Migrating a Clinical Abstraction Workflow Onto Claude
Move an existing clinical-abstraction workflow onto a Claude agent safely with shadow mode, phased autonomy, and fast rollback in 2026.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Move an existing clinical-abstraction workflow onto a Claude agent safely with shadow mode, phased autonomy, and fast rollback in 2026.
Build gold sets, field-level metrics, LLM judges, and a CI gate to measure and ship Claude clinical-abstraction agents safely in 2026.
Sandbox, least privilege, secrets handling, and prompt-injection defense for Claude agents that abstract PHI from clinical charts in 2026.
Use prompt caching, batching, and context pruning to keep Claude clinical-abstraction agents fast and cheap at registry scale in 2026.
Fix the loops, wrong tool calls, and hallucinated arguments that break Claude clinical-abstraction agents — with concrete 2026 debugging tactics.
Context engineering for Claude clinical abstraction: what to include, what to leave out, how to shape it for attribution, and why bigger windows don't change the rule.
Connect Claude's clinical abstraction agent to real systems via MCP: scoped auth, strict schemas, actionable errors, and idempotent writes that survive retries.
Reusable Claude patterns for clinical abstraction agents: role-rules prompts, evidence-first schemas, Skills as rulebooks, focused tools, and context budgeting.
An engineer's walkthrough to build a Claude agent that abstracts source-attributed clinical data: schema, parsing, tool calls, quote verification, and evals.