Conexión de registros sanitarios: base poblacional de salud de Andalucía / Health record linkage: Andalusian health population database

OBJETIVO: Describir el desarrollo de un sistema de información que conecta datos procedentes de múltiples registros, sanitarios y otros, para su uso con fines asistenciales, de administración, gestión, evaluación, inspección, investigación y salud pública. MÉTODO: Conexión determinística de datos pseudonimizados de una población de 8,5 millones de habitantes, procedentes de Base de datos de usuarios, Historia clínica electrónica DIRAYA, Conjunto mínimo básico de datos (hospitalización, cirugía mayor ambulatoria, urgencias hospitalarias y hospital de día médico) y sistemas de información de salud mental, pruebas de imagen, pruebas analíticas, vacunas, pacientes renales y farmacia. Se utilizó un codificador automático para los diagnósticos clínicos y se definieron 80 enfermedades crónicas para su seguimiento. La arquitectura del sistema de información constó de tres capas: datos (base de datos Oracle 11g), aplicaciones (MicroStrategy BI) y presentación (MicroStrategy Web, librerías JavaScript, HTML 5 y hojas de estilo CSS). Se implantaron medidas para la gobernanza del sistema. RESULTADOS: Se incluyeron datos de 12,5 millones de personas que fueron usuarias entre los años 2001 y 2017, con 435,5 millones de diagnósticos. El 88,7% de estos diagnósticos fueron generados por el codificador automático. Los datos se presentan mediante informes predefinidos o consultas dinámicas, ambos exportables a ficheros CSV para su tratamiento fuera del sistema. Analistas expertos pueden acceder directamente a las bases de datos y realizar extracciones mediante SQL o tratar directamente los datos con herramientas externas. CONCLUSIÓN: El trabajo ha mostrado cómo la conexión de registros sanitarios abre nuevas posibilidades en el análisis de datos

OBJECTIVE: To describe the development of an information system that connects data from multiple health records to improve assistance to patients, health services administration, management, evaluation, and inspection, as well as public health and research. METHOD: Deterministic connection of pseudonymized data from a population of 8.5 million inhabitants provided by: a users database, DIRAYA electronic medical records, minimum basic data sets (inpatients, outpatient mayor surgery, hospital emergencies and medical day hospital), mental health information systems, analytical and image tests, vaccines, renal patients, and pharmacy. An automatic coder was used to code clinical diagnoses and 80 chronic pathologies were identified to follow-up. The architecture of the information system consisted of three layers: data (Oracle Database 11g), applications (MicroStrategy BI) and presentation (MicroStrategy Web, JavaScript libraries, HTML 5 and CSS style sheets). Measures for the governance of the system were implemented. RESULTS: Data from 12.5 million health system users between 2001 and 2017 were gathered, including 435.5 million diagnoses, 88.7% of which were generated by the automatic coder. Data can be accessed through predefined reports or dynamic queries, both exportable to CSV files for processing outside the system. Expert analysts can directly access the databases and perform queries using SQL or directly treat the data with external tools. CONCLUSION: The work has shown that the connection of health records opens new possibilities for data analysis

[Health record linkage: Andalusian health population database]. / Conexión de registros sanitarios: base poblacional de salud de Andalucía.

OBJECTIVE: To describe the development of an information system that connects data from multiple health records to improve assistance to patients, health services administration, management, evaluation, and inspection, as well as public health and research. METHOD: Deterministic connection of pseudonymized data from a population of 8.5 million inhabitants provided by: a users database, DIRAYA electronic medical records, minimum basic data sets (inpatients, outpatient mayor surgery, hospital emergencies and medical day hospital), mental health information systems, analytical and image tests, vaccines, renal patients, and pharmacy. An automatic coder was used to code clinical diagnoses and 80 chronic pathologies were identified to follow-up. The architecture of the information system consisted of three layers: data (Oracle Database 11g), applications (MicroStrategy BI) and presentation (MicroStrategy Web, JavaScript libraries, HTML 5 and CSS style sheets). Measures for the governance of the system were implemented. RESULTS: Data from 12.5 million health system users between 2001 and 2017 were gathered, including 435.5 million diagnoses, 88.7% of which were generated by the automatic coder. Data can be accessed through predefined reports or dynamic queries, both exportable to CSV files for processing outside the system. Expert analysts can directly access the databases and perform queries using SQL or directly treat the data with external tools. CONCLUSION: The work has shown that the connection of health records opens new possibilities for data analysis.

