The Client Project
A Health Center partner needed to implement an AI solution powered by Natural Language Processing (NLP) to streamline the classification and reporting of scanned medical documents.
The goal was automatically extract quality-related data (e.g., immunizations, lab results, consult notes) and mapping it into reportable EMR fields to support better outcome tracking.
Requirements
High Accuracy Document Parsing
Develop an AI system capable of scanning and interpreting medical documents with over 90% accuracy in text recognition and data extraction.
Correct Field Mapping
Ensure the data extracted from documents is mapped to the correct patient record and field in the EMR’s reporting dashboard.
Reduced Human Workload & Error
By taking over repetitive tasks, the AI would free up staff from mundane data entry and eliminate the inconsistencies with tired or rushed staff performing manual labeling.
Transparency and Auditability
Provide clear documentation and logs for each processed document. It was important that every step the AI took could be traced and reviewed if needed.
Implemented Service
Intelligent OCR with AWS Textract
AWS Textract is used for the engine to extract text from scanned and faxed document images. Textract is a HIPAA-eligible machine learning service that can “analyze virtually any document” (forms, tables, etc.) and pull out printed text without predefined templates.
Custom NLP Classification
Developed bespoke NLP models to detect key health terms and classify document types (e.g. distinguishing an immunization record from a lab result or consult note). These models were trained on the clinic’s data to recognize context and terminology unique to healthcare documents.
Serverless Automation
Built AWS Lambda functions and API endpoints to orchestrate the workflow. As soon as a document is uploaded, the system triggers OC
Our Tech Stack
AWS Textract
AWS Lambda
PHP
Python
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The Results
Significant cost savings by automating what was previously a labor-intensive review, the FQHC and faster quality improvements improved data accuracy, the EMR’s reports now reflect up-to-date, accurate information for quality measures and audits.
Now clinical staff and administrative teams spend far less time on paperwork. The freed capacity allows them to support more providers and focus on patient care, rather than shuffling documents.
