The Next Layer of Intelligence: Turning Healthcare Data into AI-Ready Assets

Using healthcare data is like trying to have a conversation in a room where everyone speaks a different language. EHR systems, claims, and other sources of healthcare data each use different code systems.  If you're a data analyst or leading a technical team, you know this frustration intimately. You spend weeks or more just trying to get these systems to talk to each other before you can even begin to answer the business questions that actually matter.

This is where a semantic layer becomes an essential component; think of it as the universal translator that not only gets all these different data sources speaking the same language, but more importantly, they speak your team’s language. Instead of drowning in incompatible formats and conflicting definitions, you get a single, coherent view that everyone in your organization can trust and understand.

It's not just another piece of technology—it's the bridge that takes you from spending 80% of your time wrestling with data inconsistencies to actually focusing on the insights that can drive your business forward.

Define Once, Deploy Anywhere

A semantic layer sits between raw data sources and end-user tools (dashboards, ML models, queries). It defines:

• Unified Terminology:  Different systems call the same thing by different names. Claims might use member_id, EHRs use MRN. A semantic layer maps them to a universal term like patient_id, ensuring a consistent understanding. Semantically tagged data simplifies the creation of training sets by providing consistent labels and cohorts.  
• Standardized, Defined, and Reusable Metric Logic: Instead of having metric logic defined five different ways, the semantic layer defines it once, so they are trustworthy, reproducible and shorten time to insights. The same cohort logic used in population health dashboards can train models that predict chronic condition onset. 
• Normalized models show relationships across data sources: Diagnoses from claims and EHRs can be harmonized into a shared ontology like HCCs. Dates can be aligned to encounter timelines. These standardizations make cohort building and longitudinal analysis feasible.
• Source-independent queries: Analysts and business users shouldn’t need to know whether “recapture rate” lives in claims or EHR data, or which schema field to query. A semantic layer abstracts this complexity.
  

For analysts, this means you write logic once and reuse it across teams and tools. It is no longer necessary to hand-code joins, reconcile definitions, or rebuild dashboards every time a table schema changes.

An example of the semantic layer in action demonstrates how analysts can get answers to their questions more efficiently.

Business Question:

A health system wants to evaluate whether women aged 50–74 who are at risk for breast cancer (based on clinical and demographic factors) are receiving timely mammograms. Without a semantic layer, analysts would need to:

• Extract patient demographics and risk factors from the EHR, including structured and unstructured notes.
• Join with claims data to find completed screening CPT codes.
• Reconcile patient identifiers across systems.
• Manually interpret different coding systems: SNOMED for diagnoses in EHR, ICD-10 in claims, CPT/HCPCS for procedures, and possibly LOINC for imaging orders.
• Build logic from scratch to define the target population and screening completion window.
• Repeat the process with every schema change or new data feed.
  

This is time-consuming and error-prone, especially when scaling across service lines or programs. 

• Patient eligibility criteria (female, age 50–74) are automatically resolved using standardized demographic fields.
• Risk factors (e.g., family history of breast cancer, prior benign findings) from both EHR SNOMED and claims ICD-10 codes are mapped to a normalized concept: "Breast Cancer Risk Indicators".
• Screening procedures like mammograms are grouped under a standard term: "Breast Cancer Screening", regardless of whether they appear as CPT (77067, 77066) or LOINC imaging orders.
  
• Temporal logic is applied uniformly: screened_within_2_years = TRUE.
  

Now, an analyst (or AI agent) can simply query:

SELECT COUNT(*) 

WHERE cohort = 'Breast Cancer Risk' 

AND screened_within_2_years = FALSE

Or even just ask a natural language question:

“How many women at risk for breast cancer have not had a mammogram in the past 24 months?”

Like for healthcare in general, for life sciences applications, a semantic layer plays a vital role in turning complex, multi-source data into real, actionable insight. Researchers often work with information spread across clinical trial results, EHRs, research publications, genomic databases, and treatment guidelines—each with its own structure and terminology. A semantic layer standardizes these inputs and connects them through a shared understanding of key concepts like diseases, drug mechanisms, and biological pathways.

What makes this powerful isn’t just the standardization, but importantly, it’s the context. The semantic layer doesn’t just link data; it understands the relationships between terms and concepts, enabling it to surface meaningful patterns and insights that might otherwise remain hidden.

