Interoperability in Healthcare: The Data Infrastructure AI Needs Before It Can Deliver
- 10Pearls Editorial Team
- 15 min read
Summary
Healthcare AI relies on good data. Interoperability helps organizations connect patient information across systems so it can be accessed, understood, and used in everyday workflows. Before expanding AI initiatives, organizations should review their data, systems, and governance to find gaps. An interoperability assessment can help improve data quality, simplify integration, and prepare the organization for AI adoption.
42%
of healthcare leaders consider combining data from multiple EHRs their biggest interoperability challenge.
31%
said that at least half of their unstructured documents and images were available at the point of care.
What interoperability in healthcare actually means
When a patient moves from one healthcare provider to another, their information may need to move with them. Test results, medications, diagnoses, and clinical notes can sit in different systems, and getting that information into the right place can take time. The definition of interoperability in healthcare comes down to whether those systems can share information in a way that the receiving system can actually use. A lab result, for example, should arrive as usable clinical data rather than a document that someone has to read and enter manually. This is what makes interoperability important for connected care and modern healthcare technology.
This goes beyond simply sending data from one system to another. For example, a lab result sent as a PDF may reach the right clinician, but someone still has to read it and enter the details into the patient record. With effective interoperability, the result can move directly into the appropriate system and be used within the clinical workflow.
This also makes interoperability in healthcare an important part of building a modern data environment. When information can move reliably between systems, organizations have a stronger foundation for improving care coordination, reducing manual work, and supporting technologies such as analytics, automation, and AI.
The four types of interoperability in healthcare
Healthcare interoperability is usually described across four levels. Each level addresses a different part of the journey from moving data between systems to making that data useful in care.
| Level | What it enables | Healthcare example | AI relevance |
|---|---|---|---|
| Foundational | Secure data transport between systems | Sending a discharge summary from one EHR to another | Necessary, but insufficient |
| Structural | Consistent organization and formatting of data | Returning allergies, medications, and lab results in identifiable fields | Makes data machine-readable |
| Semantic | Shared meaning across systems | Mapping two local terms to the same clinical concept | Essential for reliable model inputs |
| Organizational | Exchange across different institutions, rules, and incentives | A hospital, payer, laboratory, and public-health agency agreeing on access and use | Determines whether exchange works in practice |
Foundational interoperability
This is the basic ability to send and receive health information between two systems. A discharge summary sent from a hospital to a primary care provider is a simple example. The data reaches the other side, but the receiving system may still need someone to process it.
This level solves a transport problem. It does not solve the usability problem. A file can arrive intact while remaining difficult to search, compare, calculate, or incorporate into a patient record.
Structural interoperability
Structural interoperability adds a consistent format and organization. The receiving system can identify the fields in a message and process them more reliably. For example, a laboratory result may include separate fields for the test name, value, unit, reference range, specimen time, and performing laboratory.
Standards such as HL7 and FHIR provide a common way for healthcare systems to organize and exchange information. This makes the data easier for software to use. However, using the same fields does not always mean that different systems will interpret the information in the same way.
Semantic interoperability
Semantic interoperability means that different healthcare systems understand health information in the same way. A receiving system should be able to interpret a laboratory result, diagnosis, medication, or procedure even when the source system records it differently.
This depends on common terminology, mappings, and enough context to explain the data. It is especially important for AI because a clinician may recognize an unclear label, missing unit, or local code, while an AI system may interpret it incorrectly.
Organizational interoperability
Organizational interoperability focuses on how healthcare organizations work together to share and use information. It includes the policies, agreements, responsibilities, and processes that support data sharing. It also considers privacy, governance, and clinical workflows, since successful interoperability depends on more than systems being able to exchange data.
These four levels build on one another. Moving data is the starting point, but the real value comes when that data arrives in a form that another system and the people using it can understand and act on.
The importance of interoperability in healthcare has changed
The importance of interoperability in healthcare IT services has increased because the consuming systems have changed. Earlier interoperability programs focused largely on making records available to clinicians and reducing duplication. Those goals remain important. AI adds another requirement that the data must be sufficiently complete, consistent, contextual, and timely for software to reason over it.
