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    12 min read

    Clinical Trial Document Search and Evidence Synthesis

    For pharma R&D teams managing 100K+ clinical documents. Search across protocols, CSRs, and publications. 70% reduction in literature review time.

    Who It's For

    Pharmaceutical R&D teams, medical affairs departments, and clinical operations groups managing large document repositories for drug development programs

    Problem Solved

    Finding relevant evidence across protocols, clinical study reports, publications, and regulatory submissions takes weeks and risks missing critical safety signals or efficacy data

    Why Mixpeek

    70% reduction in literature review time, 98% recall for relevant documents, and automatic identification of safety signals across document types

    Overview

    Drug development requires synthesizing evidence across thousands of documents. This use case shows how Mixpeek accelerates clinical documentation search while ensuring no critical evidence is missed.

    Challenges This Solves

    Document Volume

    100K+ documents per drug program across 10+ years

    Impact: Critical evidence buried in massive document repositories

    Format Complexity

    Tables, figures, appendices with critical data

    Impact: Text search misses data locked in non-text formats

    Medical Terminology

    Synonyms, abbreviations, evolving terminology

    Impact: Keyword search misses relevant documents using different terms

    Regulatory Requirements

    Must demonstrate comprehensive evidence review

    Impact: Incomplete searches risk regulatory findings or safety issues

    Implementation Steps

    Mixpeek indexes all clinical trial documentation including tables, figures, and appendices, enabling semantic search across the entire knowledge base with medical terminology understanding

    1

    Index Clinical Document Repository

    Process all document types with medical understanding

    import { Mixpeek } from 'mixpeek';
    const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });
    // Index clinical trial documents
    await client.buckets.connect({
    collection_id: 'clinical-docs',
    bucket_uri: 's3://clinical/documents/',
    extractors: [
    'document-parser', // PDFs, Word
    'table-extraction', // Clinical data tables
    'figure-analysis', // Efficacy/safety figures
    'medical-ner', // Medical entity extraction
    'section-detection' // Protocol/CSR sections
    ],
    settings: {
    medical_vocabularies: ['MedDRA', 'SNOMED', 'ICD-10'],
    document_types: ['protocol', 'csr', 'publication', 'sae_report', 'submission'],
    extract_references: true,
    hipaa_compliant: true
    }
    });
    2

    Enable Semantic Clinical Search

    Search with medical understanding

    // Search clinical documents semantically
    async function searchClinicalDocs(query: string, filters?: {
    document_types?: string[];
    study_phases?: string[];
    date_range?: { start: string; end: string };
    indications?: string[];
    }) {
    const results = await client.retrieve({
    collection_id: 'clinical-docs',
    query: {
    type: 'text',
    text: query, // e.g., "hepatotoxicity signals in phase 3"
    expand_medical_terms: true // Expand to synonyms
    },
    filters: filters,
    return_fields: [
    'content', 'document_type', 'study_id',
    'extracted_tables', 'extracted_figures',
    'medical_entities', 'section'
    ],
    limit: 100
    });
    return results;
    }
    3

    Extract Safety Signals

    Automatically identify safety-related content

    // Monitor for safety signals across documents
    async function findSafetySignals(drugProgram: string) {
    const signals = await client.retrieve({
    collection_id: 'clinical-docs',
    query: {
    type: 'safety_signal', // Specialized safety query
    scope: drugProgram
    },
    filters: {
    document_type: { $in: ['sae_report', 'csr', 'dsmb_report'] }
    },
    return_fields: [
    'adverse_events', 'sae_details', 'causality_assessment',
    'frequency', 'severity', 'source_document'
    ],
    aggregate_by: 'adverse_event_term'
    });
    return {
    signals_by_term: signals.aggregations,
    new_signals: signals.results.filter(s => s.is_new),
    severity_distribution: calculateSeverityDistribution(signals.results)
    };
    }
    4

    Generate Evidence Summaries

    Synthesize evidence across document types

    // Create evidence summary for regulatory submission
    async function synthesizeEvidence(topic: string, drugProgram: string) {
    const evidence = await searchClinicalDocs(topic, {
    document_types: ['protocol', 'csr', 'publication']
    });
    // Group by study and extract key data
    const synthesis = {
    topic: topic,
    studies_included: [...new Set(evidence.map(e => e.study_id))],
    efficacy_data: evidence
    .filter(e => e.section === 'efficacy')
    .map(e => ({
    study: e.study_id,
    endpoint: e.extracted_tables[0]?.endpoint,
    result: e.extracted_tables[0]?.result
    })),
    safety_data: evidence
    .filter(e => e.section === 'safety')
    .map(e => ({
    study: e.study_id,
    aes: e.adverse_events
    })),
    references: evidence.map(e => ({
    document: e.document_type,
    location: e.page_number,
    citation: e.citation
    }))
    };
    return synthesis;
    }

    Expected Outcomes

    70% reduction in systematic review time

    Literature Review Time

    98% relevant document recall vs 75% with keyword search

    Document Recall

    3x faster identification of emerging safety signals

    Safety Signal Detection

    100% of table data searchable vs 0% with text-only search

    Table Data Access

    50% faster evidence package preparation

    Regulatory Submission Prep

    Frequently Asked Questions

    Ready to Implement This Use Case?

    Our team can help you get started with Clinical Trial Document Search and Evidence Synthesis in your organization.