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Assistant cohort to dataset to De-Identification

This scenario demonstrates how to create a patient cohort with the Assistant, prepare a document-based dataset from that cohort, run de-identification on clinical documents, review detected sensitive information, and download reviewed text outputs where enabled.

Best for: privacy-first document preparation.

Step 1: Use the Assistant to create a cohort for sharing or demo preparation​

  1. In the left navigation, go to Agents and Tools > Assistant.
  2. Ask a cohort-building question based on the target sharing or demo population, for example: "Find patients with oncology notes from the last year."
  3. Review the Assistant response, including the interpreted criteria and returned patient population.
  4. If the patient population looks appropriate, click Create Cohort to begin cohort creation.
  5. In the Advanced Filtering panel on the right side of the screen, apply additional filters if needed, such as diagnosis, document type, date range, provider, visit type, or demographic criteria.
  6. Click Next to continue to Patient Management.
  7. Review the patient list and exclude any patients that should not be included in the sharing or demo cohort.
  8. Click Next to continue to Cohort Details.
  9. Enter the cohort name, description, category, and any linked cohort information.
  10. Use a clear name that identifies the cohort purpose, such as demo preparation, external review, or privacy review.
  11. Click Next to continue to Review Cohort.
  12. Review the cohort configuration and optionally define a sample size.
  13. Click Create Cohort to save the cohort.

Step 2: Build a document dataset from the cohort​

  1. In the left navigation, go to Data Curation Studio > Dataset Builder.
  2. Select Cohort & Clinical Filter as the dataset mode.
  3. Select Unstructured as the data type.
  4. Click Next to continue to Cohort Selection.
  5. Select the cohort created in the previous step.
  6. Click Next to continue to Unstructured Criteria.
  7. Apply document filters to limit the dataset to only the clinical documents that require privacy review.
  8. Use filters such as note type, document date, source, provider, or visit context where needed.
  9. Keep the dataset scope narrow enough for review, but broad enough to include all documents intended for de-identification.
  10. Click Next to continue to Dataset Details.
  11. Enter the dataset name and description.
  12. Use a name that clearly indicates the dataset is intended for document de-identification or privacy review.
  13. Click Next to continue to Review Dataset.
  14. Review the selected cohort, document filters, and expected dataset contents.
  15. Click Create Dataset to create the dataset.

Step 3: Confirm the documents that need privacy review​

  1. Open the created dataset from Dataset Explorer if a final check is needed before de-identification.
  2. Review the included patients, documents, note types, dates, and source files.
  3. Confirm that the dataset includes the clinical documents intended for privacy review.
  4. Remove or exclude any documents that should not be part of the de-identification workflow, where the deployment supports dataset adjustment.
  5. Confirm that the remaining dataset content matches the approved sharing or demo scope.

Step 4: Run De-Identification on the document dataset​

  1. In the left navigation, go to Data Enrichment > De-Identification.
  2. Start a new de-identification job or session.
  3. Select the document dataset created for privacy review.
  4. Select the appropriate de-identification profile and pipeline for text documents.
  5. Review the job configuration before starting.
  6. Click the start action to begin de-identification.
  7. Monitor the job until processing is complete.

Step 5: Review detected sensitive information​

  1. Open the completed de-identification job details page.
  2. Review detected sensitive information at the document level.
  3. Inspect the original and de-identified text where comparison is available.
  4. Confirm that patient names, identifiers, dates, contact details, locations, provider details, and other protected or sensitive elements are handled according to the review policy.
  5. Approve, correct, mask, unmask, or flag detections according to the available review workflow.
  6. Pay special attention to headers, footers, embedded tables, copied report sections, and repeated identifiers because sensitive information may appear outside the main clinical narrative.
  7. Save review decisions where supported.

Step 6: Download reviewed de-identified text outputs​

  1. Confirm that the document de-identification review is complete.
  2. Open the detailed de-identification result for the reviewed text document.
  3. Download the reviewed output directly in JSON format.
  4. Repeat the download process for additional reviewed documents as needed.
  5. Store or share the downloaded outputs according to the approved governance, privacy, and customer-delivery process.
  6. Document any known limitations, unresolved detections, or documents excluded from the review.

Recipe reference​

Each stage of this scenario is also a standalone recipe you can reuse in other workflows.

Create a patient cohort from the Assistant​

When to use on its own: When you know the clinical population but don't want to manually build detailed filters.
Features involved: Assistant, Cohort Builder, patient search and filtering, saved cohorts.
Edge cases / limitations: A zero-patient result may mean the data is missing, not that the population doesn't exist. Cohorts should be reviewed before regulatory, trial, or operational use.
Value: Moves you from question to reusable patient population quickly.

Build a dataset from a cohort or ingestion results​

When to use on its own: After ingestion and before Data Curation, Information Extraction, or De-Identification.
Features involved: Dataset Builder, Dataset Explorer, cohort filters, ingestion-result filters, dataset preview.
Edge cases / limitations: Data Curation and De-Identification require a dataset; a cohort alone is not enough. Too broad a dataset makes downstream jobs slow or noisy; too narrow risks missing evidence.
Value: Gives you a controlled, reusable work package instead of repeatedly selecting raw records.

De-identify a dataset before sharing or review​

When to use on its own: Before sharing data externally, preparing demo data, or reviewing images/documents that may contain patient identifiers.
Features involved: Dataset Builder, De-Identification, patient/document/image review, side-by-side original and de-identified review where available.
Edge cases / limitations: De-Identification requires a dataset; it isn't cohort-only. Export/save behavior should be confirmed before promising a complete handoff. Validate outputs before external sharing.
Value: Supports privacy-first data preparation for review, demos, and collaboration.