Patient Report Triage — Multi-Agent System

A LangGraph-based multi-agent pipeline that ingests patient report PDFs, classifies ailments by specialty and severity, routes them to specialist agents in priority order, and loops unresolved cases back to intake for reassessment (with a safety cap that escalates to human review instead of looping forever). Outputs one recommendation PDF per input report.

This is a decision-support prototype, not a diagnostic device. Any real deployment would need clinical validation, human sign-off on every plan, and regulatory review before touching real patient care.

Architecture

                    ┌─────────────┐
                    │   intake    │  Agent 1 (Delegator)
                    │ classify +  │  - parses report text
                    │ build queue │  - extracts ailments, specialty, severity
                    └──────┬──────┘
                           │ (queue sorted severe → major → minor)
                           ▼
                    ┌─────────────┐
              ┌────▶│  pop_next   │
              │     └──────┬──────┘
              │            ▼
              │     ┌─────────────┐
              │     │ specialist  │  Agent 2..N (one per specialty)
              │     │  consult    │  - produces treatment plan, OR
              │     └──────┬──────┘  - flags "can't determine"
              │            │
              │   resolved/escalated   unresolved (retries left)
              │            │                  │
              │            ▼                  ▼
              │     queue empty?        ┌─────────────┐
              │      /        \         │  reassess   │  back to Agent 1
              │   yes          no       │ (re-classify│  with specialist's
              │    │            │       │ w/ feedback)│  feedback
              │    ▼            └───────┴──────┬──────┘
              │ ┌─────────┐                     │
              └─┤ compose │◀────────────────────┘ (pushed back into queue)
                └────┬────┘
                     ▼
                    END → PDF written

The reassessment loop is a genuine cycle in the graph (requirement 4), capped at MAX_REASSESSMENT_ATTEMPTS (default 3) per case — after that, the case is escalated to "requires human physician review" instead of looping forever.

Multiple ailments from one report are processed in severity-priority order (severe → major → minor), satisfying requirement (3).

Setup

pip install -r requirements.txt

LLM backend (swappable — pick one via --backend)

• lmstudio (default): point at a local model served by LM Studio's built-in OpenAI-compatible server (Settings → Developer → Start Server, default http://localhost:1234/v1). Free, runs entirely locally. Set LM_STUDIO_MODEL env var to match whatever model you've loaded in LM Studio.
• anthropic: uses the Claude API. Requires ANTHROPIC_API_KEY env var.
• mock: deterministic canned responses, no model required — useful for testing the graph wiring offline.

Usage

# Put patient report PDFs in input_reports/, then:
python main.py --backend lmstudio
python main.py --backend anthropic --model claude-sonnet-4-6
python main.py --backend mock              # offline test, no LLM needed

# Custom folders:
python main.py --input-dir my_reports --output-dir my_recommendations

Each <name>.pdf in the input folder produces <name>_recommendation.pdf in the output folder, containing:

• Resolved specialist treatment plans (with clinical reasoning)
• Any cases escalated to human physician review, and why
• A full audit trail of every classification / reassessment step, for a physician to sanity-check the AI's reasoning

A SQLite log (triage_cases.db) records a summary of every run for later auditing.

Project layout

config.py           specialties, severity levels, retry limits, backend config
schemas.py           Pydantic/TypedDict data contracts between agents
llm_backends.py       swappable LLM backend (anthropic / lmstudio / mock)
utils.py              JSON extraction helper for LLM outputs
pdf_utils.py          PDF text extraction + recommendation PDF generation
db.py                 SQLite audit logging
agents/delegator.py    Agent 1: classify + reassess
agents/specialist.py   Agent 2..N: per-specialty consultation
graph.py               LangGraph wiring (the cyclic state machine)
main.py                CLI batch entry point

Extending

• Scanned/image PDFs: extract_text_from_pdf raises if no text layer is found. Add OCR (pytesseract + pdf2image) as a fallback if your reports come from scanners.
• New specialties: add to SPECIALTIES in config.py — no other code changes needed, since the specialist agent is generic and parameterized by specialty name.
• Persistent service later: graph.py and agents/ are already decoupled from the CLI in main.py, so wrapping build_graph() in a FastAPI endpoint + queue (e.g. Celery/RQ backed by the existing SQLite — or Postgres at that point) is a relatively small step from here.