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Course Description Quality Assessment

Course Title

Modeling Healthcare Data with Graphs

Overall Score: 100/100

Quality Rating: Excellent - Ready for Learning Graph Generation

Detailed Scoring Breakdown

Element Points Earned Max Points Status
Title 5 5 ✓ Complete
Target Audience 5 5 ✓ Complete
Prerequisites 5 5 ✓ Complete
Main Topics Covered 10 10 ✓ Excellent
Topics Excluded 5 5 ✓ Complete
Learning Outcomes Header 5 5 ✓ Complete
Remember Level 10 10 ✓ Excellent
Understand Level 10 10 ✓ Excellent
Apply Level 10 10 ✓ Excellent
Analyze Level 10 10 ✓ Excellent
Evaluate Level 10 10 ✓ Excellent
Create Level 10 10 ✓ Excellent
Descriptive Context 5 5 ✓ Excellent

Assessment Details

Title (5/5 points)

Clear, descriptive title: "Modeling Healthcare Data with Graphs"

Target Audience (5/5 points)

Specific audience identified: College Undergraduate

Prerequisites (5/5 points)

Prerequisites clearly stated: Knowledge of databases

Main Topics Covered (10/10 points)

Exceptional breadth with 90+ topics covering:

  • Graph fundamentals (nodes, edges, properties, Cypher, GQL)
  • Healthcare domain concepts (ICD, CPT, HCPCS codes, claims, encounters)
  • Multiple perspectives (patient, provider, payer)
  • Advanced topics (AI, LLMs, vector stores, fraud detection)
  • Security and compliance (HIPAA, RBAC)
  • Data governance (metadata, lineage, traceability, explainability)

Topics Excluded (5/5 points)

Clear boundaries set with excluded topics:

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

Learning Outcomes Header (5/5 points)

Clear statement present: "After completing this course, students will be able to:"

Bloom's Taxonomy Coverage

Remember Level (10/10 points)

5 specific, measurable outcomes using appropriate verbs (define, recall, identify, list):

  • Define key healthcare terminology
  • Recall differences between care models
  • Identify graph components
  • List major data entities
  • Recall graph query languages

Understand Level (10/10 points)

5 specific outcomes using appropriate verbs (explain, describe, summarize, interpret, discuss):

  • Explain relational database limitations
  • Describe graph database advantages
  • Summarize graph algorithm applications
  • Interpret metadata and governance concepts
  • Discuss AI/LLM integration

Apply Level (10/10 points)

5 specific outcomes using appropriate verbs (construct, write, use, apply, demonstrate):

  • Construct patient-centric graph models
  • Write graph queries
  • Use graph algorithms
  • Apply RBAC concepts
  • Demonstrate AI integration

Analyze Level (10/10 points)

5 specific outcomes using appropriate verbs (decompose, analyze, examine, evaluate, assess):

  • Decompose healthcare workflows
  • Analyze claims for fraud detection
  • Examine multi-system data connections
  • Evaluate modeling strategies
  • Assess data quality impact

Evaluate Level (10/10 points)

5 specific outcomes using appropriate verbs (critique, judge, evaluate, compare, assess):

  • Critique existing data models
  • Judge algorithm appropriateness
  • Evaluate privacy implications
  • Compare business value
  • Assess explainability

Create Level (10/10 points)

Comprehensive capstone outcomes with 4 distinct project options:

  • Design comprehensive healthcare graph schema
  • Build prototype applications (fraud detection, clinical decision support, network optimization, patient journey)
  • Develop capstone projects addressing real healthcare challenges
  • Present results with lineage and impact demonstration

Descriptive Context (5/5 points)

Excellent context provided:

  • Compelling rationale linking healthcare costs to graph solutions
  • Clear explanation of course value
  • Real-world application focus
  • Integration of emerging technologies

Estimated Concept Generation Potential

Estimated Concepts: 250-300

This significantly exceeds the 200-concept requirement due to:

  1. Topic Breadth: 90+ main topics provide strong foundation
  2. Multiple Perspectives: Patient, provider, and payer viewpoints
  3. Technical Depth: Specific technologies, algorithms, and standards
  4. Bloom's Diversity: All six cognitive levels ensure variety
  5. Application Domains: Multiple use cases and practical applications

Strengths

  1. Comprehensive Coverage: Exceptional breadth from fundamentals to advanced topics
  2. Multi-Perspective Approach: Patient, provider, and payer viewpoints enrich concept diversity
  3. Complete Bloom's Taxonomy: All six cognitive levels with 5 specific outcomes each
  4. Strong Capstone Integration: Four distinct project options with clear deliverables
  5. Rich Context: Compelling rationale connecting costs to solutions
  6. Clear Boundaries: Well-defined scope through excluded topics

Recommendations

Status: APPROVED - Proceed with Learning Graph Generation

Your course description scores 100/100, well above the minimum threshold of 70 points. It contains all necessary elements to generate a comprehensive, high-quality learning graph with 200+ concepts.

The combination of extensive topic coverage, multiple domain perspectives, complete Bloom's Taxonomy outcomes, and integration of modern technologies provides an excellent foundation for learning graph generation.

Next Step: Proceed to concept enumeration and dependency graph creation.

Assessment Date: November 6, 2025 Assessor: Claude (Learning Graph Generator Skill)