AI Clinical Support
HCE's AI clinical support is embedded directly into the clinical workflow — not a separate tool, not a chatbot, and not a replacement for clinical judgment. It surfaces context-aware suggestions at the points of care where decisions are made.
What the AI layer does
| Capability | Where it appears |
|---|---|
| Second opinion | Button inside a consultation — combines notes, labs, and imaging into an integrated analysis |
| Imaging impression | Automatic preliminary impression on uploaded radiological images |
| Lab interpretation | Plain-language summary of panels with abnormal-value context |
| Clinical notes summary | Condensed view of lengthy encounter histories |
| Emergency analysis | Triage decision support based on vitals and presenting symptoms |
| Clinical discovery | Semantic search for similar prior cases and outcomes |
| Protocol comparator | Compare a patient against successful similar cases |
| Case study generator | Auto-generate anonymized teaching case studies |
| Predictive scoring | Sepsis risk, readmission risk (optional, institution-licensed) |
Privacy-first design
HCE's AI is built around four non-negotiable privacy principles:
1. Local-first
Default AI analysis runs on on-premise models inside the hospital. Patient data does not leave the institution for routine analyses.
2. External AI is gated
Using external AI providers (for specialized synthesis that requires a larger model) is opt-in at the institutional level. External provider usage is licensed, auditable, and can be disabled per institution.
3. No raw user input is forwarded to models
AI prompts are constructed server-side from structured clinical data. The clinician's free-text input is never passed directly to a model — this eliminates prompt injection as an attack vector.
4. Every AI call is audited
The AI layer maintains its own audit log alongside the clinical audit trail. Every analysis records which models contributed, how long they took, and what they produced.
How the AI stays useful
The AI pipeline is designed to produce clinically actionable output, not generic summaries:
- Context-aware — the AI receives the patient's vital signs, current labs, active medications, and relevant history, not just the question
- Multi-model synthesis — complex analyses run multiple specialized models in parallel (e.g., one interprets labs, one analyzes images, one summarizes notes) and combine the outputs
- Confidence-scored — every analysis returns a confidence value so clinicians can weigh the suggestion appropriately
- Traceable — every analysis includes a trace of which models contributed so clinicians understand where a recommendation came from
What AI does not do in HCE
- It does not make diagnoses — it produces impressions and suggestions that clinicians evaluate
- It does not auto-prescribe — all prescriptions require physician action
- It does not override clinical workflow — it augments the existing flow with contextual suggestions
- It does not train on patient data — HCE does not send patient data to external providers for model training
Clinical discovery
A specific AI-powered feature that deserves its own note: Clinical Discovery lets physicians search across all the institution's own clinical data in the local language, with accent-insensitive and synonym-aware matching. When a physician is seeing a complex presentation, they can search for similar prior cases at the same hospital — a capability that simply wasn't possible before.
Feature availability
Not every AI capability is enabled by default. External-provider synthesis features (second opinion with cloud models) and predictive scoring are licensed separately. Your account manager can confirm which are active for your institution.