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Drug Trial Analysis System Architecture šŸ—ļø

From 10 to 10,000 trials/month with HIPAA + FDA compliance

August 28, 2025
19 min read
āš•ļø Healthcare TechšŸ—ļø ArchitecturešŸ¤– Multi-AgentšŸ”’ HIPAAšŸ“Š ML Pipelines
šŸŽÆThis Week's Journey

From prompts to production-grade clinical trial analysis.

Monday: 3 core prompts for trial analysis. Tuesday: automated extraction code. Wednesday: team workflows across research, compliance, and data teams. Thursday: complete technical architecture with multi-agent orchestration, ML pipelines, HIPAA compliance, and scaling from 10 to 10,000 trials per month.

šŸ“‹

Key Assumptions

1
Process 10-10,000 clinical trials per month with 500-5000 pages each
2
HIPAA compliance required for PHI, FDA 21 CFR Part 11 for audit trails
3
Multi-tenant SaaS with pharma/biotech customers (enterprise isolation)
4
Real-time analysis (<5 min) for urgent safety reports, batch for bulk
5
Integrate with EDC systems (Medidata Rave, Oracle Clinical), EHRs, CTMS

System Requirements

Functional

  • Ingest trial protocols, case reports, lab results, adverse events (PDF, XML, HL7 FHIR)
  • Extract structured data: endpoints, demographics, outcomes, safety signals
  • Analyze efficacy (primary/secondary endpoints), safety (AE classification), compliance (protocol deviations)
  • Generate regulatory reports (CSR sections, safety narratives, statistical summaries)
  • Detect data quality issues, missing fields, outliers, protocol violations
  • Support multi-study meta-analysis and comparative effectiveness research
  • Provide audit trail for all data transformations and AI decisions

Non-Functional (SLOs)

latency p95 ms300000
freshness min60
availability percent99.9
accuracy percent99.5
audit retention years25

šŸ’° Cost Targets: {"per_trial_usd":15,"per_page_usd":0.05,"compute_percent_of_revenue":20}

Agent Layer

planner

L4

Decompose trial analysis into subtasks, select tools, route to specialized agents

šŸ”§ TaskDecomposer, AgentRouter, PriorityQueue

āš” Recovery: If task decomposition fails ā†’ fallback to template-based plan, If agent unavailable ā†’ queue for retry with backoff, If priority conflict ā†’ escalate to human reviewer

executor

L3

Execute trial analysis: extract data, compute endpoints, generate insights

šŸ”§ DocumentParser, LLM (Claude/GPT), StatisticalAnalyzer, SafetySignalDetector, ComplianceChecker

āš” Recovery: If extraction fails ā†’ retry with different LLM, If low confidence (<0.7) ā†’ flag for human review, If statistical error ā†’ fallback to manual calculation, If timeout ā†’ save partial results, resume later

evaluator

L2

Validate output quality, check completeness, detect anomalies

šŸ”§ SchemaValidator, AnomalyDetector, CrossReferencer (vs ClinicalTrials.gov), StatisticalValidator

āš” Recovery: If validation fails ā†’ return gaps to Executor for re-extraction, If anomaly detected ā†’ flag for expert review, continue processing, If schema mismatch ā†’ attempt auto-mapping, fallback to manual

guardrail

L1

Enforce HIPAA compliance, redact PHI, check safety policies, audit all actions

šŸ”§ PHIDetector (AWS Comprehend Medical), RedactionEngine, PolicyChecker, AuditLogger, ConsentValidator

āš” Recovery: If PHI detected ā†’ block processing until redacted, If policy violation ā†’ halt workflow, notify compliance team, If audit log fails ā†’ retry 3x, escalate to ops, Never proceed without guardrail approval

meta_analysis

L3

Aggregate results across multiple trials, perform comparative effectiveness analysis

šŸ”§ MetaAnalysisEngine, ForestPlotGenerator, HeterogeneityCalculator, SubgroupAnalyzer

āš” Recovery: If insufficient trials ā†’ return error, suggest minimum N, If high heterogeneity ā†’ flag for sensitivity analysis, If statistical assumptions violated ā†’ use robust methods

report

L2

Generate regulatory reports (CSR sections, safety narratives, statistical summaries)

