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Boston Medical Data

DocRouter for anesthesia RCM packet processing

This is a live DocRouter deployment (on-prem).

Overview

Boston Medical Data processes high-volume peri-operative document packets for anesthesia revenue cycle management (RCM). Packets arrive as mixed fax/PDF bundles—often covering many patients and facilities in a single file—and must become validated, patient-level structured data for billing workflows.

DocRouter runs on-premises in their environment. The live path is a native DocRouter workflow: upload → page split → OCR → page classification → patient grouping → per-patient PDF merge and downstream extraction, with human review when pages cannot be assigned confidently.

The Challenge

Anesthesia RCM depends on complete, accurate encounter and coverage data pulled from messy clinical packets:

  • Multi-document bundles mixing surgery schedules, cover sheets, demographics, insurance cards, pre-op forms, and anesthesia records
  • Wide page range — packets from a single page up to hundreds of pages (up to ~500)
  • Many facilities and schedule formats in the same operational pipeline
  • Billing-critical fields — patient identity, MRN, insurance, procedure timing, providers, ASA status, and related clinical details
  • Scale that made manual split-and-key entry slow and error-prone

Staff previously had to visually sort packets by patient, then re-key structured fields into RCM systems—work that did not scale with packet volume or page count.

The Solution

We deployed DocRouter for end-to-end packet processing aligned to anesthesia RCM:

  1. Ingest tagged multi-page PDFs (fax and scan packets)
  2. Split and OCR each page (Textract in the live flow)
  3. Classify page type and extract identity anchors (name, DOB, MRN) in one pass
  4. Group pages into patient sets; separate schedule and cover pages
  5. Merge per-patient PDFs for targeted extraction (demographics, insurance card, anesthesia record, and related schemas)
  6. Flag unassigned pages for human review before downstream RCM action
  7. Emit structured JSON via API for billing and ops systems

Common pattern

Messy document packets → validated structured data → workflow action

What Runs in Production

Native DocRouter workflow (live path)

On document.uploaded for the anesthesia packet tag, the flow:

  • Splits the PDF into pages
  • Runs OCR and the page-classifier LLM prompt in parallel per page
  • Executes a code step that groups pages by MRN or name+DOB (with adjacency heuristics for weakly identified pages), builds one merged PDF per patient, and sets human_review when unknown pages remain

That native flow is what runs in production today.

Temporal during development

Before DocRouter had built-in workflows, we prototyped the same pattern with Temporal in doc-router-temporal: chunk pages, classify, group patients, create patient PDFs, extract insurance cards, with durable retries and polling.

Temporal was the development and early orchestration path. Once native DocRouter workflows were ready, production moved to the built-in flow. The technical write-up of that Temporal design is in How To Create Document Workflows With Temporal And DocRouter.AI.

Extraction Library

Schema-driven prompts cover the RCM packet surface:

Capability Role in anesthesia RCM
Packet page classifier Labels cover, surgery schedule, pre-op, anesthesia record, demographics, insurance card, ID card, or unknown—and pulls patient identity fields for grouping
Surgery schedule batch Extracts multi-patient encounters (times, surgeon, MRN, room, procedure) from OR schedules across facilities
Patient demographics Face-sheet / registration demographics plus primary, secondary, and tertiary insurance
Insurance card Member ID, plan, group, Rx BIN/PCN, payer ID
Anesthesia record Diagnoses, procedures, surgeons, anesthesia providers/CRNAs, start/stop times, ASA, block type
Epic anesthesia record Combined demographics/insurance with Epic Procedure Summary care-team rules
Presurgery features Deeper clinical extraction (ASA, PONV risk, airway, meds, labs, history) when the packet includes preprocedure documentation

Downstream RCM systems consume the structured output; reviewers intervene only on flagged pages and low-confidence edge cases.

Outcomes

Results below are from Boston Medical Data’s live on-prem DocRouter deployment:

  • 175,000+ documents processed
  • 1–500 pages per document
  • 99.9% precision on extractions in production
  • Human-in-the-loop retained for pages the classifier cannot assign to a patient group
  • API-ready structured data feeding anesthesia RCM workflows

Relevance to DocRouter

This deployment is DocRouter’s primary live healthcare customer proof for multi-document packet processing: on-prem operation, native workflows at volume, and schema libraries tuned for anesthesia RCM—not a one-off consulting build.

It also shaped the product path from external Temporal orchestration to first-class DocRouter workflows for the same classify → group → extract pattern.

For more details, contact Andrei Radulescu-Banu at andrei@docrouter.ai. Platform demo available at app.docrouter.ai.