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The Role of SNOMED CT in AI and Analytics

By MedCode Mastery
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The Role of SNOMED CT in AI and Analytics

If you’ve been following the digital transformation of UK healthcare, you’ve probably heard about FHIR, openEHR, and the growing use of AI in clinical decision-making. But there’s another player that doesn’t always get the spotlight—SNOMED CT.

At first glance, SNOMED CT might sound like just another clinical coding system. But in reality, it’s one of the foundational tools that make AI, analytics, and interoperable healthcare systems possible in the NHS. In this article, we’ll explore what SNOMED CT is, why it matters for AI and analytics, and how it works with FHIR and other UK healthcare digital initiatives.

What is SNOMED CT?

SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) is the internationally recognised, structured clinical terminology used in the NHS to represent healthcare concepts. It’s more than just a list of codes—it’s a hierarchical, interconnected vocabulary that captures the meaning of clinical information.

Here’s a simple way to think about it:

  • Without SNOMED CT, a GP might record “high blood pressure” in multiple ways—“hypertension,” “HTN,” or “raised BP.”
  • SNOMED CT gives each concept a unique code and defines relationships between terms. That way, systems can understand that all these phrases refer to the same clinical condition.

For the NHS, SNOMED CT is mandatory in primary care and is increasingly used in secondary care, supporting accurate, standardised clinical documentation across GPs, hospitals, and integrated care systems (ICSs).

Why SNOMED CT is Critical for AI

Now, let’s connect SNOMED CT to artificial intelligence. AI models, particularly those used in healthcare, thrive on structured, consistent, and high-quality data. Here’s how SNOMED CT contributes:

Data Consistency

AI models can struggle if the same condition is recorded in multiple ways. SNOMED CT ensures that “type 2 diabetes” is consistently coded across datasets, making AI predictions more reliable.

Hierarchical Relationships

SNOMED CT isn’t just a flat list of codes—it’s hierarchical. For example, “myocardial infarction” is a type of “ischemic heart disease.” AI can leverage these relationships to identify patterns, group patients by risk, or support predictive analytics.

Interoperability

When AI models receive data from multiple NHS systems, SNOMED CT codes ensure that the model understands the meaning of each term, whether the data comes from a GP practice, a hospital, or even a remote monitoring device.

Scalable Analytics

SNOMED CT allows for large-scale analytics across populations. Researchers can aggregate data using codes to understand disease prevalence, treatment outcomes, and healthcare utilization, all without worrying about inconsistencies in language.

FHIR and SNOMED CT: A Perfect Pairing

You might be wondering: where does FHIR fit into this picture? Well, FHIR and SNOMED CT complement each other perfectly.

FHIR provides a standardized framework for exchanging healthcare data, using resources like Patient, Condition, Observation, and MedicationRequest. SNOMED CT ensures that the clinical content within these resources is precise and unambiguous.

For example:

  • A FHIR Condition resource might include a SNOMED CT code for “asthma.”
  • A FHIR Observation resource could use SNOMED CT for “blood glucose level” combined with units and values.

This combination makes it possible to:

  • Share clinical data across the NHS reliably.
  • Feed high-quality, structured data into AI models.
  • Enable advanced analytics and population health management.

SNOMED CT in Analytics

Analytics in the NHS goes far beyond individual patient care. SNOMED CT plays a central role in:

Population Health Analytics

By coding conditions consistently, NHS analysts can track disease prevalence, identify at-risk populations, and allocate resources more effectively.

Clinical Outcomes Measurement

Hospitals and ICSs can use SNOMED CT to compare outcomes across trusts, understand variations in care, and improve quality.

Predictive Modelling

AI models predicting hospital readmissions, risk of chronic disease, or early signs of deterioration rely on standardized clinical concepts. Without SNOMED CT, these predictions would be inconsistent and unreliable.

Research and Innovation

SNOMED CT enables researchers to extract large datasets from EHRs for clinical trials, epidemiological studies, or public health research while maintaining semantic accuracy.

