Applied AI · Healthcare · 2025 to 2026
Clinical knowledge platform with a grounded AI assistant
An assistant clinical staff can trust, because it answers only from approved manual text and a second model checks it.
They complete identity setup, then role orientation and system provisioning before their first day [§4]. Badge access follows on day one [§9].
Representative UI, abstract, with no client data.
How it works
full document cached as context; bounded corpus, no RAG miss
Sonnet drafts an answer with inline section citations
Haiku independently checks every claim is supported verbatim
unsupported claims flagged; refusal detected when the manual does not cover it
wrong-answer reports go to tech review before clinical staff
Architecture
By the numbers
Prompt caching keeps a full-manual assistant affordable
Context
A healthcare team's operations manual is consulted daily and must stay accurate. In a clinical setting a confidently-wrong answer is a safety problem rather than a usability one, so a naive 'chatbot over our docs' was unacceptable.
What I did
- Built grounded Q&A that loads the full manual as cached context and answers with section citations, removing retrieval-miss risk for a bounded corpus.
- Added an independent second model that verifies every answer is supported verbatim, returning a boolean gate before anything ships.
- Designed document import so the LLM emits only structure and anchors; section bodies are spliced from the source, removing rewrite/hallucination risk.
- Wrapped high-stakes generated artifacts in a seven-step clinical release audit (banned-term + special-character scanners, mandatory fact-verification, hard-stop rule).
Outcome
- Staff get answers they can rely on, with wrong-answer reports triaged technically before ever reaching clinical decision-makers.
- Clinical words in the manual are always the human-authored source words; the model never authors clinical text.
- Prompt caching keeps the assistant cheap enough to leave running all day.
Under the hood
Grounding by a second model, not by vibes
- The answer model responds only from the approved manual with citations; a separate, cheaper model then verifies each claim is supported verbatim and returns a boolean.
- Any answer that comes back unsupported is flagged before it is shown, turning 'a chatbot over our docs' into an assistant clinical staff can rely on.
Verbatim-splice import
- To structure an existing Word/PDF manual, the LLM emits only section titles and exact start-anchor quotes, and the bodies are spliced from the source between anchors.
- The model never authors clinical text, and unmatched sections surface as explicit gaps for a human to resolve.
Clinical release audit
- High-stakes generated artifacts pass a seven-step audit: banned-term and special-character scanners, cross-document consistency, and mandatory primary-literature fact checks.
- The governing rule is simple: a single unresolved clinical-fact issue is a hard stop.