As drugs and medical procedures become more complex, payer teams need a foundation of aligned data and clinical content to improve member experiences and make informed business decisions.
Current payer challenges require clean, evidence-based data strategies
The payer industry is experiencing shifts across the organizational enterprise and ecosystem.
One of the biggest challenges is rising costs—a PwC report projects an 8% year-on-year medical cost trend for the group market and 7.5% for individuals in 2025, mostly driven by inflation, prescription drug spending, and behavioral health utilization. Additional industry challenges include:
- Meeting value-based care initiatives and unlocking insights for reporting
- Evolving health equity standards from the Centers for Medicare & Medicaid (CMS)
- Maintaining and improving Stars and HEDIS ratings
- Emerging drug therapies and specialty drugs impacting policy plans, especially GLP-1 drugs
- Constantly evolving medical evidence
- Advancements in artificial intelligence (AI) in areas like risk adjustment
What do these challenges have in common? They all highlight the need for a quality foundation in data infrastructure, analytics, and clinical evidence. Meeting these industry challenges and providing a quality, effective member experience relies on teams having centralized, evidence-based data to have clear visibility into business outcomes.
With insight into data and health information across the payer organization, teams are better aligned, cost savings can be identified, policy design and benefits can be easier to build, and members can more easily navigate the system. Clean, aligned data is also easier to report, analyze, and optimize to meet current and future challenges.
Data transformation: Aligning infrastructure across the healthcare ecosystem
The backbone of any payer organization is having the right enterprise-level data strategy to support business challenges and goals. However, many payer organizations have siloed data, impacting their ability to address challenges like population-level reporting, analytics, and interoperability. According to Gartner, poor data quality costs organizations $12.9 million annually, and data scientists spend between 50-80% of their time collecting, cleaning, and preparing data before it can be used.
Without clean, updated, and normalized data, payer organizations are unable to accurately analyze member health, report on quality measures, inform population health initiatives, or make care management decisions to improve member safety and outcomes.
