BUILDING LIFE SCIENCES LLMs: Data Access and Integration

Best Practices
Written By J. Harte Nielson
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Turning Raw Real-World Data into Fuel for Intelligent LLMs

If architecture is the foundation and components are the engine, then data is the fuel that powers your LLM solution. And in life sciences, that fuel comes from an extraordinarily complex and fragmented ecosystem of real-world data (RWD).

LLMs have the potential to unlock deep insights from unstructured sources like clinical notes, patient-reported outcomes, safety narratives, and even imaging reports. But before these models can reason, summarize, and generate value—they need clean, consistent, and contextually rich data to work with.

In Article 3 of our Building Life Sciences LLMs series, we explore how to make that happen.

Why Data Integration is the Linchpin of LLM Success

For life sciences companies, data isn’t just big—it’s messy, distributed, and multi-modal. One study may store data in OMOP; another uses raw HL7 messages. Some labs export PDFs; others push FHIR bundles. And let’s not forget handwritten discharge notes, scanned trial protocols, and image-based pathology reports.

These heterogeneous formats, data standards, and terminologies make RWD both highly valuable and exceptionally difficult to work with. LLMs are incredibly flexible—but they’re not magical. Without unified, accessible, and semantically enriched data, even the best model will hallucinate, fail to retrieve context, or produce misleading outputs that could lead to regulatory or clinical missteps.

Key Business Values:

  • Dramatically reduce the time to insight for safety, RWE, and regulatory functions

  • Minimize risk of hallucinations and inaccuracies by grounding models in source-aligned content

  • Enable true reusability of prompts and LLM workflows across datasets and functions

  • Improve auditability and reproducibility of AI-generated outputs

1. CONNECTING SILOED SYSTEMS

 

The first step in any LLM data pipeline is to connect all relevant data sources—regardless of format or origin. Integration must accommodate modern APIs, legacy exports, and even unstructured archives.

Key integration targets include:

  • Clinical data lakes and warehouses (e.g., OMOP, i2b2, Snowflake)

  • EHR systems (via HL7/FHIR connectors or direct database access)

  • Safety databases (e.g., Argus, ARISg, custom pharmacovigilance tools)

  • Scientific literature and external datasets (e.g., PubMed, ClinicalTrials.gov, FDA SPLs)

  • Legacy documents and scanned forms (often stored in SharePoint, PDF archives, or image repositories)

Modern integration methods include event-driven ingestion, RESTful APIs, batch ETL tools, and microservice pipelines to automate sync across systems.

“A global pharma company integrates four RWD sources—including safety narratives, claims data, and HCP call notes—into a unified pipeline that feeds its LLM-based safety signal detection tool. Using Kafka and S3-based data lakes, they reduce ingestion delays from days to minutes.”
 
PRO TIP
Use event-driven architectures (e.g., Kafka, FHIR Subscriptions) to keep LLM inputs up to date in real time. This reduces data latency and improves context freshness in clinical decision-making workflows.
 

2. NORMALIZATION AND SEMANTIC ALIGNMENT

 

Once connected, data must be cleaned, deduplicated, and semantically harmonized. Without normalization, LLMs must interpret inconsistent codes, abbreviations, and free-text variations, which increases hallucination risk and reduces reusability.

Key normalization steps include:

  • Code mapping (e.g., ICD-10 to SNOMED CT, NDC to RxNorm)

  • Synonym harmonization (e.g., "heart attack" = "myocardial infarction")

  • Date/time and unit conversions (e.g., mg → mcg, metric to imperial)

  • Demographic standardization (e.g., race/ethnicity codes, gender fields)

  • OCR correction for scanned data and hand-typed notes

This semantic alignment allows consistent querying, prompt reuse, and embedded compliance controls.

“A rare disease insights team builds a semantic layer that auto-translates over 700 clinical terms from 15 hospital partners into a harmonized ontology, enabling consistent LLM summarization and patient stratification across studies.”
 
PRO TIP
Build and maintain a terminology layer that sits between your raw data and prompts. LLMs perform better with clean, context-consistent inputs and structured tokens.
 

