Proactive Claim Scrubbing for Revenue Cycle Management
Client Profile & Background
A healthcare organization faced high claim rejection rates due to errors in medical claims submitted to insurance companies. These errors included incorrect coding, missing information, and mismatched payer rules, which led to increased operational costs, delayed reimbursements, and reduced cash flow.
Objective
To reduce claim rejections and improve operational efficiency, the organization implemented Proactive Claim Scrubbing—a solution leveraging AI and Microsoft Azure. The goal was to identify and resolve errors in claims before submission, ensuring higher acceptance rates.
Solution
- Data Centralization and Preparation
- Azure Blob Storage and Azure Data Factory were used to centralize claim, payment, and denial data.
- Historical data was transformed using Azure Databricks for feature engineering, creating new fields like payer-specific rules and claim completeness flags.
- Model Development
- Azure Machine Learning (Azure ML) was utilized to build predictive models:
- Binary Classification Model: To predict claim acceptance vs. rejection.
- Multi-Class Classification Model: To identify specific denial reasons, such as coding errors or missing data.
- Models were trained and evaluated for precision, recall, and generalization across payers.
- Azure Machine Learning (Azure ML) was utilized to build predictive models:
- Real-Time Integration
- Models were deployed using Azure Kubernetes Service (AKS) as RESTful APIs.
- Azure Functions triggered these APIs during claim creation in the RCM system, providing real-time feedback on potential rejection risks and reasons.
- Insights and Actions
- Power BI dashboards displayed flagged claims, denial reasons, and trends for billing teams.
- Azure Logic Apps automated notifications for flagged claims, enabling corrective actions before submission.
- Continuous Improvement
- Azure Monitor and Application Insights tracked model performance.
- Periodic retraining using Azure ML ensured models stayed updated with new payer rules and patterns.
- <Governance and Compliance
- Data security and compliance with HIPAA regulations were enforced using Azure Key Vault for secure credential management and Azure Security Centre for monitoring.
Benefits
- Operational Improvements
- 50% reduction in claim rejections due to pre-submission error identification.
- Increased operational efficiency by minimizing manual corrections and re-submissions.
- Financial Impact
- Improved cash flow with faster claim approvals and reduced delays in reimbursement.
- Regulatory Compliance
- Enhanced adherence to payer guidelines and coding standards, ensuring compliance with healthcare regulations.
Conclusion
By implementing Proactive Claim Scrubbing using Microsoft Azure’s robust ecosystem, the healthcare organization significantly improved its Revenue Cycle Management process. The AI-driven approach empowered the billing teams with actionable insights, reduced claim denials, and streamlined operations, resulting in measurable financial and operational benefits.
Everest's Expertise
With over 25 years of experience and a global team of experts, Everest delivers innovative solutions for client success. Whether it’s custom application development, engineering services, or offshore outsourcing, Everest empowers businesses to optimize their operations and achieve their goals.