Concept Taxonomy for Modeling Healthcare Data with Graphs
This document defines the categorical taxonomy for organizing the 200 concepts in the learning graph. Each category has a TaxonomyID (3-5 letter abbreviation) and a description of the concepts it contains.
Taxonomy Categories
1. Foundation Concepts
TaxonomyID: FOUND
Description: Fundamental concepts in graph theory and database systems that serve as prerequisites for understanding healthcare graph modeling. Includes basic graph structures, database concepts, and data modeling principles.
2. Graph Technologies
TaxonomyID: GTECH
Description: Technical concepts related to graph databases, query languages, and graph database implementation. Includes Cypher, GQL, GSQL, indexing, query optimization, and performance tuning.
3. Healthcare Domain
TaxonomyID: HCARE
Description: Core healthcare system concepts, terminology, and organizational structures. Includes healthcare economics, care models, medical coding systems, and healthcare interoperability.
4. Patient Data
TaxonomyID: PAT
Description: Patient-centric concepts covering clinical data, diagnoses, treatments, medications, and patient care management. Includes patient records, care plans, outcomes, and patient journey mapping.
5. Provider Operations
TaxonomyID: PROV
Description: Healthcare provider concepts including hospitals, clinics, physicians, scheduling, referrals, and provider networks. Covers credentials, performance metrics, and care team coordination.
6. Payer & Insurance
TaxonomyID: PAYER
Description: Insurance and payer-related concepts including claims processing, policies, coverage, benefits, and reimbursement. Includes pharmacy benefit management and utilization review.
7. Financial & Business
TaxonomyID: FIN
Description: Healthcare business and financial concepts covering revenue cycles, cost analysis, profitability, contracts, and value-based payment models. Includes billing codes and charge masters.
8. Fraud & Compliance
TaxonomyID: FRAUD
Description: Concepts related to detecting and preventing fraud, waste, and abuse in healthcare. Includes fraud detection techniques, anomaly detection, and specific fraud patterns.
9. Graph Analytics
TaxonomyID: ANAL
Description: Graph algorithms and analytical techniques including pathfinding, centrality measures, community detection, clustering, and graph pattern recognition. Includes graph embeddings and neural networks.
10. AI & Machine Learning
TaxonomyID: AI
Description: Artificial intelligence and machine learning concepts applied to healthcare. Includes LLMs, vector stores, semantic search, clinical decision support, predictive analytics, and recommendation systems.
11. Security & Privacy
TaxonomyID: SEC
Description: Healthcare data security and privacy concepts including HIPAA compliance, access control, authentication, authorization, and de-identification of protected health information.
12. Data Governance
TaxonomyID: GOV
Description: Data management, governance, and quality concepts including metadata management, data lineage, provenance, traceability, and explainability. Includes master data management and data stewardship.
13. Capstone & Career
TaxonomyID: CAP
Description: Final capstone projects, presentations, career development, and real-world implementation concepts. Represents the culmination of learning and application to practical scenarios.
Distribution Guidelines
Each category should ideally contain:
- Minimum: 5-10 concepts
- Target: 10-20 concepts
- Maximum: 30 concepts (avoid exceeding 15% of total)
The MISC category should be used sparingly for concepts that don't fit clearly into other categories.
Generated for Modeling Healthcare Data with Graphs course