Key Challenges
- Client software faced data interoperability challenges when integrating data from sources like EHRs and claims due to non-standardized formats and coding systems.
- Data security and regulatory compliance, such as HIPAA, while enabling data access and sharing, particularly for Protected Health Information (PHI).
- Complex adjudication processes and a high claim volume can lead to workflow delays.
- Payer faced revenue loss from fraud detection challenges.
Technologies
Solution
- Data Integration Platforms with ETL capabilities ensure data consistency and accessibility by harmonizing diverse data sources.
- Encryption and strict access controls safeguard PHI, enabling secure data access and sharing for authorized users.
- Claims Automation, powered by RPA and intelligent algorithms, expedites claims processing and reduces errors.
- AI-Driven Fraud Detection continuously learns from historical data to enhance the detection and prevention of fraudulent claims.
- Azure Machine Learning enhanced decision-making with advanced analytics and maintained HIPAA compliance with robust data security measures.
Benefits
- Improved data interoperability in healthcare promotes collaboration, reducing redundant efforts.
- Efficiency is boosted with AI and claims automation, cutting costs.
- Advanced analytics prevent fraud, leading to substantial savings.
- Power BI aids in data visualization, aiding decision-making and transparency.
- Blockchain enhances data security and patient trust and regulatory requirements like HIPAA compliance .
