BUILDING SMARTER LIFE SCIENCES LLMs: Critical Design and Architecture Decisions

Best Practices
Written By J. Harte Nielson

In the race to turn Real World Data (RWD) into actionable insights, Large Language Models (LLMs) offer a quantum leap forward. But just like drug discovery, success starts with the very first processes in early development to create a robust solution. For LLMs to deliver tangible value—whether for accelerating evidence generation, powering decentralized trials, or streamlining regulatory submissions—they need more than great algorithms.


It begins with great architecture!

This first article in our five-part "Building Smarter with LLMs" series explores the critical architecture and design decisions life sciences IT and data leaders must make to future-proof their investments and move from LLM experimentation to enterprise-wide adoption.

1. Choosing the Right Deployment Architecture

Your first decision is where the intelligence will live. This affects everything—data access, latency, privacy, and cost. This is by far not a comprehensive guide to the architectural solutions that can be created, designed, and implemented. However, this covers the key architectural options to evaluate and consider. The correct answers, as usual, depend on numerous variables that all must be considered. Hopefully, this will simplify the types and nature of architectures out there.

Centralized Architecture

All model inference and training are done within a secure central environment, typically in a private cloud or on-premise. This architecture is a perfect fit when the existing data is already harmonized (e.g., a mature data lake) and regulatory control is strong.

“A global pharma company with a large internal clinical database hosted on AWS uses a centralized GPT-based system to automate adverse event (AE) coding across trials.”

Federated Architecture

Models are deployed at the data source—each hospital, site, or country—and learn without centralizing sensitive data. This design is a great solution to employ when concerned with preserving privacy across borders and institutional firewalls.

“ A collaboration between pharma, academic hospitals, and international registries uses federated learning to train models on oncology EHR data without sharing patient records across entities.”

Hybrid Architecture

Combine the above: sensitive computations remain local (e.g., genomic data or PHI), while non-sensitive operations run centrally for scale and efficiency.

“A biopharma company processes unstructured clinical notes on-site (due to PHI) and pipes summary embeddings into a centralized LLM used for cohort discovery.”
 
Star Bullet
PRO TIP
For multinational studies, a hybrid model offers flexibility and helps navigate regional privacy laws like GDPR, LGPD, and HIPAA.
 

2. Modular System Design

Think beyond the simple model. You’re not just building a language model. You’re building an integrated solution. Effective solutions necessarily stitch together multiple components into a harmonious ecosystem.

As with all technical solutions, nothing stays static for long. Upgrades, revisions, and refinements are commonplace and should be anticipated from the start. Utilizing a modular design will help you scale and adapt across changing requirements, use cases, and functions.

Model Layer

This is the core model or models running inference. Options include fine-tuned open-source models (e.g., BioGPT, ClinicalBERT), third-party APIs (e.g., GPT-4), or custom-built domain-specific models. A smart accelerator is to choose models that are tailored or matched to the linguistic and regulatory complexity of your use case.

“A rare disease team uses a BERT-based model fine-tuned on patient forum data and clinical trial documents to understand disease progression nuances that aren’t captured in structured EHRs.”

Prompt Management Layer

Centralized prompt storage, management, and testing are essential for controlling LLM behavior in production. Create modular prompt templates with variables, metadata (e.g., use case, audience, regulatory status), and version history. Integrate prompt testing into QA pipelines.

“A regulatory affairs group uses validated prompts for summarizing FDA guidance documents. Prompts are version-controlled to meet documentation standards.”

RAG (Retrieval-Augmented Generation) Layer

RAG is a unique capability that combines LLMs with a retriever that queries an external document store (like a vector database). This ensures that LLM responses are grounded in enterprise-specific content and can include critical references and citations.

“A medical affairs team uses RAG to generate accurate answers to HCP queries by referencing both internal publications and external PubMed entries.”

Data Interface Layer

Commonly, we need to bring complex sets of data and data types together.  Interfaces with structured (e.g., OMOP, FHIR) and unstructured (e.g., scanned notes, discharge summaries) sources. Normalize and enrich inputs with terminologies like SNOMED, RxNorm, and LOINC.

“A real-world evidence platform uses this layer to extract, standardize, and enrich oncology data across three EHR systems before generating patient-level insights.”

