title: [Contribution of Electronic Health Record in Surveillance of Notifiable Diseases].
abstract: BACKGROUND
  In 2009 a system was introduced for the automatic import (AI) of cases with suspected notifiable diseases (ND) from electronic medical record (EMR) to RedAlerta, an application for surveillance in Andalusia. At present, the contribution of this system to classical active statement has not been determined enough. The main objective of this study is to evaluate the usefulness of IA in the province of Granada, between 2009 and 2014.
  
  
  METHODS
  During the study period (2009-2014), an epidemiologist assessed whether AI met declaration criteria or not. We calculate the contribution of AI to RedAlerta and the percentage of validation of AI, estimating 95% CI.
  
  
  RESULTS
  The contribution of AI was 17.3% (95% CI 16.1 to 18.5); and type of statement, 5.2% (95% CI 4.1 to 6.5) for urgent and 24.4% (95% CI 22.7 to 26.2) for ordinary. The contribution was higher (more than 45%) in Lyme disease, congenital hypothyroidism, genital herpes, hepatitis C and other viral hepatitis. 30% (95% CI 28.1 to 32) of AI were validated; 39.9% (95% CI 33 to 47.2) urgent and 29.1% (95% CI 27.2 to 31.2%) ordinary. The percentage of validation was higher than 45% (between 47.5 and 100%) in vaccine-preventable diseases, sexually transmitted infections and low incidence.
  
  
  CONCLUSIONS
  Although not replace manual reporting and requires verification, the AI system is useful and increases the completeness of the epidemiological surveillance system.
authors.forename: María Ángeles
authors.initials: MÁ
authors.lastname: Onieva-García
authors.forename: Begoña
authors.initials: B
authors.lastname: López-Hernández
authors.forename: María José
authors.initials: MJ
authors.lastname: Molina-Rueda
authors.forename: Natalia
authors.initials: N
authors.lastname: Cabrera-Castro
authors.forename: María Mar
authors.initials: MM
authors.lastname: Mochón-Ochoa
mesh_terms: D003430:Cross-Sectional Studies; D018563:Disease Notification / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D062486:Public Health Surveillance / Q000379:methods*; D013030:Spain / Q000453:epidemiology