A new architecture for intelligent clinical decision support for intensive medicine

Real-time and intelligent decision support systems are of most importance to supply intensive care professionals with important information in useful time. The work presented hereby shows an architectural overview of the communication system with bedside devices such as vital sign monitors. Intelligent Decision Support System for Intensive Medicine (ICDS4IM) goal is to ensure information quality and availability to Intensive Medicine professionals to take supported decisions in a mutable environment where complex and unpredictable events are a common state. Therefore, this work focus on Health Information Systems, Interoperability and Information Diffusion and Archive. Moreover, communication standards and the usage of a new technology such as containerization are discussed. (C) 2020 The Authors. Published by Elsevier B.V.The work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Projects Scope: UID/CEC/00319/2020 and DSAIPA/DS/0084/2018

A conceptual model for multichannel interaction in healthcare services

Multichannel interaction services have grown and evolved a lot in many sectors and their potential has been demonstrated in terms of monitoring and engaging with customers. In healthcare, has a tremendous impact on health organization as well to patients. In a multichannel interaction, environment patients can interact with health professionals across many channels without losing previous interactions, i.e. patients have a continuity of services across different channels. This paper aims to introduce a conceptual model of multichannel interaction in healthcare services. The model addresses all main actors involved in the process of multichannel interaction. The model proposed was validated through a proof of concept with a proposed artefact designed during the pandemic of new coronavirus COVID-19.FCT -Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Predictive and prescriptive analytics in healthcare: a survey

Over the years, health area has received numerous studies on how to improve its management and administration activities and, fundamentally, the Healthcare provided to its patients. Currently, there is an exponential growth of data in the health system. In this sense, it is crucial the implementation of technologies capable of using it in a beneficial way for the organization, helping it to fulfill its strategic objectives. Subsequently, this same data, if used correctly, has the capacity to assist the organization at an administrative level, as well as at the level of patient care, using predictive and optimization models capable of revolutionizing the current health system. Thus, this article aims to identify the advances that have been made in this area, focusing on the development of predictive and optimization techniques, applied in Health, and how these can improve the lives of managers, doctors and patients.FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/20

ICU data management - A permissioned blockchain approach

Since its origin in finance, blockchain have been revolutionizing data storage and sharing in many other sensitive areas. Being the focus of Permissioned Blockchains around privacy, confidentiality, immutability, interoperability and reliability, it fits perfectly within the data requisites of healthcare. Even more, with the surge of new iterations of more recent implementations based on smart-contracts/chaincode that has its focus on increasing efficiency and usability and ease of implementation. Intensive Medicine an area with such high data complexity and throughput, and high incidence of medical error and patient injury. As such, it's imperative the continuous research and implementation of new technologies that can make pertinent knowledge available through reliable and accurate data, thus providing appropriate problem-solving skills to physicians. This paper presents a solution, as part of the Intelligence Decision Support Systems for Intensive Medicine (ICDS4IM) project, which objective is to increase accuracy, confidentiality and value to data from vital sensors and monitors by assuring its immutability and controlled oversee.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/2018

mHealth: monitoring platform for diabetes patients

Diabetes is a metabolic disease that can be explained by the high level of glucose in the blood. Constant monitoring of patients with this type of disease is crucial to the success of their treatment due to the high number of factors that condition it, such as nutrition, exercise and insulin production. This research consists of a software development project based on mHealth practice, which aims to cover all the needs of patients and health professionals, introducing improvements in the prevention, diagnosis and control of endocrine pathology, as well as improvements in hospital management. The web platform should be able to send a warning to the healthcare professional in cases where a patient's recorded level exceeds normal values and contain all the patient's records. The aim is to provide support to treatment, monitoring and data collection based on IoT principles, where medical devices allow communication between machines and interaction between them, sharing and managing data. The healthcare professional will have the necessary information to assess the health status of his patient and, if necessary, make some changes to improve the patient's daily routines.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Multiplatform tool for decision and clinical practice support in neonatal and paediatric care units

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)As crianças são uma população especialmente vulnerável, nomeadamente no que diz respeito à administração de medicamentos e necessidade de nutrição. Estima-se que os doentes neonatais e pediátricos são pelo menos três vezes mais vulneráveis a danos causados devido a eventos adversos e erros de medicação do que a população adulta. O desenvolvimento de uma plataforma que suporte os médicos pediatras no exercício das suas funções diárias, de forma a reduzir o erro médico, é o principal objetivo deste projeto. A sua necessidade foi identificada por um médico pediatra em exercício de funções no Hospital de Santo António no Porto, de forma a que falhas existentes na ferramenta em uso fossem colmatadas e ainda novas funcionalidades fossem desenvolvidas. Com a presente dissertação foi procurada ainda uma abordagem que permitisse o desenvolvimento de um canal de passagem de informação entre os médicos e a Farmácia Hospitalar, e que este sistema pudesse ser altamente escalável, sendo facilmente replicado em qualquer Instituição de Saúde. O desenvolvimento do sistema foi sempre acompanhado por um médico pediatra, sendo este testado e refinado ao longo desse período. Por fim, uma versão para testes da aplicação é lançada assim como um questionário que pretende avaliar a mesma.Children are a particular vulnerable population, namely when it comes to drug’s administration and nutricional needs. It is estimated that neonatal and pediatric patients are three times more vulnerable to damage from adversal events and medication errors than the adult population. The main objective of this project is the development of a platform that supports the pediatrician in their daily functions, in order to reduce the medical error. This need was identified by a pediatrician working in the Hospital de Santo António, in Porto, to improve the tