Abstract: LARIISA is an intelligent framework for decision-making in public health systems. The project had its initial ideas conceived in 2009. Since then it has evolved in the academic and market perspective, becoming a product in 2018 called GISSA. This article presents the architectural evolution of LARIISA, the functionalities implemented, the scientific and commercial results achieved with GISSA. Ontology and Data Mining (DM) are technologies that support their inference mechanisms. A semantic portal is proposed for GISSA and a DM application is presented.

Abstract: This work presents the Quality of Health Service (QhS), an IoT solution for a health patient monitoring environment and proposes an optimization mechanism with the Diffserv and EWS protocols. A mobile application is implemented for the specific healthcare team to have access to the system for viewing and modifying patient information. The analysis of vital signs in QhS solution took into consideration the network paradigm and IoT service, as well as the risk of the patient based on the EWS protocol. Thus, the QhS combines the Diffserv (network level) and EWS (application level) protocols for the optimization of data traffic in the system monitoring information and alerts. Additionally, It results in energy saving, still a vital resource in IoT devices. Index Terms—IoT, Diffserv, EWS, homecare.

Abstract: Health data monitoring is a key activity to reduce maternal, neonatal and infant mortality rates. Data available in Brazilian health databases points that It is possible to predict death risk in early stages of gestation and newborn development. In this research, we consider the information availability still in gestational period to propose different death risk prediction models for this public of interest. We also detail the data mining process to apply machine learning-based techniques in death risk classification for maternal, neonatal and infant patients. We present an experiment pipeline to estimate average performance and evaluated machine learning models with different features combinations. Additionally, is shown a web service which provides multiple predictive models by information availability. Results shows Random Forest obtaining better performance when compared to the other machine learning methods. Index Terms—Brazilian health data, data mining, information availability

Abstract: Clinical information about patients should be consistent, complete and available to health professionals, ensuring quality care. This information is recorded on paper or on electronically-stored in a digital format. The Brazilian Government and the private providers of health services have invested in Information and Communication Technology in health, aiming at the construction of the Electronic Medical Record (EMR) that replaces the medical record on paper. EMRs are evolving to Electronic Health Record (EHR) which allows for interoperability between different systems. While the brazilian public health system uses the OpenEHR standard as an information model for EHR, private providers in Brazil have increasingly used the HL7 FHIR standard. This article proposes the GIRLS, a lowcost gateway for EHR interoperability that uses both standards. As proof of concept, a chikungunya OpenEHR archetype and an equivalent FHIR feature were implemented. This archetype is available to the Clinical Knowledge Manager (CKM), the largest online repository of archetypes on the Web.

Abstract: This work presents the Quality of Health Service (QhS), an IoT solution for a health patient monitoring environment and proposes an optimization mechanism with the Diffserv and EWS protocols. A mobile application is implemented for the specific healthcare team to have access to the system for viewing and modifying patient information. The analysis of vital signs in QhS solution took into consideration the network paradigm and IoT service, as well as the risk of the patient based on the EWS protocol. Thus, the QhS combines the Diffserv (network level) and EWS (application level) protocols for the optimization of data traffic in the system monitoring information and alerts. Additionally, It results in energy saving, still a vital resource in IoT devices.

O avanço tecnológico na sociedade é um fato e faz-se necessário considerar o uso de tecnologias nos sistemas de saúde, aumentando o escopo de intervenções no âmbito da atenção e da gestão. Os objetivos do estudo foram analisar a associação entre tecnologias em saúde e gestão compartilhada e identificar as contribuições das Tecnologias de Informação e Comunicação (TICs) para a gestão compartilhada em saúde. Métodos: Estudo qualitativo. Trata-se de uma Revisão Integrativa de Literatura com artigos científicos em língua portuguesa e inglesa. Encontradas 115 publicações na MEDLINE e 3 na Revista Eletrônica Gestão & Saúde. Resultados: Analisou-se 8 produções, publicadas no período de 2008 a 2018, identificando-se experiências de avaliação de tecnologias em saúde em 25 países com o exercício de gestão compartilhada. O Projeto de Lei Federal Nº 9.617/2018 foi incluso por propor a gestão compartilhada através da comunicação na internet, totalizando 9 produções. É possível o público participar da gestão em saúde utilizando ferramentas tecnológicas. Conclusão: Tem-se a ampliação da incorporação de tecnologias na saúde e o seu constante desenvolvimento no contexto nacional e internacional. A gestão compartilhada em saúde através das TICs é uma intervenção de saúde digital que poderá fortalecer a participação social.

Resumo: Objetivo: Compreender a relação entre o instituído constitucional sobre os determinantes sociais da saúde e a vivência urbana num território em situação de vulnerabilidade social. Métodos: Baseou-se na abordagem qualitativa, com utilização de grupo focal e de entrevista semi-estruturada, aplicados a 45 participantes. Realizou-se análise de conteúdo, o que possibilitou a categorização dos dados a partir da técnica de análise temática. Resultados: Os resultados mostraram a vivência no território com insuficiência na oferta de serviços que determinam a saúde da população, apontando para desafios marcantes como a baixa cobertura na Atenção Primária à Saúde e a violência urbana. A busca por qualidade da assistência no Sistema Único de Saúde apresentou-se como estratégia de resistência. Apontou-se para a necessidade de valorização do território como lócus de cuidado das pessoas, com ações intersetoriais que visem promover a saúde da população. Conclusão: Reconhecem-se especificidades dos determinantes sociais na saúde de populações que apresentam condições de vida permeadas por iniquidades e expõem-se subsídios para elaborar medidas que contribuam para superação desses quadros, de forma a gerar equidade social.

Abstract: The quality of services provided to patients in the health area is directly related to the quality of clinical information. In addition, this information must be consistent, secure and available to health professionals, even though health data is usually distributed across heterogeneous systems. The Electronic Patient Record (EPR) was proposed and applied to minimize integration problems through the construction of health information systems. This work proposes a methodology for the development of interoperable and flexible systems, using the EHRServer framework of the OpenEHR standard. As a case study, this methodology has been applied in Aracati/CE since March / 2017, in the context of the Chikungunya disease. The methodology is supported by a system that implements a set of OpenEHR archetypes representing the clinical treatment of Chikungunya. The system was tested in a Basic Health Unit. The archetypes and the MARCIA Templates were made available to the Clinical Knowledge Manager (CKM), the largest online repository of archetypes on the Web.

Resumo: O objetivo deste artigo foi analisar o escopo de práticas dos Agentes Comunitários de Saúde (ACS) relacionando-o à situação social e de saúde, bem como os elementos facilitadores e os limitantes. Trata-se de um estudo transversal de abordagem mista, e estratégia explanatória sequencial, realizado em quatro municípios do Ceará. No estudo quantitativo, a amostra de 160 ACS foi aleatória com instrumento estruturado. No qualitativo, realizou-se seis grupos focais e entrevistas. Prevaleceram, na amostra, mulheres (139; 86,9%), casadas (111; 69,4%), com renda familiar maior ou igual a 2 salários mínimos (102; 63,7%), nível técnico incompleto (68; 42,5%), da zona urbana (114; 71,3%), atuando como ACS há menos de 10 anos (93; 58,2%). As principais atividades foram visitação domiciliar de grupos prioritários e cadastramento de famílias. Evidenciou-se a complexidade do trabalho, que inclui ações de promoção e vigilância à saúde como pré-natal, imunizações, hipertensão, diabetes, cuidado com idosos, entre outros. Como limitantes das práticas, identificaram-se: deficiência da formação técnica, suporte reduzido no trabalho e violência. Como potencializadores: educação permanente e gestão participativa. O escopo de práticas dos ACS é complexo e abrangente, incluindo a articulação de políticas públicas no território, o que se constitui em uma potencialidade para promoção da saúde de comunidades vulneráveis.

