Integrating LLM using R Shiny for Clinical Data Review

Ensuring Data Privacy and Validity in AI-Powered Applications

Overview

Intermediate AI/LLM Shiny Data Privacy

The pharmaceutical industry is shifting from traditional SAS-based workflows toward the open-source R ecosystem. This workshop presents {DataChat}, an innovative R Shiny application that enables users to “chat with data” through a conversational interface while maintaining strict compliance with data privacy requirements and statistical validity standards.

What You’ll Learn

• 🛡️ Data privacy in LLM applications
• 💬 Conversational interfaces for clinical data
• 🔍 RAG (Retrieval-Augmented Generation) for pharma domain
• ✅ Statistical validity in AI-generated results
• 🎯 User-friendly design for non-programmers

Prerequisites

Required Knowledge:

• Intermediate R and Shiny
• Basic understanding of clinical trial data structures
• Familiarity with data privacy regulations (GDPR, HIPAA)

Technical Setup:

• R/RStudio with Shiny
• Access to sample clinical datasets

Key Packages & Tools

{ellmer}

{shinychat}

{ragnar}

{shiny}

Internal statistical tools

The Challenge

Traditional R Shiny applications for clinical data often require:

• 📚 Strong understanding of data structures (SDTM, ADaM)
• 🖱️ Familiarity with complex UI components (dropdowns, filters)
• 💻 Programming knowledge for data exploration

This creates barriers for clinical reviewers, physicians, and medical writers who need to access insights but lack technical expertise.

The Solution: {DataChat}

An AI-powered conversational interface that allows natural language interaction with clinical data while ensuring:

• 🔒 Data never leaves the secure environment
• ✅ Statistical calculations are validated
• 📊 Results are reproducible and auditable
• 👥 Accessible to non-technical users

Architecture Overview

┌─────────────────────────────────────────────────┐
│          User Interface (Shiny)                 │
│  "Show me adverse events for patients >65"      │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│       LLM Orchestration ({ellmer})              │
│  • Intent classification                        │
│  • Tool selection                               │
│  • Response generation                          │
└─────────────────┬───────────────────────────────┘
                  │
        ┌─────────┴─────────┐
        │                   │
┌───────▼──────┐   ┌────────▼────────┐
│ RAG System   │   │ Statistical     │
│ ({ragnar})   │   │ Tools           │
│              │   │ (validated)     │
│ • Document   │   │ • Summaries     │
│   retrieval  │   │ • Plots         │
│ • Context    │   │ • Models        │
└──────────────┘   └─────────────────┘

Key Features

1. Conversational Data Exploration

Natural language queries like:

• “What’s the average age of patients in the treatment arm?”
• “Show me serious adverse events by system organ class”
• “Compare baseline demographics between arms”

2. RAG for Domain Knowledge

{ragnar} provides retrieval-augmented generation capabilities:

library(ragnar)

# Create vector database from study documents
vector_db <- ragnar_db() %>%
  add_documents(
    path = "study_protocols/",
    chunk_size = 500
  )

# Query with context
context <- vector_db$search(
  query = user_question,
  top_k = 5
)

3. Privacy-Preserving Design

Critical Privacy Features:

• ✅ On-premise deployment - No data sent to external APIs
• ✅ Local LLMs supported - Can use llama.cpp or similar
• ✅ Query sanitization - Remove PII before processing
• ✅ Audit logging - Track all data access
• ✅ Role-based access - Control data visibility

4. Statistical Validity

Ensuring Accurate Results:

• All statistical calculations use validated R functions
• LLM suggests approach, validated code executes
• Results include confidence intervals and p-values
• Automatic flagging of statistical assumptions
• Human review required for critical decisions

Workshop Content

Module 1: Setting Up Secure LLM Integration

• Configuring {ellmer} for private deployments
• Local vs. cloud LLM considerations
• API security and authentication

Module 2: Building the Conversational Interface

Using {shinychat} for user interaction:

library(shinychat)
library(shiny)

ui <- fluidPage(
  chat_ui("clinical_chat")
)

server <- function(input, output, session) {
  chat <- chat_server("clinical_chat",
    system_prompt = "You are a clinical data assistant.
                     Only answer questions about the loaded study data.
                     Never make up information.",
    tools = list(
      summarize_demographics,
      plot_adverse_events,
      query_database
    )
  )
}