Decision Support Systems

Clinical decision support tools use SNOMED CT to trigger alerts, recommend treatments, or highlight contraindications. For example, an AI model may alert a clinician if a patient with “type 2 diabetes” (SNOMED CT code 44054006) also has “chronic kidney disease” (SNOMED CT code 431855005), suggesting a dose adjustment for medication.

Challenges in Using SNOMED CT for AI

Of course, implementing SNOMED CT for AI and analytics isn’t without challenges:

  • Complexity SNOMED CT has over 350,000 concepts and intricate hierarchies. Mapping local data to these codes requires expertise.
  • Data Quality Even with SNOMED CT, data must be accurately entered at the source. Poor documentation or missing codes can limit the usefulness of AI models.
  • Integration with Legacy Systems Older NHS systems may not fully support SNOMED CT or may store data in proprietary formats, requiring additional mapping or ETL processes.
  • Training AI Models Models need large volumes of high-quality SNOMED CT-coded data. Accessing sufficient datasets while maintaining UK information governance and GDPR compliance can be a challenge.

Best Practices for Using SNOMED CT in AI

To get the most out of SNOMED CT in analytics and AI, NHS organisations should follow these best practices:

Adopt FHIR UK Core

Use FHIR resources aligned with UK Core standards. This ensures interoperability and makes AI-ready data consistent across systems.

Ensure Data Governance

Follow NHS Information Governance standards, including role-based access, consent management, and audit trails. High-quality, secure data is essential for AI.

Regularly Update SNOMED CT

SNOMED CT releases updates twice a year. Keeping your systems current ensures AI models are working with the latest concepts.

Standardize Data Entry

Train clinicians and staff to use SNOMED CT accurately. User-friendly EHR interfaces with autocomplete and validation can improve coding consistency.

Leverage Mapping Tools

When integrating legacy or third-party data, use mapping tools to convert local codes to SNOMED CT reliably.

The Future of SNOMED CT in UK Healthcare

As AI and analytics become central to the NHS, SNOMED CT will play an increasingly important role. Some emerging trends include:

AI-Assisted Coding

Natural Language Processing (NLP) tools can automatically suggest SNOMED CT codes from clinical notes, reducing manual entry and improving consistency.

Predictive Population Health Models

With SNOMED CT-coded datasets, ICSs can identify population-level trends, anticipate healthcare demand, and target interventions more effectively.

Integration with Genomics and Precision Medicine

SNOMED CT codes can be combined with genomic and lifestyle data to enable AI-driven precision medicine, offering treatments tailored to individual patients.

Real-Time Clinical Decision Support

AI systems using SNOMED CT can provide alerts and recommendations in real-time during patient encounters, supporting clinicians with actionable insights.

Interoperable Research Platforms

SNOMED CT-coded datasets, exposed via FHIR APIs, will support national and international research collaborations, enabling large-scale studies without losing semantic accuracy.

SNOMED CT, FHIR, and the Connected NHS

The NHS is on a journey toward a fully connected healthcare ecosystem. FHIR, openEHR, and SNOMED CT together form the backbone of this transformation:

  • FHIR UK Core APIs provide a standard way to exchange healthcare data.
  • SNOMED CT ensures that the clinical meaning of that data is unambiguous.
  • openEHR offers a structured, long-term repository for patient records.

AI and analytics leverage these standardized, high-quality datasets to improve care, predict outcomes, and support population health.

When combined, these tools allow the NHS to move from fragmented data silos to a truly interoperable, data-driven healthcare system.

Final Thoughts

If you’re working in UK healthcare IT, AI, or analytics, SNOMED CT isn’t just another coding system—it’s the key to unlocking meaningful insights. By standardising clinical concepts across the NHS, SNOMED CT ensures that AI models can be trained effectively, analytics can scale, and patient care can improve.

FHIR makes sure that data can move between systems reliably. openEHR stores it in a structured, reusable way. And SNOMED CT ensures that, whatever the system or tool, everyone is speaking the same clinical language.

Put it all together, and you have a foundation for innovative, AI-powered, patient-centered healthcare—right here in the UK.

The next time you hear someone talk about AI in the NHS, remember: without SNOMED CT, that AI would struggle to understand the very language of medicine. With it, the possibilities are almost limitless.