Ambient scribe
Nabla
Nabla gives clinicians and healthcare organizations control over documentation, coding, and retention settings. Patient access, refusal, and correction depend on the deployment. Earlier consent and training assurances were not fully reverified in this pass.
Published September 8, 2026 as an AI-assisted draft. The public report separates documented facts, agency judgments and unresolved questions.
Summary judgment · 40 out of 100 toward patient-directed
Nabla's clinician-controlled ambient documentation, dictation, and coding workflows within provider organizations. Patient-impacting institutional AI.
Mixed
Nabla gives clinicians and healthcare organizations control over documentation, coding, and retention settings. Patient access, refusal, and correction depend on the deployment. Earlier consent and training assurances were not fully reverified in this pass.
Patient agency
How this tool changes agency
Reviewed documentation may improve later understanding, but no direct patient mechanism to inspect or contest generated notes was established. Coding traceability for clinicians is not automatically patient-accessible.
Current consent guidance, no-training promise scope, local refusal alternatives, transcript access, and patient correction mechanisms.
Patient agency assessment
Who sets and changes the goal?
Clinicians and organizations choose the workflow and retention settings. A patient may benefit without controlling the note's purpose, final content, or coding. The evidence supports this allocation, not a judgment from vendor ownership.
What can the patient understand, question, or do?
Reviewed documentation may improve later understanding, but no direct patient mechanism to inspect or contest generated notes was established. Coding traceability for clinicians is not automatically patient-accessible.
Can the patient evaluate the conditions of use?
Concrete retention and hosting disclosures are useful. The baseline's no-training-on-notes and consent claims were not fully reverified and should remain dated claims rather than universal guarantees.
Text findings
Conditions of use
Published controls and their limits
Concrete retention and hosting disclosures are useful. The baseline's no-training-on-notes and consent claims were not fully reverified and should remain dated claims rather than universal guarantees.
What remains unknown?
Not tested or not established
Current consent guidance, no-training promise scope, local refusal alternatives, transcript access, and patient correction mechanisms.
Who evaluated this?
AI-assisted public-source draft
Vendor statements describe published conditions, not independently verified behavior. No clinical, security, accessibility, or legal validation is claimed. Earlier evidence remains dated in the report and history.
Sources checked
Source-specific findings and retrieval limitations are recorded in the full report.
Review provenance
Criteria
CAIHL-derived HugoScore framework and September 7 qualitative review priorities. Draft v1.2 numerical anchors remain unadopted.
Reviewer
AI-assisted public-source reassessment prepared in OpenAI Codex.
AI / model
OpenAI Codex / GPT-6
Human review
Hugo Campos authorized publication of these AI-assisted draft reassessments on September 8, 2026. This does not claim comprehensive human verification of every finding.
Review date
2026-09-08
Limitations
Current consent guidance, no-training promise scope, local refusal alternatives, transcript access, and patient correction mechanisms. No live product use, patient-data upload, account creation, code audit, clinical evaluation, or independent implementation validation.
Review method
Focused public-source reassessment using CAIHL: patient authority, critical capacity, and informed control. Existing evidence plus one focused primary-source pass and at most one targeted follow-up. No live product testing. Numerical scores remain provisional editorial placements, not a new calculation.
AI-assisted draft · Medium for institutional deployment and retention statements, low for patient-facing rights (AI-assisted draft)