3. STRUCTURING THE UNSTRUCTURED

 

The majority of RWD is unstructured—meaning it doesn’t live in neatly labeled tables. Yet unstructured data often contains the richest context: disease progression, patient concerns, clinician observations, and treatment rationale.

LLMs excel at understanding language, but structured preparation improves precision and reduces model uncertainty. Techniques include:

  • Optical Character Recognition (OCR) to digitize scanned documents

  • Natural Language Processing (NLP) to parse sentence structure and medical terms

  • Segmentation to break documents into clinical sections for focused prompting

  • Entity Recognition to tag key elements (conditions, drugs, events)

  • Embedding preparation to create chunked, searchable contexts for RAG workflows

“A clinical development team uses OCR and NLP to extract relevant trial eligibility criteria from scanned protocols, which are then summarized and queried by a prompt-tuned LLM—improving site matching and speeding recruitment.”
 
PRO TIP
Use medical-specific NLP models (e.g., MedSpaCy, BioBERT) to pre-process and annotate before passing to your general or fine-tuned LLM. This adds domain context and reduces ambiguity.
 

4. ESTABLISHING DATA PROVENANCE AND TRACEABILITY

 

In life sciences, data lineage is essential for regulatory confidence and reproducibility. When LLMs generate insights, stakeholders need to know: What was the source? How was the data transformed? Can this be repeated?

Best practices for traceability include:

  • Assigning persistent identifiers (e.g., UUIDs, URNs) to every patient, record, and document section

  • Logging all transformations—redactions, mappings, standardizations—with timestamps and version control

  • Associating outputs with citation metadata (e.g., source document, paragraph ID, model version)

  • Retaining full lineage from raw input → transformation pipeline → prompt input → model output

“A medical affairs platform logs each LLM-generated response to an HCP question with linked document excerpts, timestamps, and semantic transformation records. This accelerates medical review cycles and improves defensibility.”
 
PRO TIP
Build data lineage into your monitoring and QA dashboards. Reviewers and auditors should be able to click any LLM output and trace it to its exact source.
 

5. AUTOMATING SECURE, ROLE-BASED DATA PIPELINES

 

Modern LLM systems require dynamic, continuously updated pipelines—but also need rigorous access controls. Not all users should see all data—or all outputs.

Your pipelines must:

  • Support RBAC across teams and geographies

  • Enforce jurisdictional filters (e.g., redact PHI for EU users)

  • Implement masking and redaction at the field, document, or user level

  • Automate logging and error handling across ingestion and transformation stages

  • Use pipeline orchestrators (e.g., Airflow, Prefect, Dagster) to manage jobs, retries, and dependencies

“A commercial analytics team accesses masked LLM summaries of physician engagement notes, while the safety team sees full-text, fully attributed event narratives—with both flows orchestrated by a unified, secure DAG.”
 
PRO TIP
Treat your pipelines as production software. Implement CI/CD for data transformation code and monitor performance across steps.
 

FINAL THOUGHTS:

Data is the Input—and the Advantage

Building a smart LLM solution starts with building a smart data foundation. Data integration is not a back-office task—it’s a strategic enabler that determines everything from insight quality to regulatory defensibility.

By focusing on interoperability, standardization, and contextual integrity, life sciences leaders can unleash the full potential of LLMs while staying compliant, traceable, and enterprise-ready.

Next up: Solution Training and Management—we’ll break down how to manage model lifecycles, version prompts, retrain workflows, and monitor performance across teams and time.

Need help getting your data LLM-ready?


Ario Health works with life sciences organizations to build modern data pipelines that power safe, secure, and scalable AI systems.

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Coming Soon: Article 4 – Solution Training and Management

Models don’t stand still. In Article 4, we’ll explore how to handle prompt drift, retraining strategies, governance cycles, and continuous improvement for life sciences-grade LLMs.

📖 Stay tuned. Intelligence is a moving target.

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J. Harte Nielson
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BUILDING LIFE SCIENCES LLMs: Essential Solution Components