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, 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).

Setup

Install the package (this registers the p-tri and p-tri-ui commands on your PATH):

pip install patient-triage

Or, if you've cloned this project instead:

pip install .            # from inside this project folder
# or, for local development with live-reload on code changes:
pip install -e .

p-tri is exactly python main.py from earlier — same CLI, same flags — just installed as a proper command instead of a script you invoke by path.

requirements.txt is also kept up to date and lists every dependency this project uses — core, plus the scale (Postgres/Redis/RQ) and ocr (PyMuPDF/pytesseract/Pillow) extras, and pytest for development — as a single flat file, if you'd rather pip install -r requirements.txt than use pyproject.toml's extras syntax (.[scale,ocr]). Both are kept in sync; use whichever fits your workflow. Note it installs everything, including the optional pieces — trim the scale/ocr sections out of the file first if you only want the core setup.

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.

Web UI

For a visual alternative to the CLI, p-tri-ui runs a small local Flask server where you can upload reports, trigger processing, and view any PDF — input report or generated recommendation — inline in the browser (using the browser's native PDF viewer, no extra JS library required).

p-tri-ui                                    # http://127.0.0.1:5000
TRIAGE_LLM_BACKEND=anthropic PORT=8080 p-tri-ui   # override backend / port

What it does:

• Upload — choose one or more PDF files, or select an entire folder (via the "Or choose a whole folder" option), and upload them all in one go. Non-PDF files in a folder selection are silently skipped.
• Process — click "Process" next to any un-processed report to run it through the same graph the CLI uses (shared code path — see pipeline.py), or click Process All to run every un-processed report in one click.
• View — click any input report or generated recommendation to load it in the right-hand pane, titled with its actual name (not "(anonymous)").

Note: the "Input reports" and "Recommendations" lists are just a live directory listing of input_reports/ and output_recommendations/ relative to wherever you launch p-tri/p-tri-ui from (or TRIAGE_INPUT_DIR / TRIAGE_OUTPUT_DIR if set) — there's no database behind it. This repo ships with the 5 sample reports from generate_samples.py already sitting in input_reports/, so they'll show up on first launch until you delete them or run everything from a different working directory.

This is a local, single-machine service — the SQLite job queue assumes a single worker thread, and there's no auth yet (see "Running as a persistent service" below for what changes if you take this further).

Running as a persistent service

p-tri-ui isn't just a request/response script — it runs a persistent background worker (worker.run_worker_loop) for the lifetime of the process, consuming a durable, SQLite-backed job queue (db.py's jobs table). This is what makes it a service rather than a dev tool:

• Submitting work is decoupled from doing the work. Clicking "Process" or "Process All" hits POST /jobs/enqueue, which just inserts row(s) into the jobs table and returns immediately — the actual triage graph runs in the background worker thread, not in the HTTP request.
• Jobs survive a restart. Job state lives in SQLite, not in a Python dict. If you stop and restart p-tri-ui, anything still queued is still queued; anything caught mid-running from a crash gets requeued by db.recover_interrupted_jobs() at startup (verified: killing the process mid-job and restarting it against the same database picks the job back up and finishes it).
• Work continues without anyone watching. The worker loop polls the queue on its own timer — a submitted job gets processed whether or not a browser tab is open polling /jobs/status/<batch_id> for progress.

Deploying it as a long-running process (still single-machine): run it under a process supervisor so it survives reboots/crashes and restarts automatically, e.g. a systemd unit:

[Unit]
Description=Patient Triage UI
After=network.target

[Service]
ExecStart=/usr/local/bin/p-tri-ui
Environment=TRIAGE_LLM_BACKEND=anthropic
Restart=on-failure
WorkingDirectory=/path/to/your/data

[Install]
WantedBy=multi-user.target

Flask's built-in dev server (what p-tri-ui runs today) says as much in its own startup warning — for anything beyond local use, put it behind a production WSGI server instead, e.g. gunicorn --workers 1 'patient_triage.web.app:create_app()'. Keep --workers 1: the job queue is correct with more (job-claiming is a proper transaction, so two workers won't double-process the same job), but a single process keeps one clear background worker thread rather than one per process.

If you outgrow SQLite (multiple machines, high job volume, or you want concurrent writers without the current single-connection lock): see "Scaling to multiple machines" below — this is implemented, not just theoretical, as of this version.

CLI Usage

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

# Custom folders:
p-tri --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

pyproject.toml                    packaging metadata + the `p-tri`/`p-tri-ui` entry points
src/patient_triage/
    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                          job-store facade (picks sqlite or postgres backend)
    db_sqlite.py                   default job store: SQLite + in-process worker
    db_postgres.py                 multi-machine job store: Postgres, FOR UPDATE SKIP LOCKED
    queue_backend.py               Redis/RQ queue accessor (distributed mode only)
    tasks.py                       the RQ task function each worker process runs
    graph.py                       LangGraph wiring (the cyclic state machine)
    pipeline.py                    shared "process one report" logic (used by CLI + UI)
    worker.py                      persistent background worker loop (local mode only)
    main.py                        CLI batch entry point (this is what `p-tri` runs)
    agents/delegator.py           Agent 1: classify + reassess
    agents/specialist.py          Agent 2..N: per-specialty consultation
    web/app.py                    Flask web UI (this is what `p-tri-ui` runs)
    web/templates/index.html      upload form, file lists, PDF viewer pane
    web/static/style.css          UI styling
generate_samples.py                dev helper: regenerates the 5 sample reports

Scaling to multiple machines

By default this runs single-machine: SQLite for job/case state, and an in-process worker thread (see "Running as a persistent service" above). For multiple machines — more throughput, or workers physically separate from the machine serving the UI — swap in Postgres + Redis/RQ instead. Nothing about the triage graph or agents changes; only the job-store and job-dispatch layers do.

