexing strategies, and performance tuning techniques specific to graph databases.
Patient-Centric Data Modeling - Focuses on modeling patient records, demographics, medical history, diseases, conditions, symptoms, diagnoses, treatment plans, prescriptions, medications, lab tests, vital signs, care plans, patient journeys, and outcomes.
Provider Operations and Networks - Examines healthcare providers including primary care, specialists, hospitals, clinics, emergency departments, provider networks, schedules, appointments, referrals, credentials, performance metrics, care teams, and clinical guidelines.
Payer Perspective and Insurance Claims - Addresses insurance claims processing, adjudication, policies, coverage, benefit plans, copayments, deductibles, formularies, pharmacy benefit managers, prior authorization, utilization review, and reimbursement.
Healthcare Financial Analytics - Analyzes healthcare costs, revenue cycles, billing codes, charge masters, profitability, operating margins, payer mix, contract negotiation, provider compensation, capitation, risk adjustment, and value-based payment models.
Fraud Detection and Compliance - Investigates healthcare fraud patterns including upcoding, unbundling, phantom billing, duplicate claims, kickback schemes, referral network analysis, community detection, anomaly detection, and specialized fraud in behavioral health and DME.
Graph Algorithms and Analytics - Teaches graph algorithms including shortest path, centrality measures (PageRank, betweenness, degree), clustering coefficients, connected components, cycle detection, pattern recognition, similarity measures, link prediction, and graph embeddings.
AI and Machine Learning Integration - Integrates artificial intelligence, machine learning, large language models, vector stores, semantic search, RAG architecture, knowledge graphs, clinical decision support, clinical discovery, recommendation systems, and population health analytics.
Security, Privacy, and Data Governance - Ensures HIPAA compliance, protected health information handling, data privacy and security, access control, role-based access control, authentication, authorization, audit trails, de-identification, metadata management, data lineage, provenance, quality, governance, and explainability.
Capstone Projects and Real-World Applications - Culminates in capstone project development, presentations, understanding graph career paths, building healthcare analytics platforms, and implementing real-world solutions.

How to Use This Textbook

Progress through the chapters sequentially, as each chapter builds upon concepts introduced in previous chapters. All concept dependencies have been carefully structured to ensure that prerequisite knowledge is always covered before more advanced topics. Early chapters establish foundational knowledge in graph theory and healthcare systems, middle chapters explore the three key perspectives (patient, provider, and payer), and later chapters integrate advanced analytics, AI, and governance principles.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.