Unlocking Healthcare Insights: First Look at Snowflake's Synthetic Dataset

The healthcare industry sits on a mountain of data, yet accessing it for research and innovation is a monumental challenge due to privacy concerns. As a data scientist, I'm always looking for ways to explore complex healthcare problems without compromising patient privacy.

I was excited to discover a new resource that could be a game-changer: the Snowflake Synthetic Healthcare Data listing on the Snowflake Marketplace. This dataset simulates a massive, realistic healthcare database with over 1.4 million patients and hundreds of millions of encounters and claims. It’s an invaluable tool for developing, testing, and demonstrating healthcare applications in a safe, privacy-compliant environment.

🧑‍⚕️ 1.4 million patients

🏥 65 million encounters

🧾 124 million claims

📄 887 million claim details

You can explore the listing yourself right here:

My immediate goal was to put this dataset to the test with a real-world question:

How can we identify communities that would benefit most from educational initiatives like continuous glucose monitoring (CGM) camps for diabetes management?

You can read about my introduction to CGM’s and Glucose Data Visualizations here

Step 1: The Data Schema — Our Map to the Data

Before I could write a single line of code, I needed to understand the structure of the data. I spent some time exploring the data schema provided by the Snowflake listing. This is arguably the most crucial step

I quickly identified the key tables I would need to answer my question:

• PATIENTS: This table holds demographic and location information like CITY and STATE. This is where I'd find the geographic data needed for my analysis.
• CONDITIONS: This table contains all the diagnosis information, including the DESCRIPTION of each condition and a foreign key to the PATIENT table. This is where I would filter for our target population—those with a diabetes diagnosis.

Other tables, like ENCOUNTERS and CLAIMS, could provide deeper insights into patient journeys and healthcare utilization, but for my initial question, the PATIENTS and CONDITIONS tables were the perfect starting point.

Step 2: The SQL Query — Translating a Question into Code

With a clear understanding of the schema, I was ready to write my query. My goal was to count the number of patients with a diabetes diagnosis in each city and state to identify the communities with the greatest need.

SELECT
    p.synthea_city,
    COUNT(DISTINCT p.patient_id) AS diabetes_patient_count
FROM
    SILVER.PATIENTS AS p
JOIN
    SILVER.CONDITIONS AS c ON p.patient_id = c.patient_id
WHERE
    c.description ILIKE '%diabetes%'
GROUP BY
    p.synthea_city
ORDER BY
    diabetes_patient_count DESC
LIMIT 5;

This query joins the patient and condition data, filters for a LIKE match on 'diabetes' in the condition description, and then aggregates the count of unique patients by city.

The ORDER BY clause lets us find the top locations with the highest concentration of patients, and the LIMIT 5 clause gives us a concise, actionable list.

Step 3: The Insight — Identifying High-Impact Areas

The results of the query were immediately insightful and gave a clear, data-backed answer to my initial question. The top 5 locations for a potential educational initiative are:

• Cleveland: 207,560 patients
• Chicago: 141,779 patients
• San Francisco: 48,542 patients
• Austin: 39,728 patients
• Detroit: 33,806 patients

This simple analysis, made possible by the rich and realistic nature of the synthetic data, provided a clear, data-backed answer to my initial question. It gives a tangible starting point for a public health initiative. An organization could use these findings to strategically launch a pilot program for free CGM educational camps in these specific cities, maximizing their potential impact.

My Takeaways & Next Steps

This initial exploration of the Snowflake synthetic healthcare dataset has been incredibly rewarding. It shows that even a simple query, when backed by realistic and well-structured data, can yield powerful and actionable insights.

For any data scientist or developer working on healthcare applications, this dataset is an invaluable asset. It allows for rapid prototyping and testing without the immense challenges and regulatory hurdles of real patient data.

My next steps with this dataset will be to:

• Characterize patient demographics: Dive deeper into age, gender, and race to tailor educational materials more effectively.
• Analyze medication usage: Investigate the types of medications prescribed to understand patient care pathways and potential gaps.
• Visualize the data: Create interactive maps and dashboards to better communicate these findings to stakeholders.

The possibilities are endless, and it’s just getting started!