Healthcare interoperability matters when patient information is stored in different places. A clinician may have the patient’s medications in one system, test results in another, and specialist notes somewhere else. Finding and piecing together that information takes time and can leave gaps in the patient’s history. It can also lead to repeated tests, conflicting treatments, slower decisions, and more administrative work.
The problem is not only whether data can be exchanged. It is whether the receiving system can incorporate and present that data in a usable form. An ONC interoperability roadmap reported that approximately 41% of hospitals had routine electronic access to necessary information from outside providers. Although 78% of hospitals sent a summary-of-care document and 56% received one, fewer than half integrated the information they received into the patient’s record. These figures are based on data from 2013 to 2015, but they illustrate the difference between exchanging information and making it part of clinical work.
41%
of hospitals had routine electronic access to necessary information from outside providers.
78%
of hospitals sent a summary-of-care document.
56%
of hospitals received a summary-of-care document.
The value of interoperability extends well beyond the point of care. A health plan with access to a broader set of patient information can see where care gaps exist and better understand how services are being used. Public-health agencies can work with standardized data to track changes in population health and plan their response. For life sciences organizations, governed access to health data can support research and help assess treatment outcomes. Together, these connections create a more complete picture of a person’s care across providers and over time.
For patients and providers, better data exchange can make referrals more efficient and give clinicians access to information that supports better-informed decisions. It can also reduce repeated procedures and manual data entry. For healthcare organizations investing in analytics, automation, and AI, interoperable data provides an important foundation. Interoperability provides the connection, but the quality of the data determines how useful that connection becomes. For the information to support clinical and operational decisions, it needs to be accurate, up to date, complete, secure, and presented in the right context.
Barriers to interoperability in healthcare
The barriers to interoperability in healthcare are technical, semantic, organizational, economic, and regulatory. The most persistent challenges with interoperability in healthcare arise when these categories reinforce one another.
No national patient identifier
The U.S. does not have a single national patient identifier. HIPAA called for a unique identifier, but one was never put into use. Healthcare organizations instead use their own record numbers along with information such as names, dates of birth, addresses, and insurance details to match patients. When those details do not line up, finding the right record can be difficult.
Inconsistent standards adoption
Healthcare data exchange standards such as HL7 v2, C-CDA, and FHIR are widely used, but organizations do not always implement them consistently. Different HL7 versions, customized interfaces, and proprietary systems are still common. Some information also comes in PDFs or other formats that are difficult for systems to process. This can leave organizations managing multiple integrations to exchange data.
Terminology & coding variance
Healthcare data is recorded using different terminology and classification systems. They may use different terminology and coding systems, including ICD-10-CM, CPT, HCPCS, LOINC, RxNorm, and SNOMED CT. Mapping between them can sometimes lose detail or depend on context. This can create problems when combining data for analytics, quality measurement, and AI.
Information blocking & Vendor Incentives
Federal information-blocking rules were introduced to prevent practices that interfere with the access, exchange, or use of electronic health information. However, organizations can still face financial, contractual, or technical barriers when trying to obtain or exchange data. Certain fees, contractual restrictions, and technical limitations may create problems when they meet the regulatory definition of information blocking. These barriers can make data exchange more difficult and contribute to the persistence of data silos.
The Federal & State Regulatory Patchwork
Healthcare organizations have to follow both federal and state rules when sharing health information. These include HIPAA, the 21st Century Cures Act, CMS interoperability requirements, and state privacy laws. State laws may add extra privacy or consent requirements. As a result, data-sharing rules can vary by location and type of information, making healthcare data integration more complex.
