BUILDING SMARTER LIFE SCIENCES LLMS: A SERIES

Best Practices
Written By J. Harte Nielson

Getting Started: A Strategic Guide to Real World Data Analysis for Life Sciences Leaders

AI & RWD Collision

HOW TO START

In an industry where speed-to-insight and data fidelity can make or break a therapy’s success, life sciences is fervently looking to improve the data they get and the value of the insights they can glean from it. From simple application-integrated BI tools, data warehouses, data lakes, data lakehouses, and even the dreaded data swamps, it seems as though we’ve tried about every avenue we could find. 

However, the newest promise, Large Language Models (LLMs), offers more than just promises of the next best thing. Integrated into the very nature of AI is the ability to go beyond collecting, analysing, and reporting on data; it now has the ability to UNDERSTAND the data, applying a game-changing ability to the very nature of how we analyze data.

No wonder so many leading firms are making large investments in Large Language Models (LLMs) to unlock the full potential of Real World Data (RWD). Designed to help leaders across pharma, from strategic brand managers to clinical operations to IT and beyond. Implementing LLMs at scale isn’t just about picking the right model—it’s about designing a solution that can evolve, integrate, and remain compliant in a high-stakes, highly regulated environment.

This five-part series, “Building Smarter with LLMs,” is designed for the vast number of pharma leaders across the value chain who believe AI has real potential if designed and applied effectively. For those hoping or aiming to operationalize it across their data value chain, each article provides a deep dive into a critical component of building enterprise-grade LLM solutions, enabling decision-makers to plan, build, and scale confidently.

Article 1: Critical Design and Architecture Decisions

If there is ever a time when it’s critical (dare I say optimal) to deprioritize timelines and instead focus on objectives, it is at the very start: The Blueprint. Far too many projects get derailed by continual revisions to the blueprint long after the project is underway. Continuous requests for new capabilities, ever-shifting project requirements, and architecture refinements are some of the common risks we deal with. This article focuses on those early-stage architectural decisions to be made that shape the long-term viability of your LLM implementation. Topics include:

  • Choosing between centralized, federated, and hybrid LLM deployment models

  • Designing modular architectures that support multiple RWD use cases (e.g., patient journey analysis, treatment response inference)

  • Selecting between open-source vs. proprietary models—balancing flexibility, transparency, and IP control

  • Infrastructure decisions: cloud-native vs. on-premise, GPU availability, container orchestration, and inference optimization

  • Embedding security, governance, and observability into the architectural fabric from day one

These choices set the tone for agility, scale, and compliance—so it’s critical to get them right.

Read our previous article on ways companies are leveraging LLMs for strategic decision making [ARTICLE]

 

Article 2: Essential Solution Components

While the many wonderful promises of AI seem to imply that a simple magic box is at the heart of it all, one of the many things that AI has been able to do is efficiently hide the complexities of the ecosystem necessary to fuel it behind usable and interactive interfaces (think ChatGPT). However, LLMs don’t operate in isolation; they’re part of a broader ecosystem of applications, components, and data. This article unpacks the essential and optional components needed to deliver a functional, reliable, and intelligent LLM-powered solution:

  • Prompt orchestration layers that enable dynamic querying and domain-specific intent resolution

  • Custom embeddings trained on life sciences corpora to improve understanding of clinical terms, medical ontologies, and unstructured notes

  • Retrieval-augmented generation (RAG) frameworks to enhance factuality using your enterprise RWD

  • Feedback loops and human-in-the-loop design to fine-tune performance and ensure clinical relevance

  • UI/API layers for model interaction—whether for medical reviewers, data scientists, or commercial teams

We’ll also cover the added complexities of evaluating component maturity, vendor compatibility, and opportunities for platformization across therapeutic areas.

 

Article 3: Data Access and Integration

The adage “Garbage In, Garbage Out” has never been more true when designing and implementing LLMs. The quest for reliable, fresh, and relevant data has been a perennial quest for this industry. With each new promise of being able to process more data, gain more insights, in less time the glaring question of whether the data being used is providign accurate answers looms large.

With AI, we are placing a much heavier workload on the applications analyzing the data and on the very quality and accuracy of the data itself. LLMs have already shown revolutionary abilities to identify patterns, draw conclusions, and make recommendations at unprecedented speed. However, inaccurate data just means you’re getting wrong answers much faster than before.

Without clean, well-connected data, even the most advanced models fail. This article tackles one of the thorniest challenges in life sciences AI: preparing RWD for LLM consumption.

  • Creating pipelines to ingest structured (claims, labs) and unstructured (EHR notes, imaging metadata, genomics) data

  • Harmonizing disparate coding systems (e.g., ICD-10, SNOMED CT, LOINC, RxNorm) through terminology mapping and normalization

  • Using LLMs themselves for semantic matching and entity resolution across patient records

  • Building metadata layers that improve searchability, traceability, and regulatory audit-readiness

  • Leveraging synthetic data generation and de-identification techniques for development and testing without PHI risk

Done right, your data integration approach becomes a force multiplier—not a bottleneck.

 

Article 4: Solution Training and Management

Model performance and effectiveness is never “set it and forget it.” The very complexity of Real World Data, the ever-changing sources, even the continual refinement in how we store data, demands that applications be agile, flexible, and refundable to ensure continuously accurate insights. This article explores how to train, fine-tune, and manage LLMs to keep them fresh, up-to-date, and innovative:

  • Strategies for domain adaptation—pretraining, continued training, and instruction tuning on biomedical data

  • Evaluation metrics for clinical accuracy, scientific relevance, and response consistency

  • Human-in-the-loop feedback for correcting hallucinations and reinforcing domain-specific responses

  • Model monitoring and drift detection—staying ahead of changes in data distributions and medical terminology

  • Operational tooling for checkpoint management, A/B testing, and rollback protocols

We’ll also examine how to align model lifecycle management with internal MLOps practices and regulatory documentation standards.

 

Article 5: Ensuring Compliance and Data Privacy

Trust is the core currency of innovation in healthcare. The very critical and sensitive nature of the data we leverage demands robust and thorough data protections. This often means that every new technology and solution inevitably runs head-first into a wealth of compliance, laws, and regulations that can delay and even derail ambitious projects.

This final article zeroes in on keeping your LLM solutions compliant with regulatory frameworks and ethical standards:

  • Designing workflows that protect PHI/PII using encryption, masking, and zero-retention architectures

  • Implementing differential privacy, secure enclaves, and federated learning where applicable

  • Ensuring traceability and auditability—versioning prompts, tracking outputs, and logging usage

  • Building transparency mechanisms for explainability, especially in patient-facing or regulatory contexts

  • Navigating HIPAA, GDPR, 21 CFR Part 11, and international variations in data handling laws

We also provide checklists and risk assessment tools to help ensure your LLM solution is defensible, auditable, and trusted by stakeholders.

WHY IT ALL MATTERS

Life sciences companies that successfully harness LLMs will unlock game-changing capabilities—from accelerating real-world evidence generation to discovering new biomarkers and improving trial design. But these capabilities hinge on more than model selection. Success requires a full-stack, end-to-end strategy that aligns technical architecture with business goals and regulatory realities.

Let this series be your strategic blueprint. And when you’re ready to bring that blueprint to life, the experts at Ario Health are here to help. We specialize in designing and implementing secure, scalable, and scientifically grounded LLM solutions that turn your real world data into real world results.

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