Resumo: O objetivo deste estudo foi investigar o processo de colaboração interprofissional entre os diretores, docentes de instituições de ensino superior (IES), gestores dos Sistemas Municipais de Saúde e profissionais da Estratégia de Saúde da Família de duas cidades estratégicas para expansão do ensino superior em saúde no Ceará. Tratou-se de estudo analítico de casos múltiplos. Foram utilizadas pesquisa documental e entrevistas semi-estruturadas com 75 gestores e profissionais da saúde, diretores e docentes de IES.

Abstract: GISSA is an intelligent system for health decision making focused on childish maternal care. In this system, are generated alerts that involve the five health domains: clinicalepidemiological, normative, administrative, knowledge management and shared knowledge. The system proposes to contribute to the reduction of child mortality in Brazil. Thus, this paper presents studies over an intelligent module that uses Machine Learning to generate child death risk alerts on GISSA. These studies focus on trying different classification Algorithms, with a methodology based on Data Mining to reach a learning model capable of calculating the probability of a newborn dying. The work brings together public databases SIM and SINASC for the training of classification algorithms, identifying relationships between birth and death data of children under one year. During the methodological process, it was made a subsampling to balance the number of inputs and be fair in the training model results, executed with Matlab scripts.

Abstract: Making good governance decisions is a constant challenge for Public Health administration. Health managers need to make data analysis in order to identify several health problems. In Brazil, these data are made available by DATASUS. Generally, they are stored in distinct and heterogeneous databases. The Linked Data approach allow a homogenized view of the data as a unique basis. This article proposes a ontology-based model and Linked Data to integrate datasets and calculate the probability of maternal and infant death risk in order to give support in decision-making in the GISSA project.

Abstract – This paper proposes the use of Bayesian networks tosupport the decision-making process in health systems governance. In particular, this paper presents LARIISA_Bay, a new component based on Bayesian networks that works together with LARIISA, acontext-aware platform to support applications in public health systems. The main goal of the proposed component is to assist teamsof health specialists in order to better diagnose diseases through data collected from users of LARIISA. As a case study, we focus on scenarios of dengue fever disease. We classify dengue cases into oneof the following levels: emergency (i.e., dengue hemorrhagic fever),grave (i.e., dengue fever) or normal (i.e., absence of the disease). Based on this classification, teams of health specialists can accurately make decisions, for example, to alert a health care agent to visit locations with a high incidence of the disease, to send an ambulance when an dengue emergency case has occurred, as well as give technical instructions on how to deal with specific cases. We present a prototype of LARIISA_Bay and the corresponding interfaces to support the interactions of the patient, the health careagent and the specialist with the system.

Abstract: Despite the fact that infant mortality rates havebeen decreased in recent years, this issue stills being considered alarming to Brazilian health system indicators. In this context,the GISSA framework, an intelligent governance framework for Brazilian health system, emerges as a smart system for the Federal Government program, called Stork Network. Its main objective is to improve the healthcare for pregnant women as well as their newborns. This application aims to generate alerts focusing on the health status verification of newborns and pregnant woman to support decision-makers in preventive actions that may mitigate severe problems. Therefore, this paper presents the LAIS, an Intelligent health analysis system that uses data mining (DM) to generate newborns death risk alerts through probability-based methods. Results show that the NaıveBayes classifier presents better performance than the other DM approaches to the used pregnancy data set analysis of this work. This approach performed an accuracy of 0.982 and a Receiver Operating Characteristic (ROC) Area of 0.921. Both indicators suggest the proposed model may contribute to the reduction of maternal and fetal deaths.

Resumo: Tomar decisões de boa governança é um desafio constante para a administração da Saúde Pública. Os gestores de saúde precisam fazer análises de dados para identificar diversos problemas de saúde. No Brasil, esses dados são disponibilizados pelo DATASUS. Geralmente, eles são armazenados em bancos de dados distintos e heterogêneos. A abordagem Linked Data permite uma visão homogeneizada dos dados como uma base única. Este artigo propõe um modelo baseado em ontologia e Linked Data para integrar conjuntos de dados e calcular a probabilidade de risco de morte materna e infantil para dar suporte à tomada de decisão no projeto GISSA.