Conjunto mínimo de datos: su utilidad en cirugía / Mínimum data set and it utility in surgery

Resumen Los sistemas de información sanitaria son fundamentales para el conocimiento y análisis del estado de salud individual y colectivo, así como para la evaluación de las funciones de los sistemas de salud; basados en el desarrollo de las historias clínicas, los expedientes clínicos permiten el acceso a dicha información. El Conjunto Mínimo de Datos (CMD), es un conjunto esencial de elementos potencialmente disponibles sobre entidades específicas, constituye un extracto de información administrativa, clínica y quirúrgica estandarizados, recogidos a partir del informe de alta o la historia clínica, siendo un paso preliminar en la gestión de información sobre enfermedades, que se traduce en la mejora de la calidad de la atención y el control de las enfermedades, así como en la posibilidad para emprender investigaciones. El objetivo de este manuscrito fue generar un documento de estudio referente al uso del CMD en cirugía, que consideró los mecanismos de aplicación, sus fortalezas y debilidades.

Health information systems are fundamental for the knowledge and analysis of the individual and collective health status, as well as for the evaluation of the functions of the health systems, based on the development of medical records, that allow access to information. The Minimum Data Set (CMD), is an essential set of elements potentially available on specific entities, constitutes an extract of standardized administrative, clinical, and surgical information, collected from the discharge report or the clinical history, being a preliminary step in disease information management that translates into improved quality of care and disease control, as well as the ability to undertake research. The aim of this manuscript was to generate a study document regarding the use of CMD in surgery, which considered the application mechanisms, as well as its strengths and weaknesses.

COVID-19 outbreak in India: an early stage analysis

The COVID-19 outbreak in several countries of the world is facing a challenging task to control the virus transmission as 3.7 million people are tested positive in all over world at the time of writing. India is also suffering with the virus outbreak in different states as on January 30, 2020, India reported its first confirmed case of coronavirus deadly disease (COVID-19) in Kerala state where three students returned from the epicentre of the disease, Wuhan, China. During the first week, India experienced a slow growth in the infected cases but soon after an outbreak has been found in several states and union territories, although strict measures are being made to control the outbreak. This study presents a comprehensive analysis to explore the current status of virus transmission at state and country level, infection growth, most affected age groups, available datasets and prediction models and strict control measures. Several data sources are analysed to collect the pandemic data such as Johns Hopkins University, Ministry of Health, COVID-19 India,Worldometer and media. The analysed study will be significant for scientist, researchers and health workers of India and also for the administrative tasks to consider the different strict measure to control COVID-19

Los Big Data: conceptos relacionados y algunas aplicaciones en pediatría / The Big Data: related concepts and some applications in pediatrics

Resumen: El avance de la tecnología médica, el registro de salud electrónico (EHR, por sus siglas en inglés) y la producción compleja de datos biomoleculares están generando grandes volúmenes de información, en varios formatos y de múltiples fuentes, que se conocen como "Big Data". El análisis integrado de estos datos ha abierto una amplia posibilidad para explorar respuestas a problemas de salud. En pediatría, se han incrementado los estudios se analizan Big Data o se utilizan las herramientas infor máticas que se han desarrollado para analizar estos datos. Los propósitos de esos estudios han sido variados, por ejemplo: en la detección y prevención temprana de una amplia gama de afecciones médicas, mejoramiento de los diagnósticos, para especificar tratamientos o anticipar el resultado de alguna patología, etc. El presente documento tiene como objetivo revisar los conceptos principales involucrados en el análisis de Big Data o en las tecnologías informáticas asociadas, así como también examinar sus aplicaciones, potencialidades y limitaciones actuales. Este estudio se realizó sobre la base de una revisión bibliográfica no sistemática, centrada en el campo de la pediatría. En la selección de los ejemplos de aplicación, se consideró que eran fuentes primarias, publicadas en los últimos cinco años y con poblaciones infanto-juveniles.

Abstract: Medical technology advances, the Electronic Health Record (EHR), and the complex production of biomolecular data are generating large volumes of information, in various formats and from multiple sources, which are known as "Big Data". The integrated analysis of these data has opened up a wide possibility to explore answers related to health problems. In pediatrics, there has been an increase in the studies on Big Data, or the computer tools use that have been developed to analyze these data. The purposes of those studies have been diverse, for example, for earlier detection and prevention of a wide range of medical conditions, improvement of diagnoses, to specify treatments or anticipate the outcome of some pathology and so on. For this reason, this contribution aims to review the main concepts involved in the analysis of Big Data or its related computer technologies, as well as to exa mine their current applications, potentialities, and limitations. This study was carried out based on a non-systematic bibliographical review, focused on the field of pediatrics. In the selection of applica tion examples, it was considered that they were primary sources, published in the last five years and with child and youth populations.

Study of classification of traditional Chinese medicine datasets based on the business domain / 国际中医中药杂志

TCM informatization has accelerated its development in the period of rapid development of the traditional Chinese medicine (TCM) industry. Thus, it is important that to classify the TCM industry management datasets on the business domain, as the business domain of TCM datasets are increasingly rich. The study is to analyses the information flow in business domain of TCM industry management datasets, based on the research of experts. The combination of facet and line classification was applied for the classification and then TCM industry management data resources were divided into eight business domains to compile the“Classification code table of TCM industry management datasets on the business domains”. The results of this study helped exchange basic information among hospitals, universities, research institutes, enterprises, cultural industries, Chinese medical research centers, general administration and other institutions. Besides, it can also help provide the reference to the strategic decisions in TCM and health services.