{
  "status": "Illustrative acceptance plan, not an executable test suite or observed production result",
  "topic": "Health data provenance architecture",
  "date": "2026-11-04",
  "sourceArticle": "https://aptixlabs.com/insights/health-data-provenance-app",
  "setup": [
    "Use synthetic accounts and records",
    "Record the environment and implementation version",
    "Agree the expected product contract before testing"
  ],
  "reviewSequence": [
    "Define the minimal lineage fields for each metric",
    "Track manual changes and transformations explicitly",
    "Restrict provenance access with the underlying record"
  ],
  "cases": [
    {
      "id": "1",
      "condition": "Source data is corrected",
      "expectedBehavior": "Derived lineage identifies the changed input",
      "observedBehavior": null,
      "status": "not_run"
    },
    {
      "id": "2",
      "condition": "User edits a value",
      "expectedBehavior": "The manual origin remains distinguishable",
      "observedBehavior": null,
      "status": "not_run"
    },
    {
      "id": "3",
      "condition": "Unauthorized coach requests metadata",
      "expectedBehavior": "Provenance is protected with the metric",
      "observedBehavior": null,
      "status": "not_run"
    }
  ],
  "tradeoff": "Complete raw-data retention is not a requirement for useful lineage. A compact record reference and transformation history may provide enough traceability with a smaller retention footprint."
}