Healthtech innovator automates claims processing with AI

Healthcare providers face slow, error-prone claims processing due to diverse document formats and the need for manual data review, which delays reimbursements and pulls resources away from patient care. The client wanted to automate reconciliation end-to-end using NLP and OCR, transforming paper-based Explanations of Payments into structured digital data - even with real variability across insurance contract templates from providers like BCBS, Aetna, and Cigna.

We helped the healthtech company succeed by leveraging our tried-and-tested innovation and execution frameworks, proactively managing risks throughout the process. 

• Collaborative design: We conducted a GV-inspired design sprint to map the ideal user journey for streamlining EOP conversion and data extraction, and created a tangible prototype for the claims automation platform. 

• Architecture sprint: Designed the platform's underlying technical structure, leveraging Apache Spark and NLP libraries for scalable data processing, efficient text extraction, and robust machine learning, laying the foundation for a high-performing system.

• Text conversion using OCR: Used Apache Spark and Python API (PYSPARK) to handle large-scale data processing, perform text extraction from scanned PDFs, store the extracted text data in Apache Hadoop HDFS for efficient storage and accessibility.

• Text extraction using NLP: Leveraged NLP libraries like SpaCy to process and extract relevant information from the text data and transform extracted data into structured formats using Pandas, enabling easy comparison and analysis.

• ML model to automate verification: Developed machine learning models to automate verification procedures with predefined rules and logic to identify anomalies or deviations from expected patterns in EOPs.

• Accuracy assessment and improvement: Used Spark's machine learning capabilities to measure system accuracy and performance metrics while continuously updating and refining the model using PySpark based on analysis from discrepancies found.

• Feedback loop and maintenance: Established a continuous integration/continuous deployment (CI/CD) pipeline using Docker containers to track and improve performance. 

This partnership accelerated claims processing for healthcare providers, reducing costs, improving accuracy, and speeding up reimbursements. It showcases the power of automation in revolutionizing healthcare revenue management, and Zemoso's ability to help healthcare companies adopt complex technologies like AI and ML.