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About This Course

A Journey from Tables to Networks

In February 2007, everything I thought I knew about databases changed forever.

I'd spent years believing that relational tables were the only way to manage production data. Then I discovered NoSQL databases—and realized I'd been solving complex problems the hard way for far too long. That revelation launched a journey that transformed my career: I founded the NoSQL Now! conference series, co-authored Making Sense of NoSQL (Manning Publications), and became a consultant helping organizations break free from the limitations of traditional databases.

Real-World Scale, Real-World Impact

Around 2015, while working for a Fortune 10 healthcare company managing both insurance and hospital operations, I discovered something remarkable: graph databases could solve healthcare's most intractable data problems—but almost no one in healthcare knew they existed.

The numbers tell the story:

  • 3,000+ professionals trained in healthcare graph technologies
  • 25 billion vertices and 630 billion edges in our production graph
  • 300 million lives queried in seconds, not hours
  • Queries that took days in relational systems now complete in under 5 seconds

Healthcare generates some of the most interconnected data on the planet. Patients visit multiple providers, receive dozens of medications, undergo countless procedures, and accumulate complex medical histories spanning decades. Traditional relational databases struggle with this complexity, requiring expensive JOINs that slow to a crawl as datasets grow. Graph databases turn this challenge into an advantage by treating relationships as first-class citizens.

Why This Course Exists

The hardest part wasn't building the graph—it was training people to think differently about data. Our best instructors were too busy managing billion-edge graphs to create training materials. Building educational content was painfully slow.

Then large language models arrived and changed everything.

This course represents a fusion of battle-tested expertise from real-world healthcare graph implementations and cutting-edge AI-powered educational tools. Every chapter includes interactive MicroSims—hands-on visualizations you can explore and modify in your browser—generated and refined to help you master graph concepts through experimentation, not just reading.

Four Perspectives, One Interconnected System

Healthcare data tells different stories depending on who's asking the questions. This course teaches you to model healthcare from four critical viewpoints:

  1. The Patient Perspective — "Tell me everything about my health and how to stay healthy"
  2. Personal health records, family history, longitudinal care journeys
  3. Medication interactions, allergies, and treatment histories

  4. Wellness recommendations and preventive care pathways

  5. The Provider Perspective — "Help me deliver better care more efficiently"

  6. Hospital operations, provider networks, referral patterns
  7. Clinical decision support and evidence-based protocols

  8. Care team coordination and quality metrics

  9. The Payer Perspective — "Help me serve members while managing costs"

  10. Claims processing, fraud detection, utilization management
  11. Network adequacy, member attribution, risk adjustment

  12. Value-based care and population health analytics

  13. The Population Health Perspective — "Help me understand how healthcare systems can serve everyone better"

  14. Epidemiological trends, social determinants of health
  15. Healthcare disparities and access gaps
  16. System-wide efficiency and outcome optimization

Why This Matters

Here's the uncomfortable truth: Americans pay nearly twice as much per person for healthcare as citizens of other developed nations—yet live shorter lives.

This isn't a technology problem. It's a data problem.

Healthcare data is siloed, fragmented, and locked in systems that can't talk to each other efficiently. Doctors can't see complete patient histories. Patients can't understand their own data. Payers spend billions on fraud that pattern analysis could catch. Researchers struggle to find connections hidden in plain sight.

Graph databases can change this.

What You'll Learn

By the end of this course, you'll be able to:

  • Design graph schemas that naturally model healthcare's interconnected entities
  • Write efficient graph queries using Cypher and other graph query languages
  • Build patient-centric data models supporting longitudinal care and precision medicine
  • Optimize provider networks using graph algorithms for centrality and community detection
  • Detect fraud patterns through network analysis of claims and providers
  • Integrate AI and machine learning with graph analytics for predictive healthcare
  • Ensure HIPAA compliance while leveraging relationship-rich data
  • Deploy production graph systems that scale to billions of edges

Most importantly, you'll learn to think in graphs—to see connections where others see tables, to find patterns where others see noise.

A Personal Note

This course distills everything I learned building and managing one of the world's largest healthcare graph databases. Every pattern, every example, every pitfall warning comes from real implementations touching hundreds of millions of lives.

My sincere hope is that you'll use this knowledge to make healthcare more accessible, more affordable, and more effective for people around the world.

The tools are ready. The data is waiting. The impact is yours to create.

Let's build something that matters.

Dan McCreary Graph Database Architect, NoSQL Pioneer, Healthcare Data Advocate