Document Upload and Screening
Uploads accept PDF, image, and CSV EOBs, with validation, virus screening, and duplicate checks before secure storage. Background jobs separate extraction from the user-facing interface.
Intelligent Ingestion & Healthcare EDI Automation Platform
This Healthcare IT platform streamlines EOB Processing through AI extraction, template matching, human review, and standardized remittance exports. It helps billing teams reduce false failures, identify payers accurately, and move claims toward reliable payment reconciliation.
Project Overview
This Custom Web Application ingests EOB (Explanation of Benefits) files in PDF, image, and CSV formats for Healthcare Payments teams. AI and OCR extract claim details, while content-based template matching corrects payer identification and directs uncertain records to review. Validated data is mapped into ANSI X12 835 files for downstream processing. The platform combines Intelligent Document Processing (IDP), Claims Automation, and a controlled audit trail to support Revenue Cycle Management (RCM). Ditstek also strengthened the cloud environment and access controls while maintaining asynchronous processing for Healthcare operations.
Challenges
High Template-Matching Failures
A fragile payer-scoped template search produced an approximately 79% false failure rate. Wrong payer guesses also allowed duplicate templates to accumulate without being properly recognized.
Inconsistent Field Matching
Manually enriched templates could score worse after users added fields. Different AI-generated and printed label wording also prevented otherwise valid documents from matching accurately and reliably.
Unstructured Exception Handling
Unreadable documents bypassed automated matching and entered manual queues without a clear recovery path. Operators needed structured failure states and reliable retry options for exceptions.
Sensitive Data Exposure
Claims and payer data required restricted access, yet administrative services and sensitive infrastructure had exposed pathways. Long-lived credentials further complicated security governance and HIPAA Compliance efforts.
Heavy Processing and Review Demands
High-volume extraction and OCR workloads could block routine application activity. Billing teams needed visible processing states, prompt notifications, and controlled review before generating payment files.
Risk in Remittance Generation
Medical Billing staff needed dependable EDI output from validated claims. Incomplete or low-confidence extractions could create incorrect remittance files and potentially serious downstream reconciliation problems.
Solutions Provided by Ditstek
Ditstek rebuilt payer matching, extraction review, and EDI export around verifiable decisions. Cross-payer fallback, controlled AI scoring, secure infrastructure, and confidence-gated generation support safer, more reliable, auditable Healthcare EDI workflows.
Cross-Payer Content Matching
When payer-scoped matching fails, organization-wide content search shortlists five templates using fuzzy similarity. An LLM scores candidates, corrects the payer when appropriate, and logs the change.
Fairer Template Scoring
Manually added fields no longer lower match scores. The engine compares normalized business labels with literal printed labels, resolving vocabulary differences across diverse payer documents.
Structured Exceptions and Retries
Failed files enter defined OCR_FAILED, NEED_TEMPLATE, or FAILED states. Reviewers can inspect exceptions and retry processing without uploading original documents again, preserving the existing record.
Stronger Infrastructure Controls
Database access was moved behind private networking and authenticated VPN access. Storage delete restrictions and tighter permissions reduce exposure of sensitive documents and administrative systems.
Asynchronous Processing and Review
Celery workers process uploads asynchronously while the Angular interface shows status updates. Human review and a confidence threshold above 90% gate automatic EDI 835 generation.
Standardized Remittance Export
Canonical claim data flows through an EDI mapping engine and segment builder to produce downloadable ANSI X12 835 files with traceable user approvals and modifications.
Features
The platform connects document intake, AI extraction, template matching, review, and remittance generation. Its features help Payer Operations teams resolve exceptions, maintain auditability, control access, and monitor work in progress.
AI Data Extraction
Google Gemini and OpenAI support OCR and structured Data Extraction Platform workflows. Schema checks and canonical mapping prepare claim information for review and downstream processing.
Intelligent Template Management
Cross-payer template search corrects misidentified payers after a strong content match. Synonym handling and original printed labels help templates recognize varied incoming payer document terminology.
Exception and Retry Queue
An exception queue separates OCR failures, missing templates, and other processing issues. Reviewers can manually retry matching and resolve records without repeating the original upload.
Real-Time Claims Review
An Angular 20 interface shows eight sequential document states from upload through download. Server-Sent Events provide timely progress updates during extraction, review, validation, and export.
EDI 835 File Generation
Validated records flow through an EDI 835 segment builder for Claims Remittance. Automatic generation requires extraction confidence above 90%, with standardized outputs available for download.
Audit Logs and Role-Based Access
Detailed audit events capture uploads, claim edits, approvals, rejections, payer corrections, and state changes. Admin, Reviewer, and Viewer roles limit actions according to defined responsibility.
Connected Data and Background Jobs
PostgreSQL stores relational users, payers, templates, and exports; MongoDB stores raw documents and extraction results. Celery and Redis support asynchronous, reliable claims processing Workflow Automation.
Protected Cloud Environment
AWS-hosted services use private database access, encrypted storage, versioned objects, and VPN-based administration. These measures support HIPAA Compliance considerations for sensitive, document-heavy Health Tech workflows.
Impact Created
93%–100% Matching Accuracy on Valid Documents
On valid documents, automated matching accuracy reached a reported 93%–100%, improving the team’s ability to identify templates and route EOBs into the correct processing path.
Fewer Avoidable Matching Failures
The earlier approximately 79% false failure rate drove the matching redesign. Cross-payer fallback and better label handling now reduce avoidable exceptions and redundant payer templates.
Confidence-Gated Automation
An 80% matching-score threshold guides automatic document processing, while generation above 90% extraction confidence provides an additional safeguard before standardized remittance file export to payers.
More Recoverable Exceptions
Structured exception states and manual retries let reviewers recover difficult documents without repeated uploads, reducing friction and duplicate effort in the overall Claims Automation workflow.
Clearer Control Over Claims Data
Private database access, encrypted storage, role permissions, and detailed audit records improve control over sensitive claims information and the actions taken during document review activities.
More Consistent Healthcare Payments Data
Standardized ANSI X12 835 exports connect extracted EOB data with downstream Healthcare Payments and reconciliation, helping the organization move claims through its revenue cycle efficiently.
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