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Dataset to De-Identification to external sharing package

This scenario demonstrates how to create a dataset for external review, run de-identification, review detected identifiers and de-identified outputs, and prepare the reviewed package for approved external sharing.

Best for: privacy review, demo data preparation, and external collaboration workflows.

Step 1: Create a dataset from a cohort or ingestion filters​

  1. In the left navigation, go to Data Curation Studio > Dataset Builder.
  2. Select the dataset mode based on the sharing scope, such as Cohort & Clinical Filter or an ingestion-filter-based dataset mode.
  3. Select the data type required for the external review package, such as clinical documents or images.
  4. Click Next to continue to the next dataset configuration step.
  5. Select the cohort, source, document filters, image filters, date range, or note types that should be included.
  6. Keep the dataset scope limited to the files approved for external review.
  7. Click Next to continue to Dataset Details.
  8. Enter the dataset name and description.
  9. Use a name that clearly identifies the dataset as an external sharing or de-identification package.
  10. Click Next to continue to Review Dataset.
  11. Review the selected patients, documents, images, filters, and expected dataset contents.
  12. Click Create Dataset to create the dataset.

Step 2: Include the documents or images intended for external review​

  1. Open the created dataset from Dataset Explorer.
  2. Review the included patients, documents, images, note types, dates, and source files.
  3. Confirm that the dataset includes only the documents or images intended for external review.
  4. Remove or exclude files that should not be part of the external sharing package where the workflow supports adjustment.
  5. Confirm that the final dataset scope matches the approved sharing request.

Step 3: Run De-Identification on the dataset​

  1. In the left navigation, go to Data Enrichment > De-Identification.
  2. Start a new de-identification job or session.
  3. Select the dataset created for external sharing.
  4. Select the appropriate de-identification profile and pipeline for the package.
  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 4: Review detected identifiers and de-identified outputs​

  1. Open the completed de-identification job details page.
  2. Review detected identifiers at the document or image level.
  3. Inspect the original and de-identified versions side by side 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 external sharing requirements.
  5. Approve, correct, mask, unmask, or flag detections according to the available review workflow.
  6. Pay special attention to scanned documents, embedded tables, headers, footers, and image overlays.
  7. Save review decisions where supported.

Step 5: Export or save the de-identified package​

  1. Confirm that the de-identification review is complete.
  2. From the de-identification details page, choose the export option that matches the delivery need: Original images only, Masked images only, or Both original and masked images.
  3. Export the reviewed de-identified outputs.
  4. If patient-level or file-level download is available, open the relevant item and download the reviewed de-identified version.
  5. Store the exported package in the approved delivery location.
  6. Track any files that were excluded, flagged, or left unresolved during review.

Step 6: Prepare the reviewed output package for approved sharing​

  1. Review the exported package before delivery.
  2. Confirm that the package contains the expected de-identified documents or images.
  3. Confirm that excluded or flagged files are not included unless explicitly approved.
  4. Add any required delivery notes, review notes, or package metadata.
  5. Share the reviewed package according to the approved external sharing workflow.
  6. Keep the dataset, de-identification job, and export details available for traceability.

Recipe reference​

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

Recipe: 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.

Recipe: 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.