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Overview VIGIA integrates with DIGEMID (Dirección General de Medicamentos, Insumos y Drogas), Peru’s national regulatory authority, to scrape safety alerts, perform product lookups, and generate compliant RAM (Reacción Adversa a Medicamentos) export reports.
Key Capabilities
Alert Scraping Automatically retrieves safety alerts and modifications from DIGEMID’s public portal
Product Lookup Searches product registry by name, sanitario number, or active ingredient
RAM Export Generates PDF reports in official DIGEMID format (Modelo A & B)
Data Enrichment Optional AI-powered field enhancement using Gemini
Alert Scraping The DIGEMID scraper extracts safety alerts from the official website and parses structured data including alert numbers, affected products, and reasons for action.
Data Structure Each scraped alert contains:
{ "n_alerta" : "ALERTA DIGEMID Nº 015-2024" , "producto" : "Atorvastatina 40mg; Losartán 50mg" , "motivo" : "Detección de impurezas fuera de especificación" , "enlace" : "https://www.digemid.minsa.gob.pe/..." , "fecha_publicada" : datetime( 2024 , 8 , 5 , tzinfo = UTC ) }
Implementation The scraper handles multiple layouts:
backend/app/services/digemid.py
backend/app/routers/source_digemid.py
def scrape_digemid_alerts ( index_url : str , max_posts : int = 100 ) -> List[Dict]: """ Scrapes DIGEMID alerts from index page. Returns: List of dicts with: n_alerta, producto, motivo, enlace, fecha_publicada """ html = _get_html(index_url) soup = _soup(html) # Try table format first rows = _parse_table_index(soup, index_url) if rows: return rows # Otherwise, collect post cards with dates post_pairs = _collect_posts_with_dates_from_index(soup, index_url) out: List[Dict] = [] for link, fecha_from_card in post_pairs[:max_posts]: try : detail_html = _get_html(link) data = _parse_detail_page(detail_html, link) if not data.get( "fecha_publicada" ): data[ "fecha_publicada" ] = fecha_from_card out.append(data) except Exception : continue return out
Date Parsing DIGEMID uses Peruvian timezone (America/Lima) with Spanish month names:
TZ_PE = ZoneInfo( "America/Lima" ) MESES = { "ene" : 1 , "feb" : 2 , "mar" : 3 , "abr" : 4 , "may" : 5 , "jun" : 6 , "jul" : 7 , "ago" : 8 , "set" : 9 , "sep" : 9 , "oct" : 10 , "nov" : 11 , "dic" : 12 } def _parse_fecha_publicada_from_card ( card : BeautifulSoup) -> Optional[datetime]: text = " " .join(card.get_text( " " ).split()) m = re.search( r " \b ( \d {1,2} ) \s * ( Ene | Feb | Mar | Abr | May | Jun | Jul | Ago | Set | Sep | Oct | Nov | Dic ) \b " , text, re.I) if not m: return None day = int (m.group( 1 )) mon = MESES [m.group( 2 ).lower()[: 3 ]] year = _parse_year_from_context(card, text) or datetime.now( TZ_PE ).year local_dt = datetime(year, mon, day, 12 , 0 , 0 , tzinfo = TZ_PE ) return local_dt.astimezone(timezone.utc)
RAM Export (ICSR Reports) VIGIA generates official DIGEMID-compliant PDF reports in two formats:
Modelo A: Evaluation Report (FRT-OP-017) Internal causality assessment using the Karch-Lasagna algorithm.
from app.services.icsr_digemid_export import generate_model_a_evaluacion_pdf # Generate evaluation report pdf_path = generate_model_a_evaluacion_pdf( db = db, icsr_id = 12345 , out_path = "/storage/icsr_exports/modelo_a_evaluacion_12345.pdf" )
Sections included:
Client/Institution reporting
Product description (presentation, lot, sanitary registration)
Communication description (narrative)
Causality analysis (Karch-Lasagna criteria)
Conclusions and recommendations
Signatures (analyst and operations manager)
Official format for regulatory submission based on DOCX template.
from app.services.icsr_digemid_export import generate_model_b_prof_salud_pdf # Generate professional health format pdf_path = generate_model_b_prof_salud_pdf( db = db, icsr_id = 12345 , out_path = "/storage/icsr_exports/modelo_b_prof_salud_12345.pdf" , extra_ctx = { "firma_nombre" : "Dr. Juan Pérez" , "firma_cargo" : "Pharmacovigilance Manager" , "firma_img_path" : "/path/to/signature.png" , } )
Template Processing:
Reads DOCX template from backend/app/templates/docs/Formato Profesionales Salud.docx
Renders with docxtpl using Jinja2 context
Converts to PDF using docx2pdf (Windows/Mac) or LibreOffice (Linux)
Saves filled DOCX copy to storage/icsr_exports/ICSR_{id}_ProfSalud_Relleno.docx
Context Builder Both models use a shared context builder:
from app.services.icsr_export_ctx import build_ctx_for_digemid ctx = build_ctx_for_digemid(db, icsr_id) # Returns dict with: # - paciente_iniciales, edad, sexo # - producto_sospechoso, presentacion, lote # - evento_adverso, fecha_inicio # - karch_criterios (temporalidad, conocimiento_previo, etc.) # - clasificacion_qf/clasificacion_sys
Configuration Environment Variables # Template path (optional, defaults to backend/app/templates/docs/) DIGEMID_TEMPLATE = /path/to/Formato \ Profesionales\ Salud.docx # Export storage directory ICSREXPORT_DIR = /path/to/storage/icsr_exports # AI enhancement (optional) GEMINI_API_KEY = your_gemini_key_here
DOCX to PDF Conversion The system attempts conversion in this order:
docx2pdf (requires Microsoft Word on Windows/Mac)
Windows: Initializes COM with pythoncom.CoInitialize()
Mac: Uses Word automation
LibreOffice headless (cross-platform fallback)soffice --headless --convert-to pdf --outdir /output /input.docx
If both fail, the system returns the filled DOCX file.
API Endpoints Search Alerts GET /api/v1/sources/digemid?q={query}&start={date}&end={date}&max_pages={int}
Response: { "source" : "digemid" , "ok" : true , "query" : "atorvastatina" , "period" : { "from" : "2024-01-01" , "to" : "2024-12-31" }, "hits" : 3 , "items" : [ { "n_alerta" : "ALERTA DIGEMID Nº 015-2024" , "producto" : "Atorvastatina 40mg" , "motivo" : "Impurezas detectadas" , "enlace" : "https://..." , "fecha_publicada" : "2024-08-05T16:00:00Z" } ], "traces" : [] }
POST /api/v1/icsr/{id}/export/digemid Content-Type : application/json { "modelo" : "B" , "firma_nombre" : "Dr. Juan Pérez" , "firma_cargo" : "Pharmacovigilance Manager" }
Response: PDF binary with Content-Disposition: attachment; filename="modelo_b_prof_salud_12345.pdf"Error Handling DIGEMID’s website structure changes occasionally. The scraper includes multiple fallback strategies:
Table-based index parsing
Card-based post extraction
PDF text extraction for missing fields
AI-powered field enrichment
Related Pages
FDA Integration US FDA FAERS database access
EMA Integration European safety alerts
VigiAccess WHO global database