Transforming care models with an AI-powered Healthcare strategy

The GenAI Smart Search solution offers hassle-free access to health information from websites through GenAI-driven search engines by collating information from multiple sources. The intelligent search & document summarization feature delivers accurate, concise, and relevant information using advanced semantic search algorithms. Patients get personalized responses, accurate health information from trusted sources, and round-the-clock query resolution. Patients understand their health conditions better, have reduced doctor dependency, and make informed decisions, driving better health outcomes.

The Risk Stratification solution segments patients by complexity and care needs, enabling healthcare organizations to provide tailored care. Using machine learning, it predicts risk scores and categorizes patients into Low, Medium, or High risk. Explainable AI identifies risk factors, while customized care models map patients to high-value care pathways. This approach improves health outcomes, facilitates proactive care management and ensures targeted interventions, reducing the need for costly treatments, and allowing Health Plans to optimize resource allocation.

The Data De-Identification solution ensures fast and effective PHI anonymization using multiple methodologies and user-specific configurations like suppression rate, K-anonymity, etc. It supports all data formats and types, making it versatile for various environments. The solution compares Safe Harbor and Expert Determination methods, providing advanced insights and automated PHI demarcation. By anonymizing data, it enhances relevance, accuracy, privacy, and compliance with regulations like HIPAA and GDPR, and allows user-specific adjustments for multiple use cases like research and analytics.

The Smart MLOps solution offers a customized model deployment and management framework to deploy, maintain, monitor, and govern clinical machine learning models across any cloud, on-premise, or hybrid environment. It transitions model monitoring and management activities from the DS team, streamlines processes, and defines an automation strategy leveraging intelligent features. It includes automated processes to develop, operationalize, and optimize regulatory compliance needs for building SaMD pipelines, ensuring a repeatable framework with key design controls, documentation, traceability, and tagging.

The Denial Management solution automatically filters claims for reasons like 'Medical Necessity' and predicts claims denials using ML. The solution employs classification and regression algorithms, propensity features related to ICD, CPT, revenue codes, and payer plan codes, and uses Word2Vec and semi-local embeddings for feature creation. It captures intra-claim interactions and addresses imbalanced datasets with techniques like SMOTE, reduces denial rates, speeds up reimbursement, and enhances operational efficiency and patient experience by minimizing billing issues and streamlining the claims process.

The Payment Integrity solution uses advanced models to predict and prevent fraudulent healthcare claims. It helps predict negotiation success rates and savings, estimate final provider offers, and aid strategy with arbitrator fair line offers. This approach improves negotiation success, increases negotiator productivity, boosts savings, and eliminates surprise billing, providing a more efficient and reliable claims process.