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Chapters

This textbook is organized into 29 chapters covering 513 concepts.

Chapter Overview

  1. Foundations of Graph Structures -- Introduces the basic building blocks of graphs: nodes, edges, labeled property graphs, directed/weighted graphs, and graph traversal.
  2. Data Modeling: Graphs vs. Relational Databases -- Contrasts graph data modeling with relational schema design, covering normalization, joins, and property graph models.
  3. Graph Query Languages and Pattern Matching -- Covers Cypher, GQL, GSQL, and the query optimization and indexing techniques used to retrieve graph patterns efficiently.
  4. Graph Database Scalability and Operations -- Covers scalability, sharding, distributed graph databases, high availability, and the operational tooling used to run graph databases in production.
  5. Graph Algorithms, Centrality, and Similarity Measures -- Introduces core graph algorithms for pathfinding, centrality, and similarity that reveal structural importance within a healthcare graph.
  6. Graph Embeddings, Clustering, and Graph Neural Networks -- Extends graph analytics into embeddings, clustering, and graph neural networks used to learn representations from graph-structured data.
  7. Healthcare Economics and Medical Coding Systems -- Establishes healthcare cost models, care models, and the ICD/CPT/HCPCS/drug coding systems used to represent clinical data.
  8. Healthcare Interoperability and Care Coordination -- Covers HL7 standards, health information exchange, and the care-coordination concepts that connect healthcare systems together.
  9. Patient Diagnosis, Treatment, and Medication -- Covers the core clinical concepts of diagnosis, treatment planning, prescriptions, and medication safety.
  10. Patient Care Plans and Chronic Disease Management -- Covers lab data, vital signs, care plans, and the ongoing management of chronic disease and preventive care.
  11. Specialty Care, Surgery, and Remote Monitoring -- Covers diagnostics, surgical and post-operative care, and the growing role of telehealth and remote patient monitoring.
  12. Provider Organizations, Networks, and Scheduling -- Introduces the provider perspective: hospitals, clinics, provider networks, scheduling, and referrals.
  13. Clinical Guidelines, Care Pathways, and Provider Workforce -- Covers evidence-based guidelines, care pathways, credentialing, and healthcare workforce concepts.
  14. Insurance Claims, Coverage, and Pharmacy Benefits -- Introduces the payer perspective through claims processing, coverage, and pharmacy benefit management.
  15. Reimbursement, Health Plan Types, and Payer Contracts -- Covers reimbursement, health plan types, payer contracts, and claims-processing operations.
  16. Healthcare Revenue and Cost Analysis -- Covers revenue cycles, billing codes, cost analysis, and value-based payment models.
  17. Healthcare Financial Forecasting and Risk -- Covers financial forecasting, collections, cost containment, and financial risk management.
  18. Healthcare Fraud Patterns and Detection -- Introduces common healthcare fraud, waste, and abuse patterns and the analytics used to detect them.
  19. Fraud Investigation and Compliance -- Covers fraud investigation workflows, regulatory enforcement, and fraud analytics tooling.
  20. AI, LLMs, and Knowledge Graphs for Healthcare -- Surveys foundational AI/ML, LLMs, RAG, context graphs, and enterprise knowledge graphs applied to healthcare.
  21. Responsible AI and Agentic Systems -- Covers NLP, model evaluation, bias, explainability, and agentic AI workflows in healthcare applications.
  22. FHIR Resources and Levels of Knowledge Representation -- Introduces HL7 FHIR resources and the Four Levels of Knowledge Representation and Tiers of Functionality.
  23. Clinical Guideline Authoring and Clinical Quality Language -- Covers guideline authoring and CQL, including the Expression Logical Model (ELM) and Clinical Quality Measures.
  24. CDS Hooks, Care Alerts, and CMS CQL Tooling -- Covers CDS Hooks, clinical alerts, and CMS-sponsored tooling (MADiE, CQL Runner, Bonnie, Cypress).
  25. Healthcare Data Security Fundamentals -- Covers HIPAA, access control, authentication, and encryption of protected health information.
  26. Advanced Security Operations and Incident Response -- Covers consent management, zero trust architecture, and security incident response practices.
  27. Data Governance and Metadata Management -- Covers metadata management, data lineage, provenance, and explainability practices.
  28. Data Quality, Stewardship, and Compliance -- Covers data quality scoring, stewardship roles, retention policy, and regulatory compliance reporting.
  29. Capstone Projects and Career Development -- Guides students through scoping, building, and presenting a capstone project.

How to Use This Textbook

Chapters are ordered so that every concept's prerequisites appear in an earlier chapter -- work through them in sequence rather than skipping ahead. The book follows three interleaved arcs: graph theory and technology foundations (Chapters 1-6), the healthcare domain across patient, provider, and payer perspectives (Chapters 7-19), and advanced topics in AI, clinical decision support, security, governance, and the capstone (Chapters 20-29).

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