šŸ”§ TemplateEngine, NarrativeGenerator (LLM), TableFormatter, PDFGenerator

āš” Recovery: If template error ā†’ fallback to default ICH E3 structure, If narrative generation fails ā†’ use rule-based templates, If formatting error ā†’ export raw data for manual formatting

ML Layer

Feature Store

Update: Daily batch + real-time for urgent safety signals

  • ā€¢ trial_phase_encoded
  • ā€¢ subject_demographics_vector
  • ā€¢ adverse_event_frequency
  • ā€¢ endpoint_effect_size
  • ā€¢ protocol_complexity_score
  • ā€¢ site_performance_metrics
  • ā€¢ drug_mechanism_embedding

Model Registry

Strategy: Semantic versioning (major.minor.patch) + Git SHA

  • ā€¢ TrialExtractionLLM
  • ā€¢ SafetyClassifier
  • ā€¢ EndpointPredictor
  • ā€¢ ComplianceDetector

Observability Stack

Real-time monitoring, tracing & alerting

0 active
SOURCES
Apps, Services, Infra
COLLECTION
11 Metrics
PROCESSING
Aggregate & Transform
DASHBOARDS
5 Views
ALERTS
Enabled
šŸ“ŠMetrics(11)
šŸ“Logs(Structured)
šŸ”—Traces(Distributed)
trial_processing_time_p95_ms
āœ“
extraction_accuracy_percent
āœ“
llm_token_count_per_trial
āœ“
llm_cost_per_trial_usd
āœ“
guardrail_block_rate
āœ“
evaluator_quality_score
āœ“

Deployment Variants

šŸš€

Startup Architecture

Fast to deploy, cost-efficient, scales to 100 competitors

Infrastructure

āœ“
AWS Lambda (serverless compute)
āœ“
API Gateway (managed API)
āœ“
RDS PostgreSQL (single-AZ)
āœ“
S3 (document storage)
āœ“
Claude API (external LLM)
āœ“
CloudWatch (logs + metrics)
āœ“
Secrets Manager (credentials)
ā†’Fast to deploy (1-2 weeks)
ā†’Low ops overhead (fully managed)
ā†’Cost: $500-2K/mo for 10-500 trials
ā†’Single-tenant (shared infra)
ā†’Basic HIPAA compliance (BAA with AWS)
ā†’Manual scaling (adjust Lambda concurrency)

Risks & Mitigations

āš ļø LLM hallucination leads to false trial results in regulatory submission

Medium (0.2% rate)

āœ“ Mitigation: 4-layer validation (confidence scoring, cross-reference, statistical plausibility, human review). 100% catch rate. Never auto-submit without expert approval.

āš ļø PHI leakage due to incomplete redaction

Low (guardrail agent enforces 100% scan)

āœ“ Mitigation: Guardrail agent blocks all processing until PHI scan completes. Dual-layer: AWS Comprehend Medical + custom NER. Quarterly audits by compliance team.

āš ļø Multi-tenant data isolation failure (customer A sees customer B's trials)

Low (VPC isolation + RBAC)

āœ“ Mitigation: Network-level isolation (VPC per tenant). Database row-level security. API-level tenant ID validation. Penetration testing quarterly.

āš ļø Model drift degrades accuracy over time (new drug classes, trial designs)

High (pharma evolves rapidly)

āœ“ Mitigation: Quarterly model retraining. Real-time drift detection (PSI > 0.25 ā†’ alert). Shadow mode testing before deployment. Rollback policy if accuracy drops >5%.

āš ļø Cost overrun due to LLM API usage (10K trials/mo Ɨ $15/trial = $150K/mo)

Medium (usage spikes unpredictable)

āœ“ Mitigation: Per-tenant rate limits. Cost alerts at 80% budget. Prompt optimization (reduce tokens 30%). Cache repeated queries. Negotiate volume discounts with LLM providers.

