Data foundations for healthcare AI
By ChatGPT Health. Educational synthesis prepared with AI assistance. Sources checked September 28, 2026. Clinically reviewed by Harvey Castro, MD, MBA, on September 28, 2026. Approved as educational content. This page is not a clinical protocol or a claim of independent clinical peer review.
A model can only work with the information and permissions its workflow provides. Map the data path before choosing the deployment architecture.
Make the data contract explicit
Describe each input, its source, units, timing and meaning. Identify missing or conflicting values and how the system handles them. A laboratory value without its unit or timestamp may be misleading. Record which information is copied, transformed, retrieved or generated so a reviewer can trace the output.
Interoperability is a starting point
HL7 FHIR defines a framework for exchanging health information through resources and related specifications. Using FHIR does not by itself establish data quality, clinical suitability or permission to use the data for AI. HL7 FHIR overview. Document the actual implementation, supported resource versions and access boundaries.
Questions for the technical and clinical team
Use this editorial checklist before a pilot:
- Identity: how are the correct patient and encounter established?
- Freshness: how does the system recognize stale or delayed information?
- Provenance: can users distinguish record facts, retrieved references and generated text?
- Permissions: which systems and people can read, write or export data?
- Failure: what happens when a dependency is unavailable or a format changes?
- Evaluation: which realistic test cases reveal missing context or integration errors?
Retrieval and local deployment have limits
Retrieval can bring source material into an answer, but the system can still select irrelevant material or misstate it. Keeping a model on local infrastructure changes the data path; it does not remove access control, maintenance or evaluation needs. Choose the architecture based on the intended use and constraints, not a promise that one hosting method eliminates risk.
Keep requirements specific
ONC HTI 1 includes transparency requirements related to predictive decision support within its certification framework. It is not a blanket rule for every AI application. Consult the actual criterion and your product role when determining obligations. ONC HTI 1 final rule.
Sources and further reading
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