Automatic and early detection of the deterioration of patients in Intensive and Intermediate Care Units: technological challenges and solutions

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Detección automática y temprana del deterioro de pacientes en unidades de cuidados intensivos: desafíos tecnológicos y soluciones

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Automatic and early detection of the deterioration of patients in Intensive and Intermediate Care Units: technological challenges and solutions

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Detección automática y temprana del deterioro de pacientes en unidades de cuidados intensivos: desafíos tecnológicos y soluciones

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Detección automática y temprana del deterioro de pacientes en unidades de cuidados intensivos: desafíos tecnológicos y soluciones

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Concepções interprofissionais sobre integralidade no cuidado à pessoa com lesão de pele

Objetivo: Analisar as concepções de profissionais de equipes da atenção básica sobre a integralidade no cuidado à pessoa com lesões de pele. Método: Estudo descritivo de abordagem qualitativa realizado mediante grupos focais com doze profissionais de saúde vinculados a quatro equipes de Estratégia de Saúde da Família. Análise categorial temática. Resultados: As concepções interprofissionais expressam a fragmentação das ações de cuidado e do modo como o trabalho organiza-se na atenção a pessoas acometidas por lesões cutâneas. Os profissionais revelam a desarticulação entre os atores e serviços que compõem as redes de apoio necessárias ao cuidado integral neste âmbito. Concebem o conceito de integralidade no cuidado em lesões como proposta de articular-se e interagir conjuntamente frente às necessidades do usuário. Considerações finais: Os profissionais entendem que o cuidado em lesões de pele, na perspectiva da integralidade, demanda a articulação no trabalho interprofissional por meio da implantação de redes de atenção à saúde, a qual supere o modelo fragmentado, centrado unicamente na lesão

Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.Facultad de Informátic

Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.Facultad de Informátic

Sete anos de nutritionDay no Brasil: Estamos melhorando o atendimento nutricional de pacientes internados?

Due to the high prevalence of hospital malnutrition, the nutritionDay worldwide project (nutriDia Brasil) aims to evaluate the nutritional care provided to inpatients.Debido a la alta prevalencia de la malnutrición intrahospitalaria, el proyecto mundial nutritionDay (nutriDia Brasil) busca evaluar la atención nutricional brindada a los pacientes hospitalizados.Devido à alta prevalência de desnutrição hospitalar, o projeto nutritionDay worldwide (nutriDia Brasil) tem como objetivo avaliar os cuidados nutricionais despendidos pelas unidades hospitalares aos seus pacientes

Educazione museale e strategie Open Access

I musei sono spesso percepiti come luoghi poco propensi al cambiamento, ma le chiusure causate dalla pandemia hanno spinto le istituzioni GLAM (Galleries, Libraries, Archives and Museums – Gallerie, Biblioteche, Archivi e Musei) a investire con rapidità nella comunicazione digitale Web. I musei hanno così saputo dimostrare la loro rilevanza nella comunità: affrontando i cambiamenti, cercando strumenti innovativi, sostenendo la resilienza della comunità, attraendo nuove competenze, e rendendo il patrimonio più accessibile attraverso le politiche di Open Access. L’Open Access ha un forte impatto sui musei e offre nuovi strumenti per sviluppare le priorità museologiche legate all’inclusività, accessibilità e coinvolgimento attivo del pubblico. I musei stanno chiarendo i motivi che li hanno portati a sperimentare queste nuove direzioni nelle loro open policies. Un’osservazione attenta ai casi nazionali e internazionali, nonché la percezione della rilevanza mondiale dell’Open Access ci possono offrire strumenti di riflessione e stimoli decisionali