Evolved interoperability: Preparing data for AI

Working toward intelligent interoperability and more AI-enabled, connected care

Interoperability has evolved beyond simple system connectivity. Healthcare organizations are now balancing technical exchange requirements with broader operational priorities, including care coordination, administrative efficiency, scalable AI workflows, and access to more complete patient information at the point of care.

In a recent Becker’s Healthcare webinar, healthcare leaders raised candid questions about FHIR adoption, interoperability economics, patient matching, governance, and the growing intersection between interoperability and AI-assisted workflows.¹ Below, we’ve consolidated the strongest themes into a practical expert Q&A focused on the operational realities organizations are navigating today.

What are the biggest interoperability challenges healthcare organizations still face today?

While interoperability capabilities have expanded significantly across healthcare, organizations still encounter persistent operational challenges that affect the consistency and usability of exchanged data. Three issues continue to surface most frequently: source data quality, patient matching, and interface management and maintenance.

Source data quality

Even when systems exchange information successfully, the underlying data may be incomplete, inconsistently coded, or missing important clinical context.

For example:

• Medication lists without dosage information
• Incomplete problem lists
• Lab data lacking reference ranges

In those situations, organizations may achieve technical interoperability without delivering meaningful clinical usability.

Patient matching

Accurately matching patients across systems remains difficult without a universal patient identifier. Most organizations rely on probabilistic matching approaches using demographic and historical data, but inconsistencies still create operational complexity.

Interface management and maintenance

Healthcare organizations often manage large ecosystems of integrations across hospitals, labs, payers, imaging centers, specialty providers, and regional exchange networks. Those connections require ongoing monitoring, maintenance, troubleshooting, and standards updates over time. This is where many organizations are increasingly reevaluating traditional point-to-point integration models.

At athenahealth, our network-enabled interoperability approach is designed to reduce some of that operational burden by managing many connections centrally across the broader customer network. That means an external party just needs a single connection and can make updates that are quickly deployed across the entire network.

That network approach becomes especially important as interoperability requirements evolve. When API updates, payer requirements, or workflow improvements are deployed centrally across the athenahealth network, organizations can benefit from those updates without rebuilding integrations individually.

We’ve also continued expanding interoperability visibility and management capabilities through the athenaConnect™ hub. In addition to serving as a centralized interoperability management dashboard, athenaConnect is designed to function more like a connected interoperability workspace. Organizations can view integrations, monitor exchange activity, and access historical data-sharing interactions with greater operational context across their interoperability ecosystem.

The goal is not simply visibility into whether data moved successfully. It’s helping organizations better understand how information is exchanged across workflows, systems, trading partners, and care settings over time.

Why has FHIR become such an important part of healthcare interoperability?

FHIR has become increasingly important because it aligns healthcare data exchange more closely with modern API development approaches already common across other industries. Compared to older integration methods, FHIR can make it easier to:

• Retrieve specific clinical data elements
• Support real-time workflows
• Enable patient and provider access applications
• Connect newer analytics or AI tools

That flexibility is helping support operational use cases like prior authorization workflows, care coordination updates, patient access experiences and data visibility expansion through Individual Access Services, as well as event-driven clinical notifications.

At athenahealth, FHIR investment extends across both our interoperability infrastructure and our broader developer ecosystem. Through the athenahealth Developer Portal and FHIR API framework, developers and partners can build integrations that connect more seamlessly into clinical and operational workflows across the network.

Importantly, organizations increasingly need interoperability strategies that support both modern API-driven workflows and legacy integration environments simultaneously. That’s why athenahealth continues supporting FHIR-based interoperability alongside broader integration frameworks that include HL7, CCDA, payer connectivity, and national exchange participation.

FHIR is an important step forward for healthcare interoperability. At the same time, organizations are still navigating the operational realities of implementation consistency, governance, and long-term maintenance.

Does broader FHIR adoption fully solve interoperability?

FHIR meaningfully improves the foundation for healthcare data exchange, but interoperability still depends heavily on implementation consistency, governance, and operational execution.

Two organizations may both support FHIR while still exposing very different levels of data completeness, standardization, and usability. That’s why industry efforts increasingly extend beyond the technical standard itself into:

• Implementation guides
• Governance frameworks
• Network participation models
• Shared exchange agreements

TEFCA represents one example of that broader evolution. It helps establish more consistent governance and exchange expectations across participating networks. A useful way to think about it:

FHIR defines how data can be exchanged. Frameworks like TEFCA help define how organizations participate in exchange ecosystems at scale.

