Business Challenge
Teams received large, mixed bundles of medical records that had to be split, identified, ordered, and summarized before any downstream work could begin. The manual process was slow, inconsistent between reviewers, and difficult to audit.
The Existing Workflow
Records arrived as large scanned files. Staff manually paged through documents to separate records, identify document types, locate key clinical and administrative data, and assemble chronologies by hand.
Engineering Approach
We designed an AI-assisted pipeline that automates the mechanical stages of record handling while keeping trained reviewers in control of judgment and quality.
Solution Architecture
- Secure record ingestion with queued processing
- OCR and ICR tuned for mixed print and handwriting quality
- Document classification and multi-record segregation
- Structured data extraction with validation rules
- Cross-document timeline and chronology generation
- Structured summaries with links back to source pages
- Reviewer workflow with exception routing and full audit history
- Reporting and API integration with existing applications
AI and Automation Components
- Document classification models
- OCR/ICR with confidence scoring
- LLM-based extraction and summarization with structured output
- Exception detection and routing rules
Integrations
- Existing client applications via API
- Reporting and operational dashboards
Security and Privacy Considerations
- Role-based access control
- Audit logging of automated and human actions
- Privacy-aware data handling
- Environment separation
Human Validation
Low-confidence classifications and extractions route automatically to trained reviewers, whose corrections are captured in the audit history and used to improve the pipeline.
Technologies
Python · FastAPI · PostgreSQL · LLM integrations · OCR/ICR engines · React · TypeScript
Outcome
- Simplified a multi-step operational workflow
- Supported more consistent document handling across reviewers
- Enabled structured review and exception management
- Created auditable processing history for every record
- [VERIFIED METRIC REQUIRED]
Ongoing Support
We continue to maintain and extend the platform, including model evaluation, pipeline tuning, and new workflow features.
Many of our engagements are delivered under confidentiality agreements. Case studies are anonymized to protect client intellectual property, business processes, data, and product strategy.