How NYC Health + Hospitals Delivers Patient Data in 5 Minutes with Snowflake
NYC Health + Hospitals is the largest municipal health system in the United States, serving over 1.4 million New Yorkers across more than 70 care locations, including a significant focus on homeless populations. The system rebuilt its data infrastructure on Snowflake’s AI Data Cloud to centralize over 100 billion rows of healthcare data, enabling secure sharing and real-time analytics. A rearchitected data ingestion pipeline now delivers health plan membership updates to stakeholders in five minutes instead of five days.
Impact
5 minutes
Time to deliver membership data to stakeholders
100 billion+
Healthcare data rows stored in Snowflake
70+
Patient care locations served
1.4 million
Patients served annually
Challenge
Membership and claims data from multiple health plan payers took five days to process and deliver to internal stakeholders, leaving care coordinators working from stale records when serving New York’s most vulnerable homeless patients.
Solution
NYC Health + Hospitals rearchitected data ingestion on Snowflake, creating automated pipelines for payer data, deploying Snowflake Secure Data Sharing for partner collaboration, and applying Horizon Catalog for RBAC and compliance governance.
What Leaders Say
“With Snowflake, our end-to-end data delivery time for membership and claims data has improved from five days to five minutes. That’s a massive gain in performance.”
“We’re talking about working with sensitive patient data — data that’s related to people’s mental health and specific clinical conditions. So, we have to build in security, governance, auditability, traceability and compliance from the very beginning, which Snowflake helps us achieve.”
“Our guiding star is to provide care to all New Yorkers. The efficiency and performance improvements we’ve achieved with Snowflake all contribute to this ultimate mission.”
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Full Story
In a city of eight million, NYC Health + Hospitals carries an outsized burden: it is the safety net for those with nowhere else to go. With homelessness in New York City reaching levels not seen since the Great Depression, the health system needed to understand its most vulnerable patients not just as episodic cases but as whole people — with shelter histories, chronic conditions, and complex social circumstances. That required data infrastructure equal to the challenge.
Before modernizing on Snowflake, NYC Health + Hospitals managed healthcare data across fragmented systems. Payer data from MetroPlusHealth and other insurance subsidiaries arrived on five-day cycles, leaving care coordinators, managed care teams, and finance departments working from stale membership records. Protected health information from dozens of sources demanded careful governance, but legacy tools made security and auditability cumbersome rather than built-in.
The solution centered on building a unified data hub on Snowflake. The team rearchitected payer data ingestion to create automated pipelines that pull membership and claims data continuously. Snowflake Secure Data Sharing allowed external partners and city agencies to access and write to shared databases without moving data or duplicating it. Snowflake Horizon Catalog provided role-based access controls and audit trails that satisfied the compliance requirements of a public healthcare system handling sensitive mental health and clinical records.
The operational shift was profound. Membership data that once took five days to process now arrives in five minutes — a 1,440x improvement in delivery time. That speed means care coordinators can intervene with timely information when a patient appears in an emergency room after a shelter visit, connecting the dots that previously required days of administrative delay. Over 100 billion rows of healthcare data now reside in a single, governed environment, providing a holistic view of patient populations that was previously impossible.
Looking ahead, NYC Health + Hospitals is using Snowflake Cortex AI to experiment with generative AI across dozens of identified high-priority use cases. An enterprise AI advisory board is evaluating how to deploy AI responsibly in a system that serves populations already marginalized by societal bias — ensuring that the models amplifying care do not amplify inequity.