05 Nov Why Payer-Provider Data Sharing Remains a Barrier to Better Healthcare

Top Takeaways

• Aetna’s latest survey highlights persistent data access challenges, with nearly one-third of providers prioritizing real-time patient information.
• Updating patient medical records remains a major administrative burden, cited by 58% of providers who identified administrative burden as a top challenge.
• Reliable, standardized healthcare data is essential for improving payer-provider collaboration, care coordination and AI readiness.
• IMAT Intelligence helps payers and providers aggregate, normalize and validate healthcare data to improve operational efficiency, support analytics and prepare for AI.

Despite improvements in payer-provider relationships, healthcare organizations continue to struggle with accessing and managing patient information.

According to Aetna’s latest Provider Survey, 58% of respondents who identified administrative burden as a top challenge cited updating patient medical records as the biggest contributor. Nearly one-third of providers also identified real-time access to patient information as the most important action payers could take to address their challenges.

The findings highlight a persistent problem in healthcare. While interoperability has improved, fragmented and inconsistent data continues to create administrative inefficiencies and complicate care coordination. As organizations expand their use of AI and advanced analytics, addressing these challenges is becoming increasingly important.

The Growing Demand for Better Data Access

The Aetna survey, conducted by Morning Consult among 727 U.S. healthcare providers, found that payer-provider trust remained relatively stable, with an average score of 6.0 out of 10. Providers also reported improvements in prior authorization processes, suggesting progress in reducing certain administrative burdens.

However, access to patient information remains a significant concern. Healthcare providers frequently rely on information from multiple sources, including electronic health records (EHRs), claims systems, laboratories and other care settings. When that information is incomplete, outdated or difficult to access, clinicians and administrative teams must spend additional time gathering and reconciling records.

These challenges can delay care coordination, complicate quality reporting and limit visibility into patients’ healthcare needs.

For payers, similar data gaps can affect care management, population health initiatives and efforts to identify opportunities for improving outcomes. The survey underscores the need to move beyond simply connecting systems toward ensuring that information is readily available and usable across the healthcare ecosystem.

Why Data Exchange Alone Is Not Enough

Improving interoperability is an important step toward addressing these challenges, but exchanging information does not automatically make it useful.

Healthcare data often arrives in different formats, with inconsistent terminology, coding structures and patient identifiers. Records may contain missing information, duplicate entries or conflicting details that require additional processing before they can support clinical and operational decisions.

Without effective data management processes, organizations can find themselves repeatedly cleaning and reconciling information for individual applications.

A stronger approach combines data aggregation, standardization and validation to create a more consistent foundation for healthcare operations. By bringing together clinical and claims information, normalizing data across sources and identifying quality issues earlier, organizations can reduce manual processing and improve the reliability of information used for care coordination, analytics and reporting.

These capabilities are particularly important as healthcare organizations expand value-based care initiatives and prepare for more advanced digital quality measurement requirements.

AI Raises the Stakes for Data Quality

The need for reliable healthcare information becomes even more critical as payers and providers increase their use of artificial intelligence.

AI offers opportunities to automate administrative workflows, identify gaps in care, support predictive analytics and improve operational efficiency. However, these applications depend on the accuracy, completeness and consistency of the underlying data.

For example, an AI application designed to identify patients who may benefit from additional care management could produce incomplete insights if clinical histories, claims information or patient identifiers are missing or inconsistent.

The Aetna survey also found that approximately 30% of providers identified using AI to flag suspicious claims or patterns as the most important action payers could take to address fraud, waste and abuse.

While this reflects growing interest in AI-enabled capabilities, successful implementation requires more than sophisticated algorithms. Organizations must ensure that the information supporting these applications is reliable and appropriately governed.

Establishing an AI-ready data foundation through aggregation, normalization, validation and ongoing quality monitoring can help organizations reduce risks while improving the value of their technology investments.

Turning Better Data Into Better Healthcare

The latest Aetna findings reinforce an important priority for healthcare organizations. Improving payer-provider collaboration requires more than streamlined administrative processes. It also depends on delivering timely, accurate and actionable information.

For payers and providers, investing in stronger data management capabilities can help reduce administrative burdens, improve care coordination and support more informed decisions.

IMAT Intelligence helps healthcare organizations address these challenges by aggregating, standardizing and validating disparate clinical and claims data. By transforming fragmented information into trusted, actionable intelligence, the platform supports more efficient data management, quality measurement, population health analytics and AI readiness.

As interoperability and AI adoption continue to advance, organizations that prioritize high-quality data will be better positioned to improve operational efficiency, strengthen collaboration and deliver better patient care.

Discover how IMAT Intelligence can help your organization overcome healthcare data challenges, improve data quality and build a stronger foundation for analytics and AI. Contact IMAT Solutions to learn more.

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Additional Insights

• Why Health Plans Are Shifting Focus from Data Exchange to Data Intelligence
• Why AI Ready Data Drives Sustainable Quality Performance for Health Plans
• What Senior Quality Leaders Must Do Now to Prepare for Digital HEDIS
• Why Health Plan Data Infrastructure Is Becoming a Competitive Advantage