Module 3: Implementing RAG

Domain-specific context retrieval:

• Indexing study protocols and SAPs
• Medical terminology databases
• Previous study reports

Module 4: Privacy Controls

Practical Implementation:

# Anonymize data before LLM processing
sanitize_query <- function(query, data) {
  # Remove patient identifiers
  query <- remove_pii(query)
  
  # Check for sensitive fields
  if (contains_sensitive_terms(query)) {
    return(list(
      allowed = FALSE,
      message = "Query contains sensitive information"
    ))
  }
  
  # Log for audit
  log_query(query, user_id = session$user)
  
  return(list(allowed = TRUE, query = query))
}

Module 5: Validation Strategy

Ensuring Reliability:

1. Tool validation - Each statistical function tested independently
2. Response validation - LLM output checked against expected format
3. User verification - Results shown with source data
4. Expert review - Critical decisions flagged for human oversight

Use Cases in Pharma

1. Clinical Review Meetings

• Quick ad-hoc analyses during discussions
• Exploration of safety signals
• Subgroup identification

2. Medical Writing

• Extracting statistics for CSR
• Verifying data consistency
• Generating descriptive text

3. Safety Monitoring

• DSMB data reviews
• Adverse event trending
• Safety signal detection

4. Regulatory Queries

• Rapid response to agency questions
• Data subsetting and analysis
• Documentation generation

Privacy Compliance

GDPR Considerations

• ✅ Data minimization
• ✅ Purpose limitation
• ✅ Right to explanation (audit logs)
• ✅ Data encryption at rest and in transit

HIPAA Compliance

• ✅ Access controls
• ✅ Audit trails
• ✅ De-identification support
• ✅ Business associate agreements (if using cloud LLMs)

21 CFR Part 11

• ✅ Electronic signatures
• ✅ Audit trails
• ✅ System validation
• ✅ Controlled access

Validation Approach

IQ (Installation Qualification)

• Environment setup documentation
• Version control
• Access controls verification

OQ (Operational Qualification)

• Test each statistical tool independently
• Verify LLM response formatting
• Confirm privacy controls function

PQ (Performance Qualification)

• End-to-end testing with real scenarios
• User acceptance testing
• Performance benchmarking

Learning Outcomes

By the end of this workshop, you will be able to:

✅ Design privacy-preserving LLM applications
✅ Implement RAG for pharmaceutical domain knowledge
✅ Build conversational interfaces with {shinychat}
✅ Ensure statistical validity in AI-generated results
✅ Deploy compliant AI solutions in regulated environments
✅ Create user-friendly tools for non-technical stakeholders

Demo Application

Workshop includes hands-on work with {DataChat} demo:

• Sample CDISC SDTM/ADaM datasets
• Pre-configured LLM (local or API)
• Example queries and workflows
• Privacy controls demonstration

Best Practices

Do’s ✅

• Always validate statistical outputs
• Log all data access for audit
• Use validated tools for calculations
• Implement role-based access control
• Test privacy controls thoroughly

Don’ts ❌

• Never send raw clinical data to external APIs (unless approved)
• Don’t rely solely on LLM for critical decisions
• Avoid exposing PII in queries
• Don’t skip validation documentation
• Never deploy without proper testing

Future Directions

• Integration with electronic data capture (EDC) systems
• Multi-lingual support for global trials
• Advanced visualization capabilities
• Automated report generation
• Real-time safety monitoring

Additional Resources

• CDISC standards: cdisc.org
• FDA guidance on AI/ML: fda.gov
• Privacy regulations: GDPR, HIPAA guidelines

WarningImportant Note

This workshop demonstrates privacy-preserving approaches but should not be considered legal or regulatory advice. Always consult with your organization’s legal, compliance, and IT security teams before deploying AI applications with clinical data.

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Similar Workshops

• Getting Started with LLM APIs - LLM basics
• pointblank: Data Validation - Data quality for AI

Related Presentations

• Integrating LLM for Clinical Data Review - DataChat presentation

Next Steps

• For validation: See pointblank workshop
• Industry trends: AI Revolution analysis

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Last updated: November 2025 | R/Pharma 2025 Conference