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 organisations connect patient information across systems so it can be accessed, understood, and used in everyday workflows. Before expanding AI initiatives, organisations should review their data, systems, and governance to find gaps. An interoperability assessment can help improve data quality, simplify integration, and prepare the organisation for AI adoption.
AI is moving from experimentation into everyday healthcare workflows. According to reports, the technology has enabled notable progress in diagnostics, data analysis, and precision medicine, with applications ranging from patient triage to cancer detection. Language models such as ChatGPT have performed well on medical examinations and clinical case tasks, while Med-PaLM was developed to answer medical questions for professionals and patients.
A strong data foundation is essential for healthcare AI. AI tools rely on access to patient information across the systems where care is documented and delivered. When that information remains spread across disconnected systems, clinicians may spend more time finding and entering data, while AI tools have less context to work with. For organisations in the UK and Europe already under pressure to control costs and improve patient outcomes, these gaps can make it harder to get real value from AI investments.
A global market insights survey found that 42% of healthcare leaders consider combining data from multiple electronic patient records (EPRs) their biggest interoperability challenge. Only 31% said that at least half of their unstructured documents and images were available at the point of care. For organisations adopting AI, interoperability in healthcare is therefore a priority. It helps make patient data more complete, consistent, and usable across systems.
42%
of healthcare leaders consider combining data from multiple EPRs 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 laboratory 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 laboratory 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, organisations 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 EPR to another | Necessary, but insufficient |
| Structural | Consistent organisation and formatting of data | Returning allergies, medications, and laboratory 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 |
| Organisational | 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 GP practice 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 organisation. 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 organise and exchange information, and in the UK they are profiled as FHIR UK Core and ITK3. 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, which in the UK means SNOMED CT and dm+d. It is especially important for AI because a clinician may recognise an unclear label, missing unit, or local code, while an AI system may interpret it incorrectly.
Organisational interoperability
Organisational interoperability focuses on how healthcare organisations work together to share and use information. It includes the policies, agreements, responsibilities, and processes that support data sharing, such as information sharing agreements and the Caldicott Principles in the UK. 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 programmes 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. An integrated care board in the UK, or a statutory health insurer in Europe, 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 such as UKHSA in the UK, and the ECDC in Europe, can work with standardised data to track changes in population health and plan their response. For life sciences organisations, 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 organisations 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, organisational, economic, and regulatory. The most persistent challenges with interoperability in healthcare arise when these categories reinforce one another.
Patient identification across settings
The UK does have a national patient identifier. The NHS number is used in England and Wales, with the CHI number in Scotland and the Health and Care Number in Northern Ireland. Healthcare organisations still use their own record numbers along with information such as names, dates of birth, and addresses 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, FHIR UK Core, and ITK3 are widely used, but organisations do not always implement them consistently. Different HL7 versions, customised 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 organisations managing multiple integrations to exchange data.
Terminology & coding variance
Healthcare data is recorded using different terminology and classification systems. In the UK these include SNOMED CT, dm+d, OPCS-4, and ICD-10, alongside legacy Read codes. In Europe, countries combine SNOMED CT, ICD-10 or ICD-11, LOINC, and national classifications. Mapping between them can sometimes lose detail or depend on context. This can create problems when combining data for analytics, quality measurement, and AI.
Supplier lock-in and commercial barriers
NHS England uses procurement standards and the Digital Technology Assessment Criteria to discourage closed systems. Organisations can still face financial, contractual, or technical barriers when trying to obtain or exchange data. Certain fees, contractual restrictions, and technical limitations can make data exchange more difficult and contribute to the persistence of data silos. In Europe, the European Health Data Space places direct obligations on suppliers to support interoperability and data access.
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, laboratory 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 and ITK3 | Structured clinical documents and summaries |
| FHIR and FHIR UK Core | Resource-based APIs and modern application exchange |
| UK Core and the Core Information Standard | A common set of health-data classes and elements for exchange across the NHS in the UK |
| SNOMED CT | Clinical concepts and conditions |
| LOINC | Laboratory and clinical observations |
| ICD-10 | Diagnoses and reporting or billing classifications |
| dm+d | Standard names and identifiers for medicines and devices used in the UK |
| Shared care records and MyHealth@EU | Governance, policy, and technical exchange frameworks in the UK and Europe |
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. In the UK, CDA is applied through the NHS England ITK3 specification for documents such as discharge summaries and outpatient letters.
FHIR, or Fast Healthcare Interoperability Resources, is a newer healthcare data exchange standard from HL7 that supports modern applications and APIs. It organises 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. It is the mandated clinical terminology across the NHS in the UK. |
| LOINC | Identifies laboratory tests, measurements, and other clinical observations. |
| ICD-10 | Classifies diseases and health conditions for reporting, statistics, reimbursement, and health-service planning. |
| Dm+d | Standardised medicine and device terminology that provides consistent names and identifiers for the products used in the NHS in the UK. |
Frameworks for trusted exchange
In the UK, trusted exchange is built around shared care records in every integrated care system, the National Record Locator, and national services such as the Personal Demographics Service and the Summary Care Record. Access is governed through information sharing agreements and the Data Security and Protection Toolkit.
In Europe, the European Health Data Space Regulation sets a common framework for the primary and secondary use of health data, and MyHealth@EU provides the infrastructure for cross-border exchange of patient summaries, ePrescriptions, laboratory results, and imaging reports.
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. Organisations 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 organisation. Look at your EPRs, 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, for example through the NHS App in the UK, 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, ideally on the NHS number, 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. In the UK, align those mappings to SNOMED CT and dm+d. 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 ensure 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 organisations, 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 organisations 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, NHS number, 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 organised consistently. Semantic means systems understand the data in the same way. Organisational means having the policies, workflows, agreements, and rules needed to support effective data sharing.
What interoperability standards apply in the UK?
NHS organisations work to standards published by NHS England, including FHIR UK Core and ITK3 for exchange, SNOMED CT for clinical terminology, and dm+d for medicines. Data protection is governed by UK GDPR, the Data Protection Act 2018, and the common law duty of confidentiality, and the clinical safety standards DCB0129 and DCB0160 apply. In Europe, the European Health Data Space is introducing comparable requirements.
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. Organisations 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 EPR integration?
EPR integration connects an EPR 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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