Course Description for Modeling Healthcare Data with Graphs

Title: Modeling Healthcare Data with Graphs

Target Audience: College Undergraduate

Prerequisites: Knowledge of databases

Course Overview

The annual per-person cost for healthcare in the United States is the highest in the world by a significant margin. Modern graph databases offer powerful ways to lower these costs by providing superior analytics that enable healthcare organizations to transition from expensive fee-for-service systems to value-based care models.

This course teaches students to model complex healthcare data from multiple perspectives, including the patient, provider, and payer viewpoints. We begin with a review of the many challenges of modeling complex interconnected clinical data. We discuss why tabular relational structures are poorly suited for clinical data and how graph databases excel at this task. We introduce key concepts including graph algorithms, graph data science, embeddings, and graph neural networks.

The course starts with a patient-centric graph data model that places the patient record at the center of our graph, including modeling patient attributes such as demographics, diseases, conditions, prescriptions, diagnoses, treatments, symptoms, procedures, providers, and care plans. We then expand to include the provider perspective, which encompasses modeling individual providers, hospitals, clinics, schedules, claims, costs, revenue, and profitability. Next, we examine the payer perspective, which includes claims processing, fraud detection, waste and abuse prevention, provider networks, policies, coverage, disputes, population analytics, and profitability analysis.

The core of our course examines how graph databases support analytics, clinical rules engines, recommendation systems, and data quality initiatives within healthcare. We take a close look at vector stores used in conjunction with AI and large language models (LLMs).

We then focus on the types of analytics used by each stakeholder perspective and discuss operational reporting, key performance indicators (KPIs), and real-time clinical discovery applications that benefit from graph databases. Finally, we conclude with advanced topics including security, HIPAA compliance, role-based access control (RBAC), metadata management, data governance, lineage tracking, traceability, explainability, and the use of machine learning and LLMs to create precise care path recommendations that combine quality with explainability.

Main Topics Covered

Annual Costs
Per Person Cost
Fee for Services
Value-based Care
Graph Data Models
Legacy Healthcare
Labeled Property Graph
Node
Node Properties
Edge
Edge Property
Graph Query
Cypher
GQL
Graph Performance
Persona
Patient Viewpoint
Provider Viewpoint
Payer Viewpoint
Hospital
Clinic
Primary Care Provider
Demographic
Encounter
Disease
Condition
Prescription
Diagnosis
Treatment
Symptom
Procedure
ICD Code
CPT Code
HCPCS Code
Drug Code
Provider
Primary Care Provider
Hospital
Clinic
Schedule
Calendar
Claims
Cost
Revenue
Profitability
Fraud
Waste
Abuse
Provider Network
Policy
Coverage
Benefit Plan
Copay
Deductible
Disputes
Pharmacy Benefit Manager
Formulary Rule
Brand Drug
Generic Drug
Rules in Graph
Rules Engine
Clinical Decision
Clinical Decision Support
Clinical Discovery
Operational Reporting
Key Performance Indicator
AI
Machine Learning
LLM
Expandability
Graph and LLMs are Complementary
Vector Store
Security
HIPAA
Role-based Access Control
Fraud Detection
Community Detection
Provider Network Fraud
Referral
Referral Inference
Durable Medical Equipment
DME Fraud
Behavioral Health
Neurodiversity
Behavioral Health Fraud
Metadata
Data governance
Lineage
Traceability
Explainability
Projects
Capstone Project
Capstone Presentation
Job Market for Graph Modeling
Using AI in the Course

Topics Not Covered

Resource Description Format
Semantic Web
Mainframes
COBOL
SAS
Statistics
Legacy Conversion to Graph

Learning Outcomes

After completing this course, students will be able to:

Remember

Retrieving, recognizing, and recalling relevant knowledge from long-term memory.

Define key healthcare terminology including ICD, CPT, HCPCS, and Drug Codes.
Recall the differences between fee-for-service and value-based care models.
Identify the main components of a labeled property graph (nodes, edges, properties).
List the major data entities in healthcare (patients, providers, payers, claims, treatments, encounters).
Recall common graph query languages (Cypher, GSQL, GQL) and their basic syntax.

Understand

Constructing meaning from instructional messages, including oral, written, and graphic communication.

Explain why relational databases struggle to model complex, interconnected healthcare data.
Describe how graph databases enable more effective modeling of patient-provider-payer relationships.
Summarize how graph algorithms can support clinical decision support and fraud detection.
Interpret how metadata, lineage, and traceability improve healthcare data governance.
Discuss how AI and LLMs can be integrated with vector stores and graph data for advanced analytics.

Apply

Carrying out or using a procedure in a given situation.

Construct a patient-centric healthcare graph model using labeled property graph principles.
Write graph queries to explore relationships among patients, providers, and payers.
Use graph algorithms (e.g., shortest path, community detection) to find clinical insights.
Apply role-based access control (RBAC) concepts to secure healthcare graph data.
Demonstrate integration of AI tools or vector stores to query and reason over graph data.

Analyze

Breaking material into constituent parts and determining how the parts relate to one another and to an overall structure or purpose.

Decompose a complex healthcare workflow into entities and relationships suitable for graph modeling.
Analyze claims and referral data to detect potential fraud, waste, or abuse patterns.
Examine how data from multiple systems (EHR, claims, pharmacy, behavioral health) connect through shared identifiers.
Evaluate trade-offs between different graph modeling strategies for scalability, query speed, and explainability.
Assess how data quality and completeness impact graph analytics outcomes.

Evaluate

Making judgments based on criteria and standards through checking and critiquing.

Critique the design of an existing healthcare data model for completeness and correctness.
Judge the appropriateness of a chosen graph algorithm for a given healthcare analytics use case.
Evaluate the ethical and privacy implications of patient graph data under HIPAA standards.
Compare the business value of graph-based analytics versus traditional relational approaches in healthcare.
Assess the explainability and transparency of AI-driven clinical recommendations derived from graph data.

Create

Putting elements together to form a coherent or functional whole; reorganizing elements into a new pattern or structure.

Design and implement a comprehensive healthcare graph schema integrating patient, provider, and payer data.
Build a prototype graph application for one of the following:
- Fraud detection using claims graph analytics
- Clinical decision support using rule-based reasoning and embeddings
- Provider network optimization through community and referral graph analysis
- Patient journey visualization using time-sequenced graph queries
Develop a capstone project that combines graph modeling, analytics, and AI integration to address a real healthcare challenge (e.g., improving care quality, reducing costs, or enhancing interoperability).
Present the capstone project results, demonstrating data lineage, explainability, and measurable impact.

Use of AI

Use of AI in this course is strongly encouraged. We will provide a library of skills that can be used to increase the quality of your capstone projects.