Problem

Medical coders faced challenges keeping up with ever-changing coding rules, especially for complex or specialized cases.
Maintaining high coding accuracy was time-consuming and could lead to billing errors and revenue losses.

Solution

Implementation of AI-powered Computer-Assisted Coding (CAC) software:

• Natural Language Processing (NLP): Analyzing unstructured text within medical records to identify key diagnoses, procedures, and relevant information
• Machine Learning: Algorithms trained on large datasets of coded medical records to accurately suggest codes and identify potential inconsistencies.
• Intuitive Interface: User-friendly platform allowing coders to review and interact with AI-generated suggestions.

Results

• Improved coding accuracy, reducing the risk of billing errors and maximizing revenue.
• Increased coder productivity, as the AI handles routine tasks and flags complex cases for in-depth review.
• Enhanced compliance with audit trails tracking changes and decisions assisted by AI, ensuring transparency.

Technology Stack

• NLP Libraries: NLTK, spaCy, or specialized medical NLP tools.
• Machine Learning: Libraries like scikit-learn, TensorFlow, or PyTorch.
• CAC Software: A robust system with the flexibility to integrate AI capabilities.

Software Development

• Model Training: Training machine learning models on a comprehensive dataset of accurately coded medical records.
• Interface Design: Prioritizing clear presentation of AI suggestions and ease of use for coders.
• Audit Trails: Transparent logging of AI-assisted coding decisions.

Before Metrics

Coding accuracy rate: 95%
Coder productivity: 4 charts per hour

After Metrics

Coding accuracy rate: 98%
Coder productivity: 5 charts per hour