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3.
Rev Esp Salud Publica ; 89(5): 515-22, 2015 Oct.
Article in Spanish | MEDLINE | ID: mdl-26650475

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.


Subject(s)
Disease Notification/methods , Electronic Health Records , Public Health Surveillance/methods , Cross-Sectional Studies , Humans , Spain/epidemiology
5.
Rev. esp. salud pública ; 89(5): 515-522, sept.-oct. 2015. tab, ilus
Article in Spanish | IBECS | ID: ibc-145437

ABSTRACT

Fundamentos: En 2009 se implantó el sistema para la importación automática (IA) de casos con sospecha de enfermedad de declaración obligatoria (EDO) desde la historia clínica digital (HCD) a la RedAlerta, aplicación informática para la vigilancia epidemiológica en Andalucía. Hasta ahora, la contribución de este sistema a la clásica declaración activa no se ha determinado suficientemente. El principal objetivo de este estudio es evaluar la utilidad de IA en la provincia de Granada, entre 2009 y 2014. Métodos: Durante el periodo de estudio (2009-2014), un epidemiólogo validó si las EDO importadas satisfacían el criterio de declaración o no. Se halló la contribución de la IA a la RedAlerta y el porcentaje de validación de IA, estimando su IC 95%.Resultados: La contribución de la IA fue del 17,3% (IC95%: 16,1-18,5). Por tipo de declaración el 5,2% (IC95%:4,1-6,5) fueron las urgentes y 24,4% (IC95%: 22,7-26,2) fueron ordinarias. La contribución fue superior al 45% en la enfermedad de Lyme, hipotiroidismo congénito, herpes genital, hepatitis C y otras hepatitis víricas. El 30% (IC95%:28,1-32) de las IA fueron validadas de las cuales el 39,9% (IC95%:33–47,2) fueron urgentes y el 29,1% (IC95%:27,2–31,2%) ordinarias. El porcentaje de validación fue superior al 45% (entre el 47,5 y el 100%) en enfermedades vacunables, en las de transmisión sexual y en las de baja incidencia. Conclusiones: Si bien no sustituye la declaración manual y requiere de un proceso de verificación, el sistema de incorporación automática es útil e incrementa la exhaustividad del sistema de vigilancia epidemiológica (AU)


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 (AU)


Subject(s)
Female , Humans , Male , Medical Records/economics , Medical Records/legislation & jurisprudence , Medical Records/standards , Epidemiological Monitoring/legislation & jurisprudence , Epidemiological Monitoring/organization & administration , Epidemiological Monitoring/standards , Clinical Record , Medical Records/classification , Medical Records/statistics & numerical data , Cross-Sectional Studies/methods , Cross-Sectional Studies/trends
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