By organizing this data into ontologies and knowledge graphs, the semantic layer helps uncover connections that accelerate critical workflows, like identifying new drug targets, spotting promising biomarkers, or generating better hypotheses faster.

From Standardization to Prediction 

The Evolution of Semantic Layers

As we’ve discussed, semantic layers help bring order to healthcare data, mapping EHR, claims, lab, and SDoH data into a unified structure so analysts and reporting tools can speak the same language.

We’re entering a new period of data architecture: one where semantic layers do more than just organize information. They’re becoming the engine behind AI-powered workflows that help predict what’s coming and guide smarter decisions before problems arise. Traditional models tell you what happened. Semantic layers, paired with machine learning, help you see what’s likely to happen.

What’s changing isn’t just the modeling, it’s the infrastructure. AI can’t function on siloed, inconsistent, hand-curated data. The modern semantic layer provides the foundation for machine learning at scale.

An AI-ready semantic layer includes many of the same inherent features of a semantic layer, like standardized terminology, reusable metric logic, and normalized data relationships, but it also includes: 

• Context-Aware Logic with rules that adjust based on clinical context (e.g., readmission window changes by discharge status)
•  ML Feature Readiness with metrics and conditions that are versioned, timestamped, and traceable.
• Cohort-Aware metrics with filters and breakdowns by payer, provider, ACO, or geography baked into the logic layer to enable quick training set generation or drill-down analysis.
• Metadata tagging about source system and lineage tracking including transformation logic, last update time, and data owner, allowing analysts to trust, trace, and audit every metric.
• Multi-modal intelligence to combine structured (claims, EHR) and unstructured data via semantic NLP.

Real-World Use Cases

Here are just a few examples of how an AI-ready semantic layer can help healthcare organizations.

• Suspected Condition Models

Train models to flag missing chronic conditions using harmonized diagnoses, labs, and SDoH. The semantic layer makes input features and labels consistent across years and systems.

• Volume Forecasting

Predict ambulatory migration or procedure volumes using structured timelines of encounters and attribution. Define site-of-care transitions using standardized location codes and provider types.

•  Cohort Scoring

Rank patients based on risk, utilization, cost, and gaps in care, using inputs defined once in the semantic layer. Analysts can run these scores in dashboards, feed them into outreach queues, or retrain models monthly.

• Identification of Drug Candidates and Optimization of Clinical Trial Design

Pharmaceutical researchers can seamlessly explore clinical trial outcomes, patient genetics, and published literature in a unified framework. , 

Why Healthcare Needs This 

The complexity of using multi-source healthcare data is especially challenging without an AI-ready semantic layer. Each project becomes a hand-built pipeline, with logic re-engineering required in every notebook or dashboard and definitions drifting across teams. With a semantic layer, metrics are aligned across analytics and AI and definitions are applied once and deployed everywhere. 

Wayfinder WorkSpace, built natively on Databricks, can provide a pre-configured semantic layer tailored for healthcare, including:

• Curated models for claims, EHR, SDoH, and other data sources
• Metric libraries ( leakage, referrals, etc.)
• Operates within your Databricks workspace

Wayfinder also scales with Delta Live Tables, Unity Catalog, and Feature Store, enabling the benefits of publishing standardized data ML reuse,  enabling cohort definitions for training/test sets with reproducible logic, embedding context-aware rules into model outputs, and working with Delta Live Tables and Unity Catalog for transparent, governed AI pipelines.

AI Doesn’t Replace the Semantic Layer—It Requires It

The future of healthcare analytics isn’t just descriptive, it’s predictive, prescriptive, and increasingly autonomous. AI in healthcare cannot scale without shared definitions, reusable metrics, and governed logic. The semantic layer is what gets us there. Kythera’s Wayfinder is not just a data platform, but a semantic AI platform that bridges operations, analytics, and strategy.

If you're still manually stitching together metric logic or battling inconsistent reports, it's time to rethink your architecture. Kythera has the tools to build a durable foundation for scalable, trustworthy, and fast healthcare intelligence.

If you want to learn more about how Kythera’s Wayfinder, built on Databricks, fast-tracks you to a modern architecture built for healthcare, get in touch.  www.kytheralabs.com