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.
Pharmaceutical R&D teams, medical affairs departments, and clinical operations groups managing large document repositories for drug development programs
Finding relevant evidence across protocols, clinical study reports, publications, and regulatory submissions takes weeks and risks missing critical safety signals or efficacy data
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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
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 documentsawait 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}});
Enable Semantic Clinical Search
Search with medical understanding
// Search clinical documents semanticallyasync 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;}
Extract Safety Signals
Automatically identify safety-related content
// Monitor for safety signals across documentsasync function findSafetySignals(drugProgram: string) {const signals = await client.retrieve({collection_id: 'clinical-docs',query: {type: 'safety_signal', // Specialized safety queryscope: 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)};}
Generate Evidence Summaries
Synthesize evidence across document types
// Create evidence summary for regulatory submissionasync function synthesizeEvidence(topic: string, drugProgram: string) {const evidence = await searchClinicalDocs(topic, {document_types: ['protocol', 'csr', 'publication']});// Group by study and extract key dataconst 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;}
Feature Extractors Used
Retriever Stages Used
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
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