Prior Authorization
- Automation and integration into EMR
- NLP auto-populates 80% of fields
Risk Adjustment
- NLP-enabled insights improve coding & diagnosis specificity accuracy
- Reduce manual chart review volume & prioritize chart reviews
- Utilize NLP to uncover valuable clinical information hidden in unstructured data
- NLP-based insights & ML create better suspect & suggestion accuracy with confidence scoring
Population Payment Management
- Real-time analytics of financial, quality, and contract performance
- AI-enabled modeling and predictive insight
- Historical and ongoing VBC program analysis
- Automation of VBC contract management
The experience, infrastructure, and data needed to train AI for healthcare
- We have nearly a decade of healthcare-specific data model training and iteration
- Our AI is trained using a data warehouse of tens of millions (and growing) unstructured charts with labeled data derived from 50 big data sources
- Our scalable training infrastructure allows us to allocate resources to accurately train/retrain AI, while a separate SaaS production infrastructure enables trained AI models to be immediately mapped onto our solutions
Guided by practicality, not by hype
- We are deliberate in our approach to AI and incorporate it into solutions where it is warranted and proven to add value
- The enhanced breadth and depth of our Large Language Model training offers greater flexibility in the range of potential future uses, allowing us to focus on AI applications that offer maximum ROI for our customers
Holistic Approach
- Our AI is designed for utilization across multiple use cases including Risk Adjustment, Value-Based Payment, and Prior Authorization
- This cross-usage drives greater learning in our models, resulting in broader prediction sets and greater accuracy
Inside Edifecs AI
Natural Language Processing
- Our NLP converts unstructured and clinically meaningful data into structured insights at the individual document level; these insights can be utilized by our AI models for analysis.
- Understands the complexities of clinical language, including grammar, syntax, context, and intent.
Generative AI
- Where feasible, we incorporate existing large language models (LLMs) such as BERT and Roberta to augment and strengthen our model's performance.
- Newer LLMs will be incorporated as they are released.
Machine Learning
- The models consume the clinical language annotations made by our NLP. This data is labeled and used as training data in multiple different models to strengthen predictive insights.
- Our proprietary algorithms and logic are unique to each model's use case; models can be chained to simultaneously analyze multiple factors for different use cases.
Training Data Warehouse
- We have more than 10 years of experience training and retraining AI for healthcare-specific applications.
- Our robust training infrastructure includes a repository of claims, charts, and coding data from over 8 million patient records.