Client Overview:
A leading healthcare software provider that specialized in automated medical coding faced major data challenges. The company worked with medical records from multiple providers and specialties, each using different formats.
Challenges:
- Medical records were non-standardized across providers and specialties.
- Traditional OCR methods lost critical data context and relationships.
- Limited training data made machine learning models ineffective.
BACS Approach:
- Built a Generative AI solution to create automated and standardized “Omegacharts” for patient encounter data.
- Used AI agents to parse complex medical records while preserving key relationships and hierarchies.
- Expanded the model to include predictive coding for higher accuracy and faster workflows.
Results:
- Standardized diverse medical records into a unified structure.
- Reduced manual effort and improved workflow efficiency.
- Achieved greater accuracy and reliability in medical coding.
Impact:
BACS transformed fragmented healthcare data into structured intelligence. The AI-driven process improved coding precision, reduced human errors, and delivered faster, more reliable outcomes for healthcare providers.