Governance and Feedback Layer

This layer handles human review, feedback collection, prompt scoring, model corrections, and approval workflows. It supports active learning, flagging of hallucinations, and automatic routing of exceptions.

“A pharmacovigilance LLM routes uncertain case narratives to safety scientists via this layer, incorporating their feedback into future model updates.”
 
Star Bullet
PRO TIP
Use a plug-and-play approach so each layer can evolve independently. This is critical for supporting multi-model strategies and facilitating compliance audits.
 

3. Infrastructure Strategy:

Cloud, On-Prem, or Hybrid?

LLM workloads are compute-hungry, which is an unavoidable fact and reality of AI. Therefore, the infrastructure you choose is critical to ensure it can keep up with the compute demands, scale efficiently, while balancing impacts and operational costs.

On-Premise GPU Clusters

Offers full control over hardware, data residency, and system performance. This approach fits well with companies with strict data privacy requirements or proprietary data workflows where there is concern with the data leaving local environments.

“A genomics firm builds an on-prem HPC cluster to fine-tune LLMs on proprietary sequences and EHR-linked biomarkers—avoiding cloud data exposure.”

Benefits:

  • Data never leaves the enterprise boundary

  • Greater control over tuning environments

  • Predictable cost structure for high-volume training

Challenges:

  • High up-front CapEx for GPU servers and storage

  • Requires specialized IT staff for maintenance

  • Limited elasticity for scaling during peak demand

Cloud-Based (IaaS or SaaS)

Flexible and elastic. Services like Azure OpenAI, AWS Bedrock, or Google Cloud’s Vertex AI make model deployment straightforward. They can often be the best place to start with innovation teams experimenting with multiple LLMs, rapid prototyping, or companies with limited internal infrastructure.

“A biotech startup rapidly prototypes drug repurposing insights using cloud-based LLMs integrated with their internal clinical trial registry.”

Benefits:

  • Instant access to high-performance compute (e.g., NVIDIA A100, H100 GPUs)

  • Easy integration with modern ML services (e.g., SageMaker, Azure ML, Vertex AI)

  • Built-in security and compliance features from cloud providers

Challenges:

  • Ongoing OpEx can scale quickly without controls

  • Vendor lock-in and API abstraction may reduce transparency

  • Regulatory scrutiny over cloud-hosted PHI

Hybrid Strategy

Leverage secure, local clusters to deploy training. This allows you to deploy in numerous lightweight models, including containerized APIs in cloud environments for downstream use in dashboards, reports, and apps.

This model can combine the benefits of both worlds. It allows you to train or fine-tune models on-prem where privacy is critical, but also allows you to efficiently push and deliver non-sensitive workloads and data to the cloud.

“A life sciences company uses Kubernetes and Ray to orchestrate LLM training on-premise, but serves prompt results via Streamlit apps in a managed cloud workspace.”

Benefits:

  • Flexibility to meet cross-border privacy laws

  • Scalable infrastructure for diverse workloads

  • Easier disaster recovery and redundancy

Challenges:

  • Increased architectural complexity

  • Requires careful coordination between systems

 
Star Bullet
PRO TIP
Develop a reference architecture that abstracts compute decisions. Use model quantization, distributed training strategies, and containerization (e.g., Docker, NVIDIA Triton) to make infrastructure portable.
 

4. Design for Security, Compliance, and Traceability

In life sciences, trust is not a luxury—it's a regulatory necessity. LLM solutions must be designed to handle sensitive data responsibly, prove compliance under scrutiny, and maintain transparency for internal and external audits.

Prompt & Output Logging

The audit log is the first line of defense. Your solutions should be maintaining structured logs for every LLM interaction, including user ID, prompt metadata, output, time stamp, and model version. Logs should be immutable, encrypted, and accessible for auditing.

“During an audit, a regulatory affairs team retrieves specific prompt-response pairs used to generate a clinical summary submitted to the FDA—ensuring end-to-end traceability.”

PHI Redaction and Zero Retention

Unavoidably, we are always working with highly sensitive information that demands we design with protections in place. Privacy techniques like deploying named entity recognition (NER) and automated masking tools to redact PII/PHI in both structured and unstructured inputs are very valuable. Other tools, like applying policies that prevent LLMs from storing or remembering PHI across sessions, can provide alternative/additional levels of protection.

