High-quality de-identified EHR data, showcasing comprehensive patient journeys and physician narratives in Dermatology. This data provides a comprehensive picture of the patient's journey and physician narrative throughout their hospital stay. With approximately 4 million+ unique patient records, the participating hospitals offer unique insights into variations in care across healthcare network hospitals and clinics. All sample data is de-identified and does not contain PHI.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
This listing uses a single pricing dimension: Product Access (Units), which grants subscribers access to the endocrinology electronic health record dataset at no cost. There are no tiers, instance sizes, or usage add-ons to compare. Access is uniform for all subscribers under this one option. The dataset contains de-identified records prepared for AI and machine learning work. To scope custom volumes, specialties, or delivery formats beyond this access grant, you contact the vendor directly for a tailored quote.
Top-of-mind questions for buyers
What does the Product Access (Units) dimension actually deliver to subscribers?
It grants access to a de-identified endocrinology electronic health record dataset. These are gold-standard medical records prepared to train clinical natural language processing and document AI models. Records are redacted under HIPAA Safe Harbor guidelines to remove personally identifiable information, so you can use them for AI training safely.
Since access is free, are there added charges if I need larger volumes or custom specialties?
The Marketplace access grant carries no cost. If you need custom volumes, specific demographics, geographies, formats, or specialties beyond this dataset, the vendor scopes that separately through a statement of work and provides a quote based on your requirements. That work sits outside this free access dimension.
In what file formats will I receive the data once I have access?
The datasets ship in formats data and machine learning teams already use, including JSON, CSV, and FHIR for structured records. This supports direct integration into AI, NLP, and healthcare model development workflows without added conversion steps.
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This file is a 6 month sample including the patients enrolled/active in the Diabetes Collaborative Registry in the 6 month time period of 1/1/2019 through 6/30/2019.