Submitted by gldickinson on
HL7 FHIR Record Lifecycle Event Implementation Guide
Please add/update reference to HL7 FHIR Record Lifecycle Event Implementation Guide, published December 2023: http://hl7.org/fhir/uv/ehrs-rle/Informative1/
Official Website of the Office of the National Coordinator for Health Information Technology
The metadata, or extra information about data, regarding who created the data and when it was created.
Data Element |
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Author Time Stamp
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Submitted by gldickinson on
Please add/update reference to HL7 FHIR Record Lifecycle Event Implementation Guide, published December 2023: http://hl7.org/fhir/uv/ehrs-rle/Informative1/
Submitted by gldickinson on
Needs to include description that Author Time Stamp must be associated with each USCDI dataset or data element that has a unique provenance set. Occurs when data is originated (captured, collected or sourced), updated, verified, attested, transformed (e.g., to/from exchange artifact such as HL7 v2 message, document or FHIR resource instance).
Provenance set includes who, what, when, where and why as metadata for USCDI data classes and data elements. Author Time Stamp is part of “when”. It offers essential assurance for transparency, accountability, trust, traceability and data integrity.
Note that Author Time Stamp is intrinsic to what the source EHR/HIT system already knows, thus it does not require extra data collection (burden) by the clinician or other end user.
Submitted by Brianmaugo on
A record-level data quality indicator as part of Provenance
Provenance tells a receiving system who authored data and when. I suggest ONC consider extending this concept, through Provenance or a USCDI+ domain, to a small set of computable, vocabulary-anchored completeness measures that certified systems could produce with each record, for example, the share of lab, medication, and problem entries carrying their standard codes, and whether the patient has had an encounter within the past year. This would let receiving systems, researchers, and AI governance committees assess fitness for use without rebuilding that assessment each time. Small, rural, and independent organizations, which have the least capacity to build their own data quality tooling, would benefit most.