At athenahealth, we view both FHIR and TEFCA as important parts of the broader interoperability landscape. Our participation in national exchange frameworks, combined with our network-enabled interoperability approach, is designed to help practices exchange information more seamlessly across providers, payers, and care settings without requiring customers to manage every connection independently.

That broader network model also creates operational advantages as interoperability standards evolve. Rather than organizations individually updating large volumes of integrations over time, shared infrastructure and centrally deployed API enhancements can help streamline adoption across the network.

If interoperability standards become more widely adopted, what differentiates interoperability vendors?

As interoperability standards mature, differentiation increasingly shifts away from basic connectivity alone. Healthcare organizations are placing greater emphasis on:

• Operational reliability
• Network scale
• Workflow integration
• Data normalization
• Governance
• Ongoing service management

Exchanging data successfully is only one part of the challenge. Organizations also need workflows that help clinicians and operational teams use that information effectively within day-to-day care delivery and administrative processes. That includes surfacing relevant information at appropriate points in workflow, maintaining integration performance over time, and reducing operational complexity for customers managing large interoperability ecosystems.

At athenahealth, our interoperability strategy continues to center around scaling connectivity and operational support across the broader network so customers can activate and manage integrations more efficiently over time.

That includes investments in:

• Shared interoperability infrastructure
• FHIR API capabilities
• National exchange participation
• Payer connectivity
• Centralized interoperability management through the athenaConnect hub

The operational goal is not simply more connectivity. It’s reducing friction for healthcare organizations navigating increasingly complex interoperability environments.

How is interoperability evolving to support AI-driven clinical workflows?

As healthcare organizations expand their use of AI-assisted workflows, interoperability is becoming increasingly tied to how effectively those tools function in practice. Ambient documentation, clinical copilots, predictive analytics, and workflow automation all depend on timely access to structured, contextual clinical data across systems and care settings.

That’s one reason FHIR adoption, API modernization, and broader interoperability initiatives matter operationally beyond compliance alone. These efforts help reduce barriers between systems, improve data accessibility, and create more consistent foundations for newer workflow technologies.

In many organizations, interoperability and AI initiatives are now maturing together. Stronger interoperability supports more reliable downstream AI workflows, while AI adoption places greater pressure on organizations to improve upstream data quality, governance, and normalization.

Network-enabled interoperability models are becoming increasingly important in that environment because AI workflows depend heavily on scalable, standardized data access across distributed care settings.

At athenahealth, our interoperability investments are designed to support that evolution through:

• Standards-based FHIR APIs
• Broader network connectivity
• Participation in national exchange frameworks
• Centralized interoperability management through athenaConnect
• Infrastructure designed to support more seamless data movement across providers, payers, and clinical workflows

The broader trend emerging across healthcare is not simply more AI adoption. It’s the gradual development of more connected workflows built on more accessible, interoperable data foundations.

How should organizations think about AI accuracy and oversight?

Healthcare organizations are increasingly viewing AI documentation tools as workflow support technologies rather than autonomous clinical systems. These tools can help reduce administrative burden, but provider oversight remains essential, particularly when documentation involves nuanced assessments, borderline diagnoses, or incomplete clinical context. The organizations seeing sustainable adoption tend to build governance directly into the workflow.

Common operational approaches include:

• Provider review before note finalization
• Confidence indicators for AI-generated suggestions
• Structured documentation workflows
• Clear escalation paths when ambiguity exists

Many health systems are also investing in provider education around, where AI tools perform well, where they may overreach, and how clinicians can validate outputs efficiently within existing workflows. Some vendors, like athenahealth, also offer pilot programs so organizations can have test-and-learn opportunities when new capabilities roll out.

Importantly, organizations are learning that AI performance often depends heavily on the quality and accessibility of the underlying data ecosystem. Inconsistent interoperability, fragmented records, or incomplete source documentation can limit the usefulness of downstream AI-assisted workflows.

That’s why many healthcare leaders increasingly view interoperability modernization, FHIR-based access, workflow integration, and AI governance as interconnected operational priorities rather than isolated technology initiatives.

At athenahealth, we’re focused on integrating AI-assisted documentation into athenaOne® workflows in ways that reduce administrative friction while maintaining clinical accountability, provider control, and alignment with broader interoperability infrastructure.

The broader lesson emerging across healthcare is that sustainable AI adoption depends less on automation alone and more on how effectively organizations connect data, workflows, governance, and clinical oversight together.