Install the extra dependencies (kept optional so the default setup doesn't need Postgres/Redis at all):

pip install patient-triage[scale]

What changes and why:

• Job/case state moves from SQLite to Postgres (db_postgres.py), so every machine — the one serving the UI and every worker — reads and writes the same durable state instead of a local file. Claiming a job uses SELECT ... FOR UPDATE SKIP LOCKED, the standard Postgres pattern for letting several readers pull distinct rows from the same queue table safely; this was stress-tested with 5 concurrent claimers racing over 10 jobs and confirmed no job was ever claimed twice.
• Job dispatch moves from an in-process thread to Redis + RQ (queue_backend.py, tasks.py). The web process no longer runs the triage graph itself in distributed mode — it only enqueues a job row in Postgres and pushes a matching task onto Redis. Any number of separate rq worker processes, on any number of machines, pull from that same Redis queue and actually execute the graph.
• Input/output PDFs need to be visible to every machine. This repo doesn't add object storage — the simplest correct setup is pointing TRIAGE_INPUT_DIR / TRIAGE_OUTPUT_DIR at the same shared network mount (NFS/EFS/etc.) path on every machine, web server and workers alike, so "the same file" really is the same file everywhere.

Configuration (environment variables):

TRIAGE_DB_BACKEND=postgres
DATABASE_URL=postgresql://user:pass@dbhost:5432/patient_triage
TRIAGE_QUEUE_BACKEND=distributed
REDIS_URL=redis://redishost:6379/0
TRIAGE_INPUT_DIR=/shared/input_reports    # same path, mounted on every machine
TRIAGE_OUTPUT_DIR=/shared/output_recommendations
TRIAGE_LLM_BACKEND=anthropic              # or lmstudio/mock

Running it — on the machine serving the UI:

p-tri-ui

It'll print Running in DISTRIBUTED mode on startup and won't spin up the local worker thread — it only enqueues jobs now.

On each worker machine (same env vars, same shared mount):

rq worker patient_triage --url $REDIS_URL

Run as many of these as you want, on as many machines as you want; RQ dispatches each queued job to exactly one of them. Verified directly: ran the web app and a separate rq worker process independently, confirmed the worker (which never talked to the web process) completed all jobs, and that a third, freshly-started web app process correctly showed everything as done — because the shared state lives in Postgres, not in any one process's memory.

Crash recovery differs from the single-machine mode here: rather than the app's own recover_interrupted_jobs() polling loop, prefer RQ's own Retry / job_timeout (already set to a 600s timeout in tasks.py) for handling a worker that dies mid-job — that's RQ's job, not ours, once it's in the picture. db_postgres.py still ships recover_interrupted_jobs for interface parity / anyone running Postgres without RQ, but the RQ path doesn't rely on it.

Scanned/photographed reports (OCR)

extract_text_from_pdf (pdf_utils.py) checks each page independently: if a page has a normal text layer, it's used as-is; if a page has little to no extractable text (under ~20 characters — i.e. it's a scanned or photographed page with no text layer), that specific page is rasterized and run through OCR instead. Native-text and scanned pages can be mixed in the same document — verified with a 2-page test PDF (one real text page, one image-only page) where OCR ran only on the scanned page.

Install:

pip install patient-triage[ocr]

Also needs the tesseract-ocr binary itself, which isn't pip-installable:

apt-get install tesseract-ocr     # Debian/Ubuntu
brew install tesseract            # macOS

On Windows, there's no official installer from the Tesseract project itself — two commonly used sources:

• UB-Mannheim Tesseract builds (.exe installer, most frequently recommended)
• Tesseract OCR on SourceForge (an official mirror of the tesseract-ocr GitHub project, also has Windows builds)

If you get pytesseract can't find/run the tesseract-ocr binary even though tesseract --version works in your terminal: this is almost always a stale PATH in whichever process is running the app — Windows in particular doesn't propagate PATH changes to processes that were already running when Tesseract was installed. Fully restart the terminal/IDE running p-tri/p-tri-ui first. If that doesn't fix it, set TESSERACT_CMD to the binary's full path and it'll be used regardless of PATH:

$env:TESSERACT_CMD = "C:\Program Files\Tesseract-OCR\tesseract.exe"   # Windows

export TESSERACT_CMD=/usr/local/bin/tesseract                          # macOS/Linux

How it works: pages needing OCR are rendered to images with PyMuPDF (no system Poppler dependency, unlike pdf2image) at 300 DPI, then read with pytesseract (default language eng — pass ocr_language= to extract_text_from_pdf for others, provided the matching Tesseract language pack is installed).

If the OCR extras aren't installed and a scanned page is encountered, you get a clear error telling you to pip install patient-triage[ocr], rather than a confusing import crash — verified directly by simulating the dependencies being absent.

Verified end-to-end: built a genuine image-only PDF (rasterized an existing sample report to an image, then rebuilt a PDF containing only that image — confirmed zero extractable text beforehand), ran it through OCR and got the original patient details back correctly, then ran that same file through the actual web app's upload → enqueue → process → recommendation flow and confirmed it completed successfully, indistinguishable from a native-text PDF to the rest of the pipeline.

Extending

• New specialties: add to SPECIALTIES in config.py — no other code changes needed, since the specialist agent is generic and parameterized by specialty name.
• Shared file storage beyond a network mount (e.g. S3-compatible object storage instead of NFS): would replace the raw os.path/open() calls in web/app.py and pipeline.py with a small storage abstraction — not implemented here since a shared mount already solves the multi-machine case correctly for a single deployment.