Trust begins
with clarity.

Explaining a record, suggesting a health plan and supporting a clinical decision do not carry the same responsibilities. Each intended use needs its own evidence, testing and boundaries.

Relevant expertise, validation and authorization need to match the care a future service would provide.

It should be clear who made a clinical decision, what information they had and when it happened. Confirming what you reported is different from a clinician determining what it means.

We’re exploring clinical services and provider workflows, with responsibilities defined before care is offered.

Evaluation needs to cover record retrieval, relevance, source preservation, explanations and user understanding, as well as the care workflows that follow. Timeouts, errors and cases the system cannot handle count, not just successful answers.

We want to measure whether relevant history makes the experience more useful and whether important information reaches the person making the next decision. Serious failures should not disappear into an average.

Permission to retrieve a record or support care should not be treated as blanket permission to record a visit, train a model or share information elsewhere. Those purposes need to be explained and handled separately.

You should be able to understand what each permission covers and make a separate choice for each purpose.

A correction should record what changed, where, why and by whom. An override is not automatically proof the AI was wrong.

Any future training use would require appropriate permissions, quality review, dataset preparation, evaluation and an approved release. Editing a record would not retrain a model in real time.