āš ļø Vendor lock-in (AWS, Anthropic) limits flexibility

Medium (deep integration)

āœ“ Mitigation: Abstract LLM calls behind interface (swap providers without code changes). Use open standards (FHIR, HL7). Terraform for IaC (multi-cloud ready). Quarterly vendor review.

āš ļø Regulatory changes (FDA updates 21 CFR Part 11, HIPAA updates)

Low (changes infrequent but high-impact)

āœ“ Mitigation: Dedicated compliance officer. Quarterly regulatory reviews. Modular architecture (isolate compliance logic). Compliance-as-code (automated policy checks).

šŸ§¬

Evolution Roadmap

Progressive transformation from MVP to scale

šŸŒ±
Phase 1Months 0-3

Phase 1: MVP (0-3 months)

1
Launch serverless architecture (Lambda + RDS)
2
Support 10-50 trials/month
3
Basic extraction (3 agents: Executor, Evaluator, Guardrail)
4
HIPAA compliance (BAA with AWS)
Complexity Level
ā–¼
šŸŒæ
Phase 2Months 3-6

Phase 2: Scale (3-6 months)

1
Migrate to EKS (Kubernetes)
2
Add Planner + MetaAnalysis + Report agents
3
Support 500 trials/month
4
Multi-LLM ensemble (Claude + GPT + Gemini)
5
Advanced ML (RAG, fine-tuning, evaluation loop)
Complexity Level
ā–¼
šŸŒ³
Phase 3Months 6-12

Phase 3: Enterprise (6-12 months)

1
Multi-tenant with VPC isolation
2
Support 5K+ trials/month
3
Private LLM endpoints (SageMaker or self-hosted)
4
Multi-region deployment (US + EU)
5
FDA 21 CFR Part 11 validation
Complexity Level
šŸš€Production Ready
šŸ—ļø

Complete Systems Architecture

9-layer architecture from ingestion to compliance

1
šŸŒ

Presentation

3 components

Research Portal (React)
Compliance Dashboard (Next.js)
API Docs (OpenAPI)
2
āš™ļø

API Gateway

4 components

Kong/Apigee
Rate Limiter (per-tenant)
Auth (OIDC/SAML)
Request Router
3
šŸ’¾

Agent Layer

6 components

PlannerAgent (task decomposition)
ExecutorAgent (trial analysis)
EvaluatorAgent (quality checks)
GuardrailAgent (HIPAA/safety)
MetaAnalysisAgent (cross-study)
ReportAgent (CSR generation)
4
šŸ”Œ

ML Layer

5 components

Feature Store (trial metrics)
Model Registry (LLMs, classifiers)
Offline Training (batch)
Online Inference (real-time)
Evaluation Loop (drift, quality)
5
šŸ“Š

Integration

5 components

EDC Adapter (Medidata, Oracle)
EHR Adapter (Epic, Cerner)
CTMS Connector
PHI Handler (Comprehend Medical)
FHIR Mapper
6
šŸŒ

Data

4 components

PostgreSQL (structured trial data)
S3 (raw documents, audit logs)
Pinecone (vector embeddings)
Redis (cache, queue)
7
āš™ļø

External

4 components

Claude/GPT APIs
AWS Comprehend Medical
FDA FAERS API
ClinicalTrials.gov API
8
šŸ’¾

Observability

5 components

CloudWatch/Datadog (metrics)
ELK Stack (logs)
Jaeger (traces)
Grafana (dashboards)
Eval DB (quality metrics)
9
šŸ”Œ

Security

5 components

KMS (encryption)
WAF (DDoS protection)
IAM (RBAC)
Audit Logger (25yr retention)
VPC (network isolation)
šŸ”„

Request Flow - Trial Analysis

Automated data flow every hour

Step 0 of 11
ResearcherAPI GatewayPlannerAgentExecutorAgentGuardrailAgentEvaluatorAgentML LayerData StorePOST /trials/{id}/analyzeRoute request, check authDecompose: extract ā†’ analyze ā†’ reportCheck PHI, validate inputsPHI redacted, inputs validExtract endpoints, demographics, outcomesStructured JSON (500 fields)Validate completeness, accuracyQuality score: 98.5%, gaps: 3 fieldsSave results + audit log200 OK + analysis report

Data Flow - Trial Analysis Pipeline

1
Researcher0s
Uploads protocol PDF + case reports ā†’ Raw documents
2
API Gateway50ms
Authenticates, rate limits, routes ā†’ Request metadata
3
PlannerAgent200ms
Decomposes into: extract ā†’ validate ā†’ analyze ā†’ report ā†’ Task DAG
4
GuardrailAgent10s
Scans for PHI, redacts sensitive data ā†’ Redacted documents
5
ExecutorAgent2-3min
Extracts 500+ fields (endpoints, demographics, AEs) ā†’ Structured JSON
6
EvaluatorAgent30s
Validates completeness, checks anomalies ā†’ Quality score + gaps
7
ExecutorAgent1min
Re-extracts missing fields (if gaps exist) ā†’ Updated JSON
8
EvaluatorAgent20s
Final validation (99.5% target accuracy) ā†’ Approved results
9
ReportAgent1min
Generates CSR sections (ICH E3 format) ā†’ PDF report
10
AuditLogger500ms
Logs all actions (FDA 21 CFR Part 11) ā†’ Audit trail
11
Data Store2s
Saves results to PostgreSQL + S3 ā†’ Persisted data
12
Researcher5min total
Receives analysis report + audit log ā†’ Final deliverable
1
Volume
10-50 trials/month
Pattern
Serverless Monolith
šŸ—ļø
Architecture
AWS Lambda (Python)
API Gateway
RDS PostgreSQL
S3 (documents)
Claude API
Cost & Performance
$500/mo
per month
3-5min per trial
2
Volume
50-500 trials/month
Pattern
Queue + Workers
šŸ—ļø
Architecture
ECS Fargate (API)
SQS (job queue)
Lambda workers
RDS + S3
Redis (cache)
Cost & Performance
$2K/mo
per month
2-4min per trial
3
Volume
500-5K trials/month
Pattern
Multi-Agent Orchestration
šŸ—ļø
Architecture
EKS (Kubernetes)
LangGraph orchestrator
Kafka (event streaming)
Aurora PostgreSQL
Pinecone (vector DB)
Multi-LLM (Claude + GPT failover)
Cost & Performance
$8K/mo
per month
1-3min per trial
Recommended
4
Volume
5K-10K+ trials/month
Pattern
Enterprise Multi-Region
šŸ—ļø
Architecture
Multi-region EKS
Global load balancer
Multi-tenant isolation (VPC per customer)
Replicated Aurora (multi-region)
Private LLM endpoints
Dedicated compliance infrastructure
Cost & Performance
$25K+/mo
per month
<1min per trial

Key Integrations

EDC Systems (Medidata Rave, Oracle Clinical)

Protocol: REST API + HL7 FHIR
Poll EDC for new case reports
Extract via FHIR API
Map to internal schema
Trigger analysis pipeline

EHR Systems (Epic, Cerner)

Protocol: HL7 FHIR R4
Query patient data via FHIR
Link to trial subjects
Enrich trial data with real-world outcomes
Anonymize before analysis

AWS Comprehend Medical

Protocol: AWS SDK (boto3)
Send trial text to Comprehend
DetectPHI API call
Receive PHI entities
Redact before LLM processing

ClinicalTrials.gov

Protocol: REST API (public)
Query by NCT ID
Fetch protocol metadata
Cross-reference with extracted data
Flag discrepancies

FDA FAERS (Adverse Event Reporting)

Protocol: REST API (OpenFDA)
Query by drug name
Fetch historical adverse events
Compare with trial safety data
Generate comparative safety report

Security & Compliance

Failure Modes & Recovery

FailureFallbackImpactSLA
LLM API down (Claude/GPT)Switch to backup LLM ā†’ Queue for retry ā†’ Manual queue (if all fail)Degraded latency (30s delay), no data loss99.5% (5min downtime/mo)
Extraction low confidence (<0.7)Flag for human review ā†’ Re-extract with different prompt ā†’ Use template-based extractionQuality maintained (no false positives)99.9% (human review SLA: 24hr)
PHI detection fails (Comprehend Medical timeout)Block processing ā†’ Retry 3x ā†’ Escalate to compliance teamSafety first (no PHI leakage)100% (zero tolerance for PHI exposure)
Database unavailable (RDS outage)Read from replica ā†’ Cache recent queries (Redis) ā†’ Queue writes for replayRead-only mode (30min), writes delayed99.9% (multi-AZ failover <5min)
Audit log write failsRetry 3x ā†’ Write to backup S3 bucket ā†’ Alert opsCompliance risk (no audit trail)100% (FDA requirement)
Agent timeout (processing >10min)Save partial results ā†’ Resume from checkpoint ā†’ Split into smaller tasksDegraded latency (retry adds 5min)95% complete <5min, 99% <10min
Multi-tenant resource contentionThrottle lower-priority tenants ā†’ Spin up additional workers ā†’ Notify affected tenantsFair queuing (enterprise tenants prioritized)Per-tenant SLA (enterprise: 99.9%, standard: 99.0%)

RAG vs Fine-Tuning

Hallucination Detection

LLMs hallucinate trial data (fake endpoints, false adverse events, invented statistics)
L1
Confidence scoring (LLM logprobs < -5 = low confidence)
L2
Cross-reference with ClinicalTrials.gov (NCT ID, sponsor, phase)
L3
Statistical plausibility (p-values, effect sizes, sample sizes)
L4
Human expert review (flagged for medical writer)
0.2% hallucination rate (2 per 1000 trials), 100% caught by L1-L3, 0% reach reports

Evaluation Framework

Extraction Accuracy
99.7%target: 99.5%
Recall (Completeness)
96.8%target: 95%
Precision (No Hallucinations)
99.8%target: 99%
Safety Signal Detection
92.3%target: 90% sensitivity
Report Quality
4.6/5target: 4.5/5 (human eval)
Testing: Shadow mode: 500 trials processed in parallel with human experts. Weekly comparison meetings. Quarterly model updates based on feedback.

Dataset Curation

1
Collect: 5K trial protocols (ClinicalTrials.gov + partner pharma) - Web scraping + API + data partnerships
2
De-identify: 5K ā†’ 4.8K usable (200 excluded for privacy) - AWS Comprehend Medical + manual review
3
Label: 4.8K labeled (500 fields per trial = 2.4M labels) - ($$240K (medical experts @ $50/trial))
4
Augment: +1.2K synthetic trials (edge cases) - GPT-4 generates realistic protocols with known errors
ā†’ 6K high-quality training examples. 80/10/10 train/val/test split. Quarterly refresh with new trials.

Agentic RAG

Executor agent iteratively retrieves context based on reasoning chain
Trial mentions 'pembrolizumab' ā†’ RAG retrieves drug info ā†’ Agent reasons 'need immune-related AEs' ā†’ RAG retrieves irAE patterns ā†’ Agent reasons 'need dosing for renal impairment' ā†’ RAG retrieves renal dosing ā†’ Question generated with full context.
šŸ’” Not one-shot retrieval. Agent builds knowledge graph on-the-fly. 15% higher accuracy vs static RAG.

Multi-Model Ensemble

Tech Stack Summary

LLMs
Claude 3.5 Sonnet (primary), GPT-4 Turbo (fallback), Gemini Pro (tie-breaker)
Orchestration
LangGraph (agent framework), Apache Airflow (batch pipelines)
Database
Aurora PostgreSQL (primary), Redis (cache), Pinecone (vector DB)
Compute
EKS (Kubernetes), Lambda (serverless), Fargate (containers)
Storage
S3 (documents, logs), EFS (shared file system)
Queue
SQS (job queue), Kafka (event streaming)
Monitoring
Datadog (metrics, logs, traces), Grafana (dashboards), PagerDuty (alerts)
Security
AWS KMS (encryption), Secrets Manager (credentials), WAF (DDoS protection), IAM (access control)
CI/CD
GitHub Actions (CI), ArgoCD (CD), Terraform (IaC)
ML Ops
Feast (feature store), MLflow (model registry), Weights & Biases (experiment tracking)
šŸ—ļø

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