Automating Authentication Classification and CPT Code Mapping Using Machine Learning
In the healthcare industry, efficient management of patient appointments, insurance verifications, and accurate billing are critical for both compliance and financial stability. A leading North American healthcare provider faced significant challenges in manually processing appointment classifications and CPT (Current Procedural Terminology) code mapping, leading to operational bottlenecks and costly errors.
To address this, we developed a comprehensive machine learning (ML) solution that automated two critical workflows:
- Authentication Classification: Automatically determining whether an appointment requires insurance pre-authorization.
- CPT Code Mapping: Accurately matching unstructured appointment descriptions to CPT codes for billing.
Our solution leveraged Random Forest for decision trees and BERT (Bidirectional Encoder Representations from Transformers) for deep learning, seamlessly integrated with the client’s .NET-based backend systems.
The Challenge:
The client’s existing manual processes presented several key challenges
Manual Authentication Classification
- Time-Consuming Operations: Each appointment required manual review for insurance pre-authorization, delaying scheduling and patient care.
- Inconsistent Decision-Making: Staff varied in experience, leading to inconsistent interpretations and classification errors.
- Non-Scalable Workflows: As patient volumes grew, the manual system could not keep pace, causing workflow backlogs.
- High Risk of Human Error: Manual processes introduced the risk of misclassifications, resulting in non-compliance and potential claim denials.
Manual CPT Code Mapping
- Labor-Intensive Process: Staff manually reviewed free-text appointment descriptions to assign appropriate CPT codes, leading to delays and inconsistencies.
- Noisy and Unstructured Data: Appointment notes often contained inconsistencies, misspellings, and varied phrasing, complicating the mapping process.
- Scalability Issues: The growing volume of appointments made the manual process unsustainable without significant staffing increases.
Our Solution:
We developed a modular ML system to automate these critical workflows, improving speed, accuracy, and scalability. The solution was built in Python with .NET integration for seamless deployment into the client’s existing infrastructure.
Component 1: Automated Authentication Classification (Random Forest)
Implementation Workflow
- Data Collection and Feature Engineering: Historical appointment data was collected, including patient demographics, diagnosis codes, service categories, and insurance providers. This data was preprocessed using Pandas and NumPy for model training.
- Model Training: A Random Forest model was trained using Scikit-learn, optimized with hyperparameter tuning and cross-validation for high accuracy.
- Real-Time API Integration: The trained model was deployed via a RESTful API, allowing real-time classification of appointment requests during scheduling.
- Continuous Learning: Feedback loops were integrated for ongoing model improvement, ensuring the system adapts as data volumes grow.
Component 2: Automated CPT Code Mapping (BERT)
Implementation Workflow
- Data Preparation: Historical CPT mappings and appointment descriptions were cleaned and standardized using Pandas. Medical ontologies were used to ensure semantic accuracy.
- Model Training: A BERT-based deep learning model was fine-tuned on this domain-specific dataset using PyTorch, capturing semantic relationships for accurate code mapping.
- API Deployment: The model was deployed via APIs, providing real-time CPT code predictions integrated into the client’s .NET billing system.
- Confidence Scoring: The system included a confidence scoring mechanism, routing uncertain predictions to human reviewers for verification, reducing false positives.
Impact & Results:
Key Benefits
- Improved Operational Efficiency: Automated classification reduced manual workloads, freeing up staff for patient care.
- Higher Accuracy: Reduced errors and improved billing compliance through AI-driven decision-making.
- Scalability: Easily scaled with growing patient volumes without additional staffing.
- Real-Time Decision-Making: Enabled instant classification and billing decisions, reducing wait times.
- Seamless .NET Integration: Smooth integration with existing .NET-based infrastructure minimized disruption.
- Reduced Claim Rejections: Improved code mapping accuracy reduced billing disputes.
Conclusion:
This project demonstrated the powerful impact of machine learning and automation in transforming healthcare operations. By integrating Random Forest and BERT models, we delivered a scalable, high-accuracy solution that significantly reduced manual effort, improved decision-making, and optimized financial outcomes.