Overview

EastMark Health is a US-based hospital chain. Their patient data was fragmented across separate EHR, claims, and lab systems, so there was no single view of a patient and no secure way to share it across departments, facilities, or payers without making copies. To solve this, Bacancy Technology built a healthcare data sharing platform on Snowflake, consolidating the EHR, claims, and lab data into one governed patient record and using Snowflake Secure Data Sharing to give payers and partner hospitals live read only access with no copies. Access was rebuilt with role-based controls, masking, and query-level audit to keep protected health information HIPAA-compliant, and readmission and risk models ran on the live data with Snowpark and Cortex. This made reporting 45% faster, cut infrastructure and maintenance costs by 78%.

Technologies Used

Snowflake
Python
SQL
dbt
Git
Docker

Project Highlights

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Secure Data Sharing deployed to all payers and partner hospitals on Snowflake, with no data copied

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EHR, claims, and lab data consolidated into a single governed patient record on Snowflake, with complete validation

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Readmission and risk-prediction models built on Snowflake using Snowpark and Cortex

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All patient data secured with role-based access, column masking, and query-level audit, matched to HIPAA

The Challenges

1

Patient information was distributed across separate EHR, claims, and laboratory systems, with each maintaining its own records. To collect a complete medical history, staff had to query each system separately, which delayed access to information when patients moved between departments or facilities.

2

The legacy data warehouse ran on fixed compute shared across teams, so it could not handle many workloads at once. When analytics and quality reporting ran their reports at the same time, queries queued behind one another, the system slowed, and large jobs failed to finish, so teams had to schedule around each other or run heavy work overnight.

3

Access to patient data could only be granted in broad terms, not limited to specific tables or columns, so staff routinely saw more information than their role required. Sensitive fields could not be masked, and the systems kept no record of who viewed which data, leaving the provider unable to demonstrate how protected health information was accessed.

4

The existing infrastructure could not support AI or machine learning workloads. The data was fragmented and held in formats that model development could not readily use, and the platform offered no scalable compute to train or run models, which left the provider unable to pursue applications such as risk prediction and clinical decision support.

Solutions by Bacancy

1

Our Snowflake migration experts consolidated all data from the EHR, claims, and laboratory systems into a single governed platform on Snowflake, giving each patient one complete record. We used Snowflake’s secure data sharing to let departments and outside facilities work from the same live data without copies, so staff could pull a full medical history from one place instead of querying each system.

2

Bacancy Technology’s Snowflake developers set up a separate virtual warehouse for each team, with compute separated from storage and sized to each workload, all drawing on the same shared copy of the data. This let analytics and quality reporting run at the same time without competing for resources, so queries stopped slowing one another and large jobs finished without having to wait in the queue.

3

Our data engineers rebuilt access on Snowflake with role-based policies that limit each user to the tables and columns their role requires, and applied dynamic masking to hide sensitive fields like patient identifiers from anyone not cleared to see them. Snowflake’s access history records every query, so the provider can show who viewed which data and demonstrate how PHI was accessed, as HIPAA requires.

4

Bacancy Technology’s ML engineers built and trained the provider’s machine learning models directly on the governed data in Snowflake, using Snowpark and Cortex, so the data never had to leave the platform. Snowflake’s elastic compute supplied the power to train and run those models, which enabled applications such as risk prediction and clinical decision support.

Core Features

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FHIR R4 API layer over Snowflake that serves the unified patient record back to EHRs and other clinical applications in real time

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Two-layer role-based access control with tag-based masking and row-access policies for column-level PHI protection and per-team isolation

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Continuous ingestion of EHR, claims, and lab data through Snowpipe, with Streams and Tasks capturing source changes and incremental application

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A dbt transformation layer that normalizes HL7 v2 and source records into a validated, governed clinical data model

No. of Resource

06

No. of Resource

Time Frame

March 2025 to January 2026

Time Frame

Project Snapshot

Eastmark Health Dashboard

Outcomes

All payers and partner hospitals given live, read-only access to the same data with no copies made

A single patient record built from EHR, claims, and lab data, replacing separate lookups across systems

Every query on patient data masked and logged by role, keeping PHI compliant with HIPAA across all facilities

Readmission and risk scores generated daily on live patient data, with the model reaching an AUROC of 0.82.

Reporting runs 45% faster, with overnight batch jobs brought into the working day for analytics teams

Infrastructure and maintenance costs cut by 78% after retiring the on-premises warehouse

Technical Stack

Data platform Snowflake
Ingestion and integration SnowpipeStreams and Tasks
Transformation and modeling dbt
Data sharing Secure Data SharingReader Accounts
Interoperability HL7 v2FHIR R4 API layer
Languages PythonSQL
Machine learning SnowparkSnowflake Cortex
Governance and security Snowflake HorizonSnowflake RBACDynamic Data MaskingRow Access PoliciesObject TaggingAccess History
Version control and deployment GitDocker

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