Healthcare interoperability standards in 2026
Healthcare interoperability standards can seem like a confusing list of acronyms. Each one serves a different purpose. Some standards explain how healthcare systems exchange information, while others help ensure that diagnoses, lab results, medications, and other clinical data have a consistent meaning.
| Standard or framework | Primary purpose |
|---|---|
| HL7 v2 | Event and message exchange, including admissions, discharges, and laboratory workflows |
| CDA | Structured clinical documents and summaries |
| FHIR | Resource-based APIs and modern application exchange |
| USCDI | A common set of health-data classes and elements for nationwide exchange |
| SNOMED CT | Clinical concepts and conditions |
| LOINC | Laboratory and clinical observations |
| ICD-10 | Diagnoses and reporting or billing classifications |
| RxNorm | Normalized medication names and concepts |
| TEFCA | Nationwide governance, policy, and technical exchange framework |
Standards for exchanging information
HL7 v2 is an established and widely used healthcare messaging standard. It allows systems to send structured messages when events such as admissions, discharges, transfers, or laboratory results occur. It has been widely used for operational workflows, but different implementations can make these messages harder to interpret and maintain.
CDA is used for clinical documents. A discharge summary, consultation note, or continuity-of-care document can include a complete clinical narrative along with structured information that software can read. C-CDA, or Consolidated CDA, provides guidance for common clinical document types used in the United States.
FHIR, or Fast Healthcare Interoperability Resources, is a newer healthcare data exchange standard from HL7 that supports modern applications and APIs. It organizes health information into resources such as Patient, Observation, Medication, and Allergy Intolerance. Applications can request specific information, such as a patient’s current medications, in a structured format such as JSON. FHIR is becoming more common in healthcare integrations, although how much data is available and how it is implemented can vary.
Standards for clinical meaning
| SNOMED CT | Represents clinical concepts, including conditions, findings, procedures, and anatomical terms. |
| LOINC | Identifies laboratory tests, measurements, and other clinical observations. |
| ICD-10 | Classifies diseases and health conditions for reporting, statistics, reimbursement, and health-service planning. |
| RxNorm | Standardized medication terminology that provides consistent names for clinical drugs across different brands, manufacturers, and formulations. |
Frameworks for trusted exchange
TEFCA stands for the Trusted Exchange Framework and Common Agreement. It provides a nationwide framework and common requirements for sharing health information across networks. QHINs, or Qualified Health Information Networks, form the backbone of this network of networks and connect participating organizations.
The CMS Interoperability Framework is a separate, voluntary blueprint for networks and other participants. It includes criteria for FHIR APIs, USCDI data, terminology compliance, patient access, provider access, identity, security, and trust. Its 2026 criteria also address access to claims, prior authorizations, and clinical information.
Interoperability is more than data exchange
Connecting two healthcare systems is only one part of interoperability. The data that moves between them still needs to be complete, accurate, current, and useful. In practice, an active interface does not always mean that the receiving system has reliable information to work with.
Healthcare data can have many quality issues. Patient records may be duplicated, units may be missing, and local codes may not be mapped to a common terminology. Some fields may contain very little useful information even though they pass basic validation. Dates and timestamps can also be lost or changed when information moves between systems. These issues affect how the data can be used. For example, a medication list without dosage or route does not provide the same information as a complete medication record.
Data quality should therefore be assessed alongside data exchange. Organizations can look at measures such as patient matching, field completeness, terminology mapping, data freshness, duplicate records, errors, and data provenance.
- Patient matching
- Field completeness
- Terminology mapping
- Data freshness
- Errors
- Data provenance
It is also useful to know how much of the data can be used directly in a clinical or analytical workflow without manual correction or re-entry.
This matters even more when AI is involved. An AI system can receive the expected data and still produce unreliable results if the information is incomplete or interpreted differently across sites. A document may contain the information needed by a clinician but not in a form that an AI system can reliably process. For AI projects, the question is not simply whether data can be exchanged. It is whether the data is reliable and usable for the intended purpose.
How to achieve interoperability in healthcare
Achieving interoperability in healthcare is a step-by-step process. It starts with understanding your data, defining what you need to achieve, and putting the right standards and controls in place.
