What ChironAI CDS surfaces to the clinician, by workflow.

AI-led structured intake with the patient: history, ROS, social and environmental factors, red-flag triggers. Output structured for the consultation context.

High-priority flags surfaced from the patient interview before the clinician opens the chart. Red-flag patterns trigger AB 489-compliant clinical attention.

Explicit consent capture for AI-assisted workflows, per AB 3030 disclosure. Persisted with the visit record.

Decision-support reasoning over presenting features with Bayesian confidence and qualitative tiers. Discriminating features called out per differential. The clinician evaluates and selects.

Real-time synthesis of canonical guidelines and peer-reviewed literature. Source-grounded with explicit guideline anchors.

Wells (DVT/PE), GRACE (ACS), MELD, CHA₂DS₂-VASc, TIMI, HEART, others. The reasoning that justifies each score is shown.

Six-tier qualitative scale plus quantitative Bayesian percentages. “Cannot exclude” as a first-class state when data is insufficient.

Architectural AB 489 gate. Every AI artifact carries the non-dismissible review banner. Every PDF export carries the disclosure in the footer.

Five-pass structured second-look review for radiology — structure, pathology, artifacts, missed zones, cross-window correlation — as a named ordered process. The radiologist drafts and signs the impression of record.

BI-RADS, LI-RADS, PI-RADS, Lung-RADS, TI-RADS, ACR-TI-RADS, RECIST, PERCIST, ASPECTS, AO/OTA, others. Modality-appropriate framework selection.

Time-critical findings are architecturally separated from routine outputs. Notification path is distinct from the read.

Llama 4 Vision routes imaging through structured reasoning. Candidate findings cite the imaging series and slice they were observed on, surfaced for radiologist review.

Sepsis screen, AKI, thyroid trajectory, lipid panel, diabetic series, hepatic panel, renal panel, infectious panel, oncology panel, cardiovascular panel.

Demographic adjustment for age, sex, pregnancy status, and applicable specialty context. The system does not flag normal pediatric values as adult abnormal.

Longitudinal lab trajectory rendered inline with the current value. Patterns recognized across the longitudinal context.

Where a pattern warrants additional testing, the recommendation surfaces with the guideline anchor that warrants it.

Every statement in the generated SOAP note traces to its underlying source field, source value, and source date. Visible to the clinician at review time.

English, Spanish, French, German, Hindi, Mandarin, Arabic, Tagalog, Vietnamese, Korean, Portuguese, Russian. RTL support for Arabic.

Every signed document gets an immutable SHA-256 hash at signature time. Amendments are recorded as new versions; the original signed version stays verifiable.

Document hash + signing clinician identity + timestamp + audit-chain entry. Any subsequent modification fails hash verification.

Multiple documents reviewed and signed in one workflow. Each document still requires individual physician attestation; bulk-sign is a UI optimization, not a compliance shortcut.

Indication, mechanism, contraindication, drug-drug and drug-disease interaction, dose-adjustment guidance. RxNorm-anchored.

Critical (contraindicated), Major (monitor closely), Moderate (caution), Minor (informational). With mechanism disclosure and recommendation per interaction.

When a prescription would trigger a step-therapy requirement or prior auth under common payer formularies, the system surfaces the flag before the prescription is signed.

Each interaction cites the canonical pharmacology source that warrants the severity tier and recommendation.

Patient-facing portal for pre-visit interview, post-visit follow-up, patient education, and consent capture. Mobile-first.

Patient education output composed at kindergarten, elementary, middle-school, high-school, college, and professional reading levels. The system meets the patient where they read.

Patient-facing content rendered in any of the twelve supported locales. RTL support preserved end to end.

HMAC + previous-hash audit log on every clinical action. Immutability enforced at the database layer. Verifiable end to end.

AB 375 / CCPA / CPRA Automated Decision-Making Technology notice surfaces on first AI feature use. Opt-out controls in the user-preferences surface.

Generative-AI authorship disclosed on every AI artifact. Non-dismissible by design.

Every database row carries a tenant ID. PostgreSQL Row-Level Security policies enforce isolation at the database layer, independently of the application layer.