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FHIR IG analytics

Packagehl7.fhir.uv.aitransparency
Resource TypeProvenance
IdProvenance-AI-generated-patient-resource.json
FHIR VersionR4

Resources that use this resource

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Resources that this resource uses

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Narrative

Note: links and images are rebased to the (stated) source

Generated Narrative: Provenance AI-generated-patient-resource

Profile: AI Provenance

Provenance for Jane Doe (official) Female, DoB: 1950-11-15 ( Medical Record Number: MRN123456789 (use: usual, ))

Summary

Occurrence2025-06-18 00:00:00+0000
Recorded2025-06-18 00:00:00+0000
Policyhttp://example.org/policies/ai-authorized-patient-generation

Agents

Typewho
Verifierhttp://server.example.org/fhir/Practitioner/pract
AuthorDevice: extension = Large Language Models; identifier = http://example.org/ehr/client-ids#goodhealth; manufacturer = Acme Devices, Inc; type = All kinds of Artificial Intelligence; contact = http://example.org; note = --- language: - en license: - bsd-3-clause annotations_creators: - crowdsourced - expert-generated language_creators: - found multilinguality: - monolingual size_categories: - n<1K task_categories: - image-segmentation task_ids: - semantic-segmentation pretty_name: Sample Segmentation --- # Dataset Card for Sample Segmentation This is a sample dataset card for a semantic segmentation dataset.

Source1

{
  "resourceType": "Provenance",
  "id": "AI-generated-patient-resource",
  "meta": {
    "profile": [
      "http://hl7.org/fhir/uv/aitransparency/StructureDefinition/AI-Provenance"
    ]
  },
  "text": {
    "status": "generated",
    "div": "<!-- snip (see above) -->"
  },
  "contained": [
    {
      "resourceType": "DocumentReference",
      "id": "Input-Prompt-create-patient",
      "meta": {
        "profile": [
          "http://hl7.org/fhir/uv/aitransparency/StructureDefinition/AI-ModelCard"
        ]
      },
      "status": "current",
      "type": {
        "coding": [
          {
            "system": "http://hl7.org/fhir/uv/aitransparency/CodeSystem/AImodelCardCS",
            "code": "AIModelCard",
            "display": "AI Model-Card"
          }
        ]
      },
      "category": [
        {
          "coding": [
            {
              "system": "http://hl7.org/fhir/uv/aitransparency/CodeSystem/AImodelCardCS",
              "code": "AIInputPrompt",
              "display": "AI Input Prompt"
            }
          ]
        },
        {
          "coding": [
            {
              "system": "http://hl7.org/fhir/uv/aitransparency/CodeSystem/AImodelCardCS",
              "code": "AImodelCardMarkdownFormat",
              "display": "Markdown Format"
            }
          ]
        }
      ],
      "description": "System Prompt\n\nYou are a healthcare data specialist that converts natural language patient information into valid FHIR Patient resources. Your task is to extract relevant patient demographics and create a well-formed FHIR Patient resource that is fully conformant with FHIR US Core 6.1.0 specifications.\n\nRequirements:\n\n- Generate valid JSON that conforms to FHIR R4 Patient resource structure\n- Ensure compliance with US Core Patient Profile (US Core 6.1.0)\n- Include all required US Core elements when data is available\n- Use appropriate FHIR data types and value sets\n- Generate a unique resource ID using UUID format\n- Apply proper FHIR coding systems and terminologies\n- Handle missing data appropriately (omit optional fields when data unavailable)\n- Use standard US address formatting\n- Apply proper date formatting (YYYY-MM-DD)\n- Include appropriate extensions when necessary for US Core compliance\n\nUS Core 6.1.0 Patient Profile Requirements:\n\n- Must include: identifier, name, gender, birthDate\n- Should include: address, telecom, race, ethnicity when available\n- Use US Core extensions for race and ethnicity\n- Follow US postal address conventions\n- Use appropriate terminologies (e.g., HL7 AdministrativeGender, OMB race categories)\n\nData Mapping Guidelines:\n\n- Extract patient name and structure as HumanName with family/given components\n- Map gender terms to FHIR AdministrativeGender codes (male, female, other, unknown)\n- Convert birth dates to FHIR date format\n- Structure addresses using Address data type with appropriate use codes\n- Map race/ethnicity information using US Core extensions with appropriate OMB codes\n- Generate medical record number as primary identifier when not provided\n- Include meta.profile reference to US Core Patient profile\n\nOutput Format:\n\n- Provide only the valid FHIR JSON resource without additional commentary or explanation.\n\nUser Prompt\n\n- Convert the following patient information into a FHIR Patient resource conformant with US Core 6.1.0:\n\n`Jane Doe is a white female born on November 15, 1950. She lives at 123 Main Street, Anytown, Michigan, zipcode 12345.`",
      "content": [
        {
          "attachment": {
            "contentType": "text/markdown",
            "data": "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"
          }
        }
      ]
    }
  ],
  "target": [
    {
      "reference": "Patient/a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    }
  ],
  "occurredDateTime": "2025-06-18T00:00:00Z",
  "recorded": "2025-06-18T00:00:00Z",
  "policy": [
    "http://example.org/policies/ai-authorized-patient-generation"
  ],
  "reason": [
    {
      "coding": [
        {
          "system": "http://terminology.hl7.org/CodeSystem/v3-ActReason",
          "code": "HOPERAT"
        }
      ]
    },
    {
      "coding": [
        {
          "system": "http://terminology.hl7.org/CodeSystem/v3-ObservationValue",
          "version": "4.0.0",
          "code": "AIAST"
        }
      ]
    }
  ],
  "agent": [
    {
      "type": {
        "coding": [
          {
            "system": "http://terminology.hl7.org/CodeSystem/provenance-participant-type",
            "code": "verifier",
            "display": "Verifier"
          }
        ]
      },
      "who": {
        "reference": "http://server.example.org/fhir/Practitioner/pract"
      }
    },
    {
      "type": {
        "coding": [
          {
            "system": "http://terminology.hl7.org/CodeSystem/provenance-participant-type",
            "code": "author",
            "display": "Author"
          }
        ]
      },
      "who": {
        "reference": "Device/Note-ModelCard"
      }
    }
  ],
  "entity": [
    {
      "role": "quotation",
      "what": {
        "reference": "#Input-Prompt-create-patient"
      },
      "agent": [
        {
          "type": {
            "coding": [
              {
                "system": "http://terminology.hl7.org/CodeSystem/provenance-participant-type",
                "code": "author",
                "display": "Author"
              }
            ]
          },
          "who": {
            "reference": "http://server.example.org/fhir/Practitioner/pract"
          }
        }
      ]
    }
  ]
}