I. Map your data & systems
Start by identifying where your patient data is stored and how it moves through your organization. Look at your EHRs, lab and imaging systems, pharmacy platforms, devices, and other sources. Note who owns the data, how often it is updated, what format it uses, and where quality issues exist.
II. Start with a clear use case
Decide what you want the data exchange to achieve. Sharing records during a patient transfer will have different requirements from giving patients access to their health information or supporting an AI application. The use case should guide your choice of standards, data requirements, and exchange methods.
III. Focus on data quality
Moving data between systems is only useful when the receiving system can understand and use it correctly. Check that patients can be matched accurately, clinical terms are understood, dates are correct, and important information is not missing. Set clear quality standards based on what the use case requires.
IV. Keep terminology consistent
Different systems can use different codes and terms for the same information. Create clear rules for mapping these terms and assign responsibility for maintaining them. Test the mappings regularly, especially when clinical codes or systems change.
V. Build security & governance into the process
Set clear rules for who can access patient data, why they can access it, and how that access is recorded. Keep track of where the information came from and any changes made to it. This helps protect patient privacy and gives healthcare teams confidence in the data.
VI. Monitor interoperability after implementation
Interoperability needs to be monitored after implementation. Track failed exchanges, missing data, patient-matching issues, terminology errors, and system performance. Regular checks can help identify problems early and make sure data continues to move reliably as systems, workflows, and requirements change.
Key checks before starting a healthcare AI project
Before starting an AI project, a health system should be able to answer yes to five questions:
Are the relevant terms coded and mapped?
The project should know which vocabularies and value sets it uses and how local concepts map to them.
Is the relevant history longitudinally complete?
The dataset should identify material gaps across organizations, time periods, and care settings.
Is latency acceptable for the use case?
A retrospective model and a point-of-care tool do not have the same freshness requirement.
Is provenance documented?
Teams should be able to see the source of the data, when it was created, and whether it was changed or cleaned along the way.
Is access governed?
Before deployment, define who has access, what they can use it for, how access is monitored, and what happens when there is a privacy or security concern.
For organizations assessing their next step, a focused interoperability assessment can identify the systems, data-quality gaps, standards, and governance decisions to address before an AI investment scales. Learn more about healthcare AI consulting.
FAQs about interoperability in healthcare
What is interoperability in healthcare?
Healthcare interoperability means different healthcare systems can securely share, understand, and use health information. It goes beyond simply sending data. The information must be in a usable format, linked to the correct patient, understood consistently, and available when needed for care, analytics, or other healthcare purposes.
What are the four types of interoperability in healthcare?
Interoperability is often explained in four levels. Foundational means systems can send data. Structural means data is organized consistently. Semantic means systems understand the data in the same way. Organizational means having the policies, workflows, agreements, and rules needed to support effective data sharing.
What is the CMS interoperability rule for 2026?
The CMS Interoperability Framework is a voluntary, open, standards-based blueprint, not a new regulation. Its 2026 criteria address patient access, provider access, data availability and standards compliance, network connectivity and transparency, and identity, security, and trust. Its FHIR data-availability criteria took effect July 4, 2026.
What are the main barriers to interoperability in healthcare?
Healthcare organizations face several challenges when connecting their systems. These include older systems, different data standards, inconsistent medical terminology, patient-matching errors, poor-quality or missing data, privacy requirements, and proprietary interfaces. Organizations may also have different policies and priorities around data sharing. Because these issues are often connected, simply linking two systems does not always solve the underlying problem.
What is the difference between interoperability and EHR integration?
EHR integration connects an EHR with another system or application so they can exchange data. Interoperability in healthcare covers a much broader area. It focuses on whether different systems can share information, understand it, and use it consistently. An integration can successfully move data between two systems while still leaving problems with missing information, different terminology, or limited clinical context.
Why does AI need interoperable data?
AI depends on reliable data to produce useful results. Interoperability helps bring information together from different healthcare systems and provides a more complete view of the patient. It also supports consistent terminology and patient records. Incomplete, duplicated, outdated, or unclear data can reduce the reliability of AI results.
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