“A clinical trial LLM receives anonymized EHR notes preprocessed with PHI redaction before summarization and inference.”

Access Controls and Segmentation

Ensuring that the appropriate levels of access are in place is the most basic of security procedures to initiate. Enforcement of role-based access and endpoint-level permissions to restrict data exposure is critical. Creating environments where prompts with PHI can only be processed individually, such as in isolated and secure nodes, is a fundamental requirement for access control.

“A medical reviewer receives full-text with citations, while commercial users see only high-level summaries from the same system—customized by access level.”

Explainability and Attribution

For critical, high-stakes decisions, models must “show their work”. Consider adding LLM-generated rationale summaries, citation traces, or saliency explanations for regulatory submissions. Equipping LLM responses with metadata tags, source links, or rationale generation to support scientific and regulatory review can significantly increase the quality of the output.

“For a pharmacovigilance LLM used in ICSRs (Individual Case Safety Reports), every output includes linked source excerpts and version stamps.”

To meet standards like HIPAA, GDPR, and 21 CFR Part 11, security and compliance must be embedded in the design phase—not bolted on later. Threat modeling, privacy impact assessments (PIAs), and automated monitoring should be part of your standard development lifecycle.

 
Star Bullet
PRO TIP
Establish a cross-functional review board (IT, legal, regulatory, clinical) to oversee AI model deployment and ensure your LLM environment meets both ethical and regulatory expectations.
 

5. Align Technical Choices to Business Priorities

The best technical design is one that serves a clear business goal. In life sciences, that means aligning LLM architecture with the functional needs of your clinical, regulatory, medical, and commercial teams—each of whom have unique constraints and priorities.

Business-Driven Design Questions to Ask:

  • What user types will interact with the model?

  • What decisions will be made based on LLM outputs?

  • What accuracy, latency, and compliance thresholds are acceptable?

  • How will success be measured—and how can architecture support those KPIs

Clinical R&D and Scientific Discovery:

Focus on model flexibility, query depth, and iteration speed. Enable exploratory research using both structured and unstructured data. Support semantic search across multi-omics datasets, trial protocols, and literature.

Commercial Operations and Market Access:

Prioritize fast, structured summarization and integration with tools like Veeva, Salesforce, and CRM systems. Outputs should be concise, audience-specific, and consistent with approved messaging.

Medical Affairs and Real-World Evidence:

Emphasize contextual understanding, scientific accuracy, and the ability to handle long-form unstructured content. Outputs must be defensible in field discussions or publications.

Executive Strategy and Portfolio Planning:

Provide dashboards and briefings powered by LLM-summarized insights from cross-domain data. Enable rapid situational awareness and hypothesis generation at scale.

 
Star Bullet
PRO TIP
Map every LLM capability to a tangible business outcome—like faster study start-up, reduced review cycles, or increased MSL engagement—and ensure your system design enables that linkage.
 

Wrapping Up: Your Architecture Is Your Edge

Any LLM solution is only as good as the architecture it runs on. Remember, your architecture is the core foundation that will drive most future decisions about how you use, operate, and expand for years to come. Focusing on simple solutions or quick solutions can sometimes be time and cost-effective. Alternatively, some solutions demand a bigger investment in designing and architecting larger, more complex architectures. Determining that approach is most effectively achieved when the business demands are prioritized to guide the decision-making and selection process.

The right foundation allows your organization to:

  • Adapt faster to new data and regulatory needs

  • Serve multiple stakeholders with tailored outputs

  • Reduce cost and complexity over time

  • And most importantly, unlock the full value of RWD


🚀 Ready to architect smarter?

Let Ario Health help you design and deploy scalable, secure, and life sciences-ready LLM infrastructure.

STAY TUNED:

Now that the foundation is set, it’s time to build upward. In Article 2: Essential Solution Components, we’ll dive into the key solution components that bring your LLM architecture to life—everything from prompt orchestration, data retrieval to biomedical embeddings and human-in-the-loop feedback systems.

If architecture is the blueprint, these components are the organs and muscles—the functional layers that turn static design into dynamic intelligence. You’ll learn how to create flexible, interoperable systems that support both experimentation and enterprise-grade delivery.

📖 Stay tuned. The building blocks of your LLM success story are just ahead.

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