Syntegra: Syntegra Synthetic Medicare Claims – Analytics-ready format
1000 synthetic patient level claims records
1000 synthetic Medicare claims patient records in an analytics-ready (TUVA-core) format. This synthetic data is generated using deep generative models trained on real clinical data. Additional synthetic data in this format, trained from real patient data, can be provided by Syntegra including generation of custom populations.
Sample tables and fields:
* PATIENT: data about each unique patient; fields include patient_id, birth_date, gender, and death_date
* CONDITION: each row represents a diagnosis event for a patient; fields include patient_id, code (diagnosis code), encounter ID (link to the ENCOUNTERS table), description (of the condition) and condition_date (date diagnosed)
* ENCOUNTER: each row represents a unique encounter for some user; fields include encounter_id, patient_id, information about admission (admit_type_code, admit_type_description), encounter type, encounter date.
* COVERAGE: information about patient insurance coverage; fields include patient_id, coverage_start_date, coverage_end_date, and payer
And 4 other tables: MEDICAL_CLAIM, MEDICATION, PROCEDURE and MEMBER_MONTH
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.