At Caalm-ai, we tackled the challenge of streamlining the data review process healthcare organizations. This involved handling a variety of Documentation Data Extraction—ranging from medical records to agreements—before they could be used in proprietary systems. By leveraging advanced technology, including AI, automation tools, and custom software, we’ve significantly boosted both efficiency and accuracy.
Documentation Data Extraction
Approach
- Developed a dynamic tool for accurate data verification and introduced a centralized intake for document automation. This streamlined process significantly enhanced accuracy and reduced manual work, boosting operational efficiency.
Delivered Solution
- Our solution leverages advanced AI to classify complex Documentation Data Extraction with a 98% success rate, enabling smarter decisions. This precise data categorization deepens analysis and reveals insights, significantly impacting strategy.
Value Generated
- The automation and AI enhancements have collectively saved approximately 30,000 operational hours annually, translating to about $1.5 million in cost savings.
Our latest work
More Case Studies
State License Verification Automation
A state license verification automation solution streamlined provider data searches, handling CAPTCHA challenges and complex licensing websites. By automating document capture, reporting, and compliance...
Separation of UB from Medical Records
An optimized solution combined a CNN model with UiPath workflows to efficiently extract UB claim pages from large medical records. By reducing AI resource...
Expansion of Claims Automation for Health Payer via Snowflake Integration
A health payer client expanded claims automation by integrating Snowflake with existing systems, enabling seamless ZAP file generation. The solution scaled processing, reduced errors,...
