Healthcare AI Provenance | AI Output Tracking
- The HL7 Workgroup plus meeting this week highlights the growing importance of artificial intelligence in healthcare.Recognizing the need for clarity, the Security Workgroup is emphasizing existing measures designed...
- A key concern is the ability to identify content created or assisted by AI, including diagnoses, notes, and observations.Tagging this data ensures that downstream users are aware of...
- To address this,the HL7 Security Workgroup is promoting the use of the AIAST tag and the Provenance resource to ensure proper AI data management.
Address the critical need for transparency in healthcare with the HL7 Security Workgroup’s innovative approach to AI provenance.Discover how the AIAST tag and Provenance resource are revolutionizing how we identify AI-generated data, ensuring the integrity of patient information and AI-assisted healthcare. News Directory 3 highlights the importance of these advancements in managing and understanding primary_keyword like AI-generated diagnoses and secondary_keyword clinical notes. learn how these tools promote accountability and trust in AI, giving healthcare professionals the clarity they need. Discover what’s next for AI provenance in medicine.
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HL7 Security workgroup Addresses AI Provenance with AIAST Tag
Updated May 31, 2025
The HL7 Workgroup plus meeting this week highlights the growing importance of artificial intelligence in healthcare.Recognizing the need for clarity, the Security Workgroup is emphasizing existing measures designed to support AI data handling, especially concerning AI provenance.
A key concern is the ability to identify content created or assisted by AI, including diagnoses, notes, and observations.Tagging this data ensures that downstream users are aware of its origin, informing their decisions. The Security Workgroup aims to provide a mechanism for appropriate data tagging, neither promoting nor discrediting AI, but ensuring openness in AI-assisted healthcare.
To address this,the HL7 Security Workgroup is promoting the use of the AIAST tag and the Provenance resource to ensure proper AI data management.
Provenance Tag: Identifying AI-Generated Data
The AIAST (Artificial Intelligence Asserted) tag is a data tag applicable to any data, indicating its AI origin. This tag, defined within the HL7 terminology, serves as security provenance metadata. It identifies data or information objects asserted by AI, such as clinical decision support systems or machine learning algorithms.
The AIAST code is available for use in various standards, including HL7 v2, CDA, DICOM, and IHE-XDS. Its portability makes it a versatile tool for carrying security tags across different platforms.
This tag can appear at the top of a FHIR Resource in the .meta.security section, like this:
"resourceType" : "Condition",
"id" : "1",
"meta" : {
"security" : [{
"system" : "http://terminology.hl7.org/CodeSystem/v3-ObservationValue",
"code" : "AIAST" }
]
},
... other content etc.....}
It can also be used with element-level tagging defined in DS4P inline security labels. For example,a DiagnosticReport might use an inline label to tag a specific .note element resulting from AI analysis.
Provenance Resource: providing Context
The Provenance resource offers a more detailed approach when a simple tag is insufficient. It leverages the AIAST tag to provide complete information about the AI assertion, detailing the background and context of the AI’s involvement.
this resource can use the target element extension or target path extension to pinpoint specific elements within the target resource that originated from AI assertions. It can also identify the specific AI algorithm used via a device resource, enabling traceability and accountability.
The Provenance resource can also indicate the data from the patient chart that were considered by the AI algorithm.
Furthermore, the Provenance resource can indicate the specific AI algorithm using a Device resource. this allows users to understand the revision of the AI that was used. If a problem (bias) is later discovered with that version of the AI model, all decisions recorded from it can be identified.
As with any Provenance, the other elements can be filled out to provide details on when, why, where.
AI’s Role in Using Provenance
AI algorithms often analyze patient records to generate new diagnoses or notes. These algorithms should recognize the AIAST tag to differentiate between newly entered data and data derived from previous AI use. This awareness helps prevent “model collapse” or “feedback loops,” where AI inadvertently reinforces its own outputs. One approach is for AI to disregard data or elements previously authored by AI.
What’s next
As AI continues to evolve in healthcare, the HL7 security Workgroup’s efforts to establish clear provenance standards will become increasingly vital. The use of the AIAST tag and Provenance resource will promote transparency, accountability, and trust in AI-driven healthcare decisions.