LIMS Workstation
Laboratory Information Management System for automated diagnostic workflows.
Engineering Telemetry & Metrics
Bottlenecks & Scale Constraints
Manual paper-heavy workflows introduced a 4% human error rate in diagnostic entry. On-premise silos prevented remote data sharing and caused turnarounds averaging 48 hours.
Target Architecture & Execution
Built a cloud-native SPA managing samples with Redux state. Created an 'AI Guardrails' microservice to flag anomalous result spikes against longitudinal patient history, ensuring HIPAA compliance via RBAC.
Key Architectural Pillars
Real-time Sample Tracking
AI-Assisted Result Validation
HIPAA Compliant RBAC
Immutable PostgreSQL Audit Logs
Data Pipeline & Node Flow
Microservices architecture separating concerns. Node.js API Gateway handles sync/routing to MongoDB. AI Validator runs as an isolated Python service via gRPC for high-speed anomaly scoring.
Dashboard (React)
Implementation Snippet
Acts as a gatekeeper before database commits. Aggregates longitudinal history and hits the AI model. Flagging high-confidence anomalies to prevent diagnostic errors.
async function validateSample(sample: Sample) {
const history = await db.samples.find({ patientId: sample.patientId });
const analysis = await aiModel.predict({
current: sample.data,
history: history.map(h => h.data)
});
if (analysis.confidence > 0.9 && analysis.isAnomaly) {
await AlertService.notify('QA_TEAM', { sampleId: sample.id });
return { status: 'FLAGGED', reason: analysis.type };
}
return { status: 'APPROVED' };
}Dossier Specifications
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