14 Oct Healthcare Interoperability Challenges Persist. What Comes Next?

Despite widespread adoption of electronic health records and progress in nationwide data exchange, healthcare interoperability challenges continue to limit how effectively organizations can access and use patient information. A new congressional report highlights why data quality, standardization and usability remain essential.

Top Takeaways

• A new Congressional Research Service report identifies four persistent barriers to healthcare interoperability.
• healthcare interoperability.
• Widespread EHR adoption has not eliminated challenges involving data exchange, accessibility and usability.
• Uneven participation among healthcare organizations continues to limit interoperability progress.
• Data normalization and validation can help organizations make exchanged information more reliable and actionable.
• IMAT Intelligence helps healthcare organizations turn fragmented clinical and claims data into trusted information for quality measurement, population health and AI.

Healthcare Interoperability Challenges Draw Renewed Attention

Healthcare has spent decades investing in electronic health records, data exchange infrastructure and interoperability standards. Yet significant gaps remain in how information moves between organizations and how effectively it can be used.

A new Congressional Research Service (CRS) report, highlighted in recent Legis1 coverage, underscores these continuing challenges.

According to the report, 100 percent of nonfederal acute care hospitals reported using certified electronic health records (EHRs) in 2024, along with 91 percent of office-based physicians. Despite that progress, interoperability remains uneven across the healthcare ecosystem.

The CRS identifies four major obstacles: uneven health IT adoption, information blocking, gaps in the Trusted Exchange Framework and Common Agreement (TEFCA), and governance structures that have struggled to keep pace with technological change.

The findings demonstrate that widespread adoption of digital health technology has not resolved the challenges of making healthcare information consistently accessible, reliable and useful.

Why Healthcare Interoperability Gaps Remain

One significant barrier is uneven participation. While hospitals and physician practices have achieved high levels of EHR adoption, other segments of healthcare remain less connected. The CRS report notes that only 19 percent of surveyed behavioral health facilities participated in a health information exchange (HIE).

Public health agencies also face challenges involving funding, technical capabilities and the ability to receive and process electronic messages.

Meanwhile, TEFCA continues to expand. The framework now includes more than 21,000 live organizations, demonstrating growing momentum toward nationwide exchange. However, questions involving trust, governance and participation persist.

For healthcare organizations, these challenges can translate into incomplete patient records, inconsistent information across systems and additional administrative work. They can also complicate efforts to coordinate care, measure quality and manage population health.

Improving Healthcare Data Quality and Usability

Addressing these challenges requires continued progress on national interoperability policies and standards, along with improvements within individual healthcare data environments.

Information received from multiple sources may contain missing fields, duplicate records, inconsistent terminology or conflicting formats. Even when systems successfully exchange information, these issues can limit its usefulness.

Data normalization and validation help create greater consistency across clinical and claims information, allowing organizations to integrate disparate sources into a more reliable foundation for analysis, reporting and operational decisions.

Healthcare organizations also need to understand where their existing data processes fall short. Investments in connectivity may have expanded access to information without adequately addressing its completeness, accuracy or readiness for downstream applications.

A structured data quality assessment can identify weaknesses in integration, normalization and validation, helping organizations prioritize improvements and maximize the value of existing interoperability investments.

Building Stronger Data Foundations with IMAT Intelligence

The importance of reliable, usable data continues to grow as healthcare organizations expand their digital capabilities.

Digital quality measurement requires consistent information from multiple sources. Population health initiatives depend on comprehensive patient data to identify care gaps and monitor outcomes. AI applications require trustworthy information to generate meaningful insights and support informed decisions.

Organizations that improve their data infrastructure for interoperability can also strengthen their readiness for quality reporting, analytics and AI. IMAT Intelligence helps organizations address these requirements by aggregating, normalizing, validating and operationalizing clinical and claims data from disparate sources.

The platform enables health plans, HIEs and providers to transform fragmented information into consistent, actionable data that supports interoperability, digital quality measurement, population health, value-based care and AI readiness.

By streamlining complex data management processes, IMAT Intelligence also helps organizations reduce manual effort, improve operational efficiency and accelerate access to the information needed for critical healthcare initiatives.

For organizations seeking to evaluate their existing capabilities, IMAT’s Health Data Quality Assessment provides a structured review of data integration, normalization, completeness and accuracy, identifying gaps and establishing a practical roadmap for improvement.

Turning Interoperability Progress into Measurable Value

The latest CRS findings demonstrate that expanding connectivity is only part of the work required to achieve meaningful healthcare interoperability. Organizations must also ensure that exchanged information is reliable, consistent and ready to support clinical and business priorities.

IMAT Intelligence helps bridge that gap by providing the data management and intelligence capabilities needed to make information from multiple sources more accessible, accurate and useful.

As nationwide exchange continues to evolve, healthcare organizations have an opportunity to build on their interoperability investments, strengthen their data foundations and realize greater value from the information already available to them.

Contact us to learn how IMAT Intelligence and our Health Data Quality Assessment can help your organization overcome healthcare interoperability challenges and turn complex data into actionable intelligence.

 

 

 

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Additional Insights 

• IMAT to Showcase Health Data Quality for HIEs at Civitas 2026
• Why AI Ready Data is the Key to Hitting Population Health KPIs
• Interoperability in 2026: Progress, Gaps, and What It Means for Closing Care Gaps
•Health IT Answers: Why Data Intelligence Is the Missing Link in Healthcare Modernization