Syntegra: Patient-level Clinical Data (Inpatient and Outpatient Hospital)

Analytics ready, augmentable, expandable, high-fidelity synthetic data


Gain immediate access to patient-level data representative of patients from both large academic medical centers and small community hospitals across the U.S. This high-quality, longitudinal data is provided in a cleaned and normalized, analytics ready format, rather than raw healthcare data. Additionally, given data is provided as a synthetic generation, data can be provided immediately with no privacy risk, accelerating use for analysis and model development, testing and training.

With Syntegra’s unique, generative approach, you can also scale the data to fit your specific needs and populations of interest by generating a larger volume of synthetic patients and/or shifting the demographic makeup to create representative populations that match your exact focus.

Sample Tables:
– Demographic information about patients
– Insurance coverage information
– Info about the facility in which the patient was treated
– Info about procedures including cpt and icd-10-pcs
– Info about lab tests and results
– Info about medications ordered, taken and filled
– 7 additional tables

Sample Fields:
– Name
– Gender
– Race
– Ethnicity
– Date of Birth
– Zip Code
– Payer Name
– Payer Type
– Claims Cost
– Type of Facility
– Condition Name
– Condition Code
– Procedure Code
– Medication Code
– Lab Test Results
– 112 additional fields

Additional Information:
Synthetic data maintains all of the statistical accuracy of the real data it is trained from without containing any real patients. This complete privacy preservation means synthetic data can be used freely for a variety of purposes without privacy concerns and barriers, whether using standard, enterprise or business critical Snowflake.

About the Provider:

Syntegra makes healthcare data flexible and accessible with high-fidelity synthetic data generated using groundbreaking machine learning models. Syntegra synthetic data enables organizations to leverage, enhance, and extend new and existing data sources to drive innovation and advance research, data science and AI/ML efforts.

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