Características de las urgencias hospitalarias del Servicio Andalus de Salud durante 2012. A partir del Conjunto Mínimo Básico de Datos de urgencias / Case-mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain

Fundamentos: La implantación de Diraya-Urgencias en los hospitales del Servicio Andaluz de Salud (SAS) y el desarrollo de un codificador automático propio ha permitido instaurar el Conjunto Mínimo Básico de Datos de Urgencias (CMBD-U). El objetivo de este artículo es describir la casuística de los servicios de urgencias hospitalarios utilizando las distintas dimensiones contenidas en el CMBD-U. Métodos: Utilizando el CMBD-U, se clasificaron 3.235.600 registros de urgencias hospitalarias de 2012 en categorías clínicas mediante el código CIE-9-MC proporcionado por el codificador automático. Se definieron reglas de validez para la explotación de los tiempos. Se realizó un análisis descriptivo obteniendo indicadores demográficos, cronológicos, tasas de hospitalización, retorno y exitus y tiempos asistenciales y de permanencia en urgencias. Resultados: Las mujeres generaron el 54,26% de las urgencias. Su edad media (39,98 años) superó a la de los hombres (37,61). El 21,49% fueron urgencias pediátricas. La máxima afluencia horaria fue de 10:00 a 13:00 y de 16:00 a 17:00. Los pacientes que no pasaron por observación (92,67%) permanecieron en urgencias 153 minutos de media. Más del 50% de las urgencias fueron generadas por lesiones e intoxicaciones, enfermedades respiratorias, osteomusculares y síntomas y signos. Entre los procesos asistenciales integrados se identificaron 79.191 casos de dolor torácico, 28.741 de insuficiencia cardiaca y 27.989 infecciones graves. Conclusiones: El CMBD-U permite analizar sistemáticamente las urgencias hospitalarias identificando la actividad desarrollada, la casuística atendida, los tiempos de respuesta asistencial y permanencia en urgencias y la calidad asistencial (AU)

Background: The implementation of digital health records in emergency departments (ED) in hospitals in theAndalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the casemix of hospital EDs using various dimensions contained in the MDS-ED. Methods: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the EDs. Results:Women generated 54,26%of all occurrences and their average age (39,98 years) was higher than men’s (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. Conclusions: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care (AU)

[Case-Mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain]. / Características de las urgencias hospitalarias del Servicio Andaluz de Salud durante 2012 a partir del Conjunto Mínimo Básico de Datos de Urgencias.

BACKGROUND: The implementation of digital health records in emergency departments (ED) in hospitals in the Andalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the case mix of hospital EDs using various dimensions contained in the MDS-ED. METHODS: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the Eds. RESULTS: Women generated 54,26% of all occurrences and their average age (39,98 years) was higher than men's (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. CONCLUSIONS: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care.

Características de las urgencias hospitalarias del Servicio Andaluz de Salud durante 2012 a partir del Conjunto Mínimo Básico de Datos de Urgencias / Case-mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain

Fundamentos: La implantación de Diraya-Urgencias en los hospitales del Servicio Andaluz de Salud (SAS) y el desarrollo de un codificador automático propio ha permitido instaurar el Conjunto Mínimo Básico de Datos de Urgencias (CMBD-U). El objetivo de este artículo es describir la casuística de los servicios de urgencias hospitalarios utilizando las distintas dimensiones contenidas en el CMBD-U. Métodos: Utilizando el CMBD-U, se clasificaron 3.235.600 registros de urgencias hospitalarias de 2012 en categorías clínicas mediante el código CIE-9-MC proporcionado por el codificador automático. Se definieron reglas de validez para la explotación de los tiempos. Se realizó un análisis descriptivo obteniendo indicadores demográficos, cronológicos, tasas de hospitalización, retorno y exitus y tiempos asistenciales y de permanencia en urgencias. Resultados: Las mujeres generaron el 54,26% de las urgencias. Su edad media (39,98 años) superó a la de los hombres (37,61). El 21,49% fueron urgencias pediátricas. La máxima afluencia horaria fue de 10:00 a 13:00 y de 16:00 a 17:00. Los pacientes que no pasaron por observación (92,67%) permanecieron en urgencias 153 minutos de media. Más del 50% de las urgencias fueron generadas por lesiones e intoxicaciones, enfermedades respiratorias, osteomusculares y síntomas y signos. Entre los procesos asistenciales integrados se identificaron 79.191 casos de dolor torácico, 28.741 de insuficiencia cardiaca y 27.989 infecciones graves. Conclusiones: El CMBD-U permite analizar sistemáticamente las urgencias hospitalarias identificando la actividad desarrollada, la casuística atendida, los tiempos de respuesta asistencial y permanencia en urgencias y la calidad asistencial (AU)

Background: The implementation of digital health records in emergency departments (ED) in hospitals in the Andalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the case mix of hospital EDs using various dimensions contained in the MDS-ED. Methods: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the Eds. Results: Women generated 54,26% of all occurrences and their average age (39,98 years) was higher than men's (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. Conclusions: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care(AU)