Standards and interoperability

Guides

1. SMART on FHIR and the data trust surface area for third-party app access

   August 29, 2026

   SMART on FHIR enables third-party apps to access patient data through standardized authorization. But the protocol says nothing about the quality, provenance, or consent integrity of the data it exposes. That gap is the data trust surface area most health systems never measure.

2. The CCD document quality problem: hidden trust failures in care summary exchange

   August 28, 2026

   Most Continuity of Care Documents exchanged between health systems contain structural defects that silently degrade clinical decision-making. Missing medication lists, outdated allergy records, and unmapped terminology codes pass through health information exchanges without any trust validation. The CCD document quality problem is not a format problem; it is a data trust failure hiding in plain sight.

3. Food insecurity screening data quality: FHIR resource requirements for SDOH data

   August 6, 2026

   Over 88% of food insecurity screenings captured in EHRs fail to use the FHIR resources required for interoperable SDOH data exchange. Without proper Observation, Condition, and ServiceRequest mappings aligned to the Gravity Project value sets, Z-code data for food insecurity becomes clinically useless and analytically toxic. This post maps the exact FHIR resource requirements, the data quality failures that degrade food insecurity screening data, and what trust scoring reveals about the gap between collection and usability.

4. Imaging metadata integrity: DICOM provenance and radiology AI data trust

   July 18, 2026

   DICOM metadata carries over 2,000 possible data elements per imaging study, yet most radiology AI pipelines never verify whether that metadata is accurate, complete, or internally consistent. When provenance breaks in medical imaging, every downstream model inherits the error. This post maps where DICOM data trust fails and what scoring it before training actually requires.

5. RxNorm drug data integrity: what medication reconciliation errors mean for AI

   July 17, 2026

   RxNorm is the backbone of standardized drug data in the U.S., but inconsistent concept mapping, deprecated codes, and source system fragmentation introduce errors that silently corrupt medication reconciliation. When AI models train on these records without measuring data integrity first, the result is not just bad predictions. It is patient harm at scale.

6. LOINC code standardization and the lab data trust problem

   July 16, 2026

   Between 6% and 19% of laboratory tests cannot be accurately mapped to LOINC codes, according to published research. That mapping failure rate means any AI model trained on lab data inherits systematic misclassification at the source. LOINC code trust is not a terminology problem; it is a data integrity problem that compounds across every downstream system that consumes lab results.

7. SNOMED CT mapping trust: when clinical terminology becomes a liability

   July 15, 2026

   SNOMED CT contains over 350,000 clinical concepts, but mapping errors between systems silently corrupt the data that health AI models train on. When a 'heart failure' concept maps to the wrong hierarchy or a retired code persists in a production system, clinical terminology stops being a foundation and starts being a liability.

8. CPT code integrity: what claims data quality means for AI model accuracy

   July 14, 2026

   CPT codes drive billions of dollars in claims decisions annually, but the procedural code data feeding AI models carries systemic quality problems that most teams never measure. Upcoding, unbundling, modifier misuse, and stale code mappings corrupt training data before a model ever sees it. CPT code integrity is not a billing problem; it is a model accuracy problem.

9. ICD-10 coding accuracy: how billing data becomes a health AI liability

   July 13, 2026

   ICD-10 codes were designed for billing, not clinical truth. When health AI systems train on claims data without verifying diagnostic coding accuracy, they inherit financial incentives as clinical signals, turning reimbursement artifacts into model predictions that affect real patients.

10. HL7 message quality scoring: what trust looks like for legacy EHR data

    July 12, 2026

    More than 90% of hospital interfaces still run on HL7 Version 2, a standard first published in 1987. Every one of those messages carries structural ambiguity that degrades downstream analytics, AI training, and clinical decision support. Scoring HL7 message quality is how trust gets attached to legacy EHR data before it causes harm.

11. FHIR R4 vs R5: what changes for data trust scoring

    July 11, 2026

    FHIR R4 contains 157 resources and remains the normative baseline for US healthcare interoperability. FHIR R5, published in 2023, introduces 31 new resources and structural changes to subscriptions, evidence handling, and requirements tracking that directly affect how data trust architectures validate provenance, consent, and recency. The version gap is not just a technical migration question; it is a trust architecture question.

12. The right to data portability: FHIR APIs and what patients can actually access

    May 8, 2026

    FHIR APIs promise patients the right to access and move their health data. The reality is that most patients can only retrieve a narrow slice of their record, and what they get lacks the provenance and trust scoring needed for meaningful use. The gap between legal right and practical access defines the next frontier of health data governance.

13. TEFCA and the interoperability imperative: what health systems need to prepare

    April 27, 2026

    TEFCA will connect every major health network in the US through a common trust framework, but connectivity without data integrity creates new risks. Health systems that prepare only for technical compliance will find that TEFCA interoperability exposes every upstream data quality problem they have been ignoring.

14. NCQA Credentialing Standards 2025-2026: How Data Trust Scoring Supports Compliance

    April 7, 2026

    NCQA's updated credentialing standards effective July 2025 tighten primary source verification timelines and add continuous monitoring requirements. For health plans, compliance is not just about having the right processes. It is about being able to demonstrate those processes are working — with an auditable, defensible record.