eHealth Beat

Introduction

The stop-loss and reinsurance industry is undergoing a significant transformation, driven by the increasing availability and sophistication of health IT. In a recent interview with the CTO of a leading stop-loss carrier, we delved into the critical role of health IT, particularly the ability to obtain frequent, usable, and clean data, in driving predictability and enabling earlier intervention.  

Importance of Data in Stop-Loss and Reinsurance

In stop-loss and reinsurance, predicting future healthcare costs is paramount. Traditionally, this relied heavily on historical data and actuarial models. However, health IT is changing the game by enabling us to incorporate real-time data and a broader range of variables into our predictive models.  

• Real-Time Insights: Access to timely claims data and insights from sources like SDOH provides a more comprehensive understanding of the factors influencing healthcare costs. This granularity allows us to move beyond population-level averages and identify specific individuals or groups at higher risk.  
• Data Standardization is Key: The ability to obtain frequent, usable, and clean data is not just important—it’s essential. As the EHR Association task force emphasizes, the lack of uniformity in prioritizing and defining social risk domains by different stakeholders results in the absence of a consistent, universally agreed-upon, and prioritized list of domains appropriate for assessment by providers, which complicates the exchange and interpretation of the data.  

Health IT: Enabling Proactive Intervention

Improved predictability, fueled by health IT, unlocks the potential for earlier intervention. Instead of reacting to high-cost claims after they occur, we can leverage data to proactively identify potential issues and collaborate with clients to implement cost-effective solutions.  

• For instance, if data analytics pinpoint a member at high risk of developing a costly chronic condition, we can facilitate access to preventive care programs or disease management resources.  
• This proactive stance helps control costs and leads to improved health outcomes for members.  

Technology as the Catalyst

To realize this vision, strategic investments in technology are essential:

• Robust Data Infrastructure: We need systems capable of collecting, storing, and managing vast amounts of data from diverse sources.  
• Advanced Analytics: Tools and techniques for data analysis, pattern identification, and actionable insight generation are critical. AI and machine learning are key enablers here.  
• Interoperability: Seamless data exchange with other healthcare stakeholders, including providers, payers, and Health Information Exchanges (HIEs), is vital.  
• Cybersecurity: Protecting sensitive data is paramount. We must prioritize robust cybersecurity measures to safeguard patient information.  

Conclusion

Health IT, focusing on obtaining frequent, usable, and clean data, is no longer just a supporting function in stop-loss and reinsurance. It’s a core driver of our ability to deliver client value, manage risk effectively, and foster a more sustainable and equitable healthcare ecosystem.