How iMDP Cut Attachment-Processing Time from 18 to 3 Minutes for a National Payor

iMDP (intelligent Medical Document Processing) | Payor Claims Attachments | Adjudication Readiness
Learn how a national payor reduced claims attachment review time from 18 minutes to 3 minutes through automation.
Solutions Used
Industry
Healthcare
4.8M attachments/year

Claim documentation volume processed

$23M+ avoidable cost addressed

Manual processing gap reduced

30% clean claims pending

Attachment review bottleneck targeted

Business Challenge

A national payor was under pressure to improve claims throughput across a growing attachment environment.

The organization processed 4.8M attachments per year, including X12 275 files, scanned clinical documentation, prior authorization support, operative reports, lab results, and other provider-submitted records.

The bottleneck was manual abstraction.

Documentation arrived in multiple formats and channels, but claims teams still had to interpret each file, identify the document type, extract clinical and billing entities, and align the evidence to claim adjudication requirements.

The financial impact was measurable.

Manual attachment handling cost about $5.97 per attachment compared with an automated target of $1.04, creating a $23M+ annual avoidable processing-cost gap before factoring in adjudication delay, SLA exposure, and provider abrasion.

The executive trigger was the need for adjudication-ready attachment data.

Leadership needed iMDP to automate classification, extraction, X12 275 mapping, and structured output delivery while preserving auditability and operational control.

Solution Offered

Novacis Digital deployed iMDP (Intelligent Medical Document Processing), an AI-powered claims attachment solution built to convert unstructured provider documentation into adjudication-ready data.

The solution ingested X12 275 electronic attachments, scanned medical records, operative reports, lab results, prior authorization support documents, provider fax streams, portal uploads, and scanned mail.

It classified document types, extracted clinical and billing entities, mapped attachment data to claim elements, and delivered structured outputs directly into adjudication workflows.

The technical differentiation was claims-specific medical document processing at national scale: iMDP combined document classification, entity extraction, X12 275 mapping, claim-level matching, validation rules, and audit-ready output in one governed workflow.

Claims teams retained control over exception handling, adjudication review, and final claim decisions.

This shifted the operating model from manual attachment abstraction to AI-prepared adjudication support with human-controlled claim resolution.

iMDP Claims Attachment Capabilities

  • Ingest attachment channels
  • Classify medical documents
  • Extract claim evidence
  • Match data to claims
  • Preserve audit traceability

Adjudication Readiness Features

  • EDI 275, portals, fax streams, and scanned mail
  • Operative reports, labs, notes, and support records
  • Clinical, billing, and authorization data
  • Attachment-to-claim element alignment
  • Source records, validation flags, and review history

Results Delivered

Novacis Digital implemented iMDP in two stages: a 6-week claims attachment pilot [illustrative] for high-volume provider documentation channels, followed by a 10–12 week enterprise expansion [illustrative] across X12 275, portal, fax, and scanned-mail workflows.

The initial rollout focused on cases where document classification, clinical abstraction, and claim matching consumed the most operations time.

Early wins included medical document classification, structured clinical and billing extraction, X12 275 alignment, attachment-to-claim matching, and cleaner adjudication-engine handoff.

This gave claims and operations leadership proof that attachment-processing speed could improve while preserving exception review, audit traceability, and human-controlled claim resolution.

Business Outcomes:

Addressed $23M+ in annual avoidable processing cost by shifting attachment handling from $5.97 manual cost to $1.04 automated cost per file.

Reduced processing time from 18 minutes to 3 minutes per attachment, giving adjudication workflows faster access to structured evidence.

Absorbed 12% annual attachment volume growth with a scalable processing model instead of linear abstraction-team expansion.

Structured 4.8M annual claim attachments for adjudication use, proving execution across X12 275, portal, fax, and scanned-mail workflows.

Reduced manual abstraction dependency for attachment-driven claims, helping teams focus on exceptions, adjudication support, and provider response quality.

Additional Value:

  • Established a reusable iMDP layer for claims attachments, authorization support, appeals intake, and provider-submitted clinical documentation.
  • Preserved audit traceability from source attachment to extracted data, claim match, validation status, and reviewer action.
  • Added exception queues for incomplete, mismatched, or low-confidence attachment data.
  • Supported cleaner claims-engine handoff through standardized clinical and billing data outputs.
  • Kept claim disposition, payment review, and exception handling under claims-team control.

See how Intelligent Automation
Applies to your Operations.

If you’re evaluating automation, modernizing operations, or exploring
next steps, book a demo with us.
Book a Demo
Book a Demo

Related Case Studies

See how organizations use Novacis Digital iMDP to improve accuracy, speed, and compliance across medical workflows.

How AI-Accelerated Data Migration compressed an 18-month Snowflake & Databricks roadmap into 6 months for a Fortune 500 Enterprise

Learn how a Fortune 500 enterprise reduced cloud migration timelines from 18 months to 6 months using AI-powered migration acceleration.

How Novacis Digital’s AI Data Migrator Recovered ~$210K in Annual IT Capacity for a 12-Location Healthcare Pharmacy Network

Learn how a pharmacy network recovered IT capacity and reduced reporting cycles from weeks to hours through AI-driven data migration.