Datasets & Document Sources

Example datasets used in this workshop

Dataset	What it is	Used for
Clinical + claims (Tuva model)	A synthetic, patient-level Medicare claims + clinical dataset (the open Tuva data model): patients, encounters, conditions, procedures, claims, eligibility, member-months.	Track A — rich entities for metrics like readmission rate and cost PMPM.
Medicare Part D + demographics	Public CMS Part D landscape data — plans, formulary, costs, prescribers — plus county-level socioeconomic reference data.	Cross-source analysis and the role-based-access examples.

Demo data — Numbers and dates are illustrative. For your own build, connect your data sources and run the same steps. Set connectors at the start of a session; credentials are managed in connector settings — never stored in the ontology repo.

Bringing your documents to Ana

Method	How it works	Best for
Upload to a session	Add files directly (PDF, DOCX, PPTX, XLSX, CSV, Markdown).	A handful of documents, or adding “one more”.
Cloud document store	Connect a shared drive and point Ana at a folder.	Teams whose documents live in a managed drive.
Enterprise document systems	SharePoint / OneDrive, Confluence, or internal object storage (S3 / Azure Blob / GCS).	Larger or regulated organizations.
Git repository	Keep the distilled ontology (Markdown + .tql) in your own git repo.	The governed ontology itself.

For regulated organizations — Keep documents and data inside your own boundary. With a single-tenant / in-VPC deployment, sensitive content never leaves your environment, and Ana ingests from your sanctioned stores rather than a consumer drive. A common pattern is a watched location — a designated folder or bucket Ana monitors, so a newly dropped document is distilled into the ontology as a reviewable change.

The flow: raw documents are inputs; the ontology is the governed output they become. You don’t upload raw binaries into the ontology repo — you feed them to Ana, and she distills them into the governed Markdown/.tql that lives there.