openevidence-reference-architecture

OpenEvidence Reference Architecture

Overview

Production architecture for clinical decision support integrations with OpenEvidence. Designed for healthcare platforms needing evidence-based query processing, citation-backed clinical answers, and full audit logging for regulatory compliance. Key design drivers: HIPAA-compliant data handling, deterministic citation pipelines for clinical accuracy, query audit trails for malpractice risk mitigation, and sub-second response times for point-of-care workflows where clinicians need answers during patient encounters.

Architecture Diagram

Clinician UI ──→ API Gateway (auth + HIPAA) ──→ Query Service ──→ OpenEvidence API
                        ↓                            ↓             /query
                   Audit Logger ──→ Audit DB    Cache (Redis)      /citations
                        ↓                            ↓
                   Analytics ──→ Usage Dashboard  Citation Store ──→ Evidence DB

Service Layer

class ClinicalQueryService {
  constructor(private oe: OpenEvidenceClient, private cache: CacheLayer, private audit: AuditLogger) {}

  async queryEvidence(query: ClinicalQuery): Promise<EvidenceResponse> {
    await this.audit.log({ type: 'query_submitted', clinicianId: query.clinicianId, queryText: query.text, timestamp: new Date() });
    const cacheKey = `evidence:${this.hashQuery(query.text)}`;
    const cached = await this.cache.get(cacheKey);
    if (cached) { await this.audit.log({ type: 'cache_hit', cacheKey }); return cached; }
    const response = await this.oe.query(query.text, { specialty: query.specialty });
    await this.storeCitations(response.citations);
    await this.cache.set(cacheKey, response, CACHE_CONFIG.evidence.ttl);
    await this.audit.log({ type: 'query_completed', citationCount: response.citations.length });
    return response;
  }

  async getCitationChain(citationId: string): Promise<Citation[]> {
    return this.evidenceDb.getCitationWithReferences(citationId);
  }
}

Caching Strategy

const CACHE_CONFIG = {
  evidence:   { ttl: 86400, prefix: 'evidence' },  // 24 hr — clinical evidence changes slowly
  citations:  { ttl: 604800, prefix: 'cite' },     // 7 days — published citations are stable
  queryHist:  { ttl: 3600, prefix: 'qhist' },      // 1 hr — recent query dedup for same clinician
  guidelines: { ttl: 43200, prefix: 'guide' },      // 12 hr — clinical guidelines update infrequently
  audit:      { ttl: 0, prefix: 'audit' },          // never cached — every audit entry must persist
};
// New guideline publication events invalidate evidence cache for affected specialties

Event Pipeline

class ClinicalEventPipeline {
  private queue = new Bull('clinical-events', { redis: process.env.REDIS_URL });

  async onQueryCompleted(event: QueryCompletedEvent): Promise<void> {
    await this.queue.add('process', event, { attempts: 5, backoff: { type: 'exponential', delay: 2000 } });
  }

  async processQueryEvent(event: QueryCompletedEvent): Promise<void> {
    await this.updateUsageAnalytics(event.clinicianId, event.specialty);
    if (event.feedbackScore !== undefined) await this.logFeedback(event);
    await this.checkGuidelineAlignment(event);  // Flag if answer diverges from current guidelines
  }
}

Data Model

interface ClinicalQuery    { clinicianId: string; text: string; specialty: string; patientContext?: string; urgency: 'routine' | 'urgent'; }
interface EvidenceResponse { answer: string; confidence: number; citations: Citation[]; specialty: string; responseTimeMs: number; }
interface Citation         { id: string; title: string; journal: string; year: number; doi: string; relevanceScore: number; evidenceLevel: 'I' | 'II' | 'III' | 'IV' | 'V'; }
interface AuditEntry       { id: string; type: string; clinicianId: string; timestamp: Date; queryText?: string; citationCount?: number; ipAddress: string; }

Scaling Considerations

• Separate audit write path from query path — audit logging must never slow clinical responses
• Cache evidence responses aggressively — same clinical questions recur across clinicians
• Partition audit DB by month for compliance retention windows and query performance
• Use read replicas for analytics dashboard; primary DB reserved for audit writes
• Rate-limit per clinician to prevent abuse while ensuring genuine clinical queries are never blocked

Error Handling

Component	Failure Mode	Recovery
Evidence query	OpenEvidence API timeout	Serve cached response if available, degrade to "consult specialist" message
Audit logging	Audit DB write failure	Buffer to local WAL, retry with dead-letter queue — never drop audit entries
Citation retrieval	DOI resolution failure	Return citation metadata without full text link, flag for manual review
Cache layer	Redis connection lost	Bypass cache, query API directly, alert ops for cache restoration
HIPAA compliance	Unauthorized access attempt	Immediate block, audit log, alert security team, preserve evidence

Resources

• OpenEvidence Platform
• OpenEvidence for Clinicians

Next Steps

See openevidence-deploy-integration.