License patient-level, synthetic EHR data that is built from the statistical distribution of data from U.S.-based hospital EHR systems and is readily accessible and easily scalable. Augment the original data by expanding cohorts, balancing biased populations, and imputing missingness. Organizations can license synthetic, structured data generated by Syntegra from electronic health record systems of community hospitals across the United States, reaching beyond just claims and Rx data. The synthetic data provides a detailed picture of the patient's journey throughout their hospital stay, including patient demographic information and payer type, as well as rich data not found in any other sources. Examples of this data include: drugs given (timing and dosing), patient location (e.g., ICU, floor, ER), lab results (timing by day and hour), physician roles (e.g., surgeon, attending), medications given, and vital signs. The participating community hospitals with bed sizes ranging from 25 to 532 provide unique visibility and assessment of variation in care outside of large academic medical centers and healthcare networks. Our synthetic data engine is trained on a broadly representative dataset made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history. Notably, synthetic data generation allows for the creation of any number of records needed to power your project. EHR data is available in the following formats: — Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format — FHIR USCDI (labs, medications, vitals, encounters, patients, etc.) The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained. — Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning — Impute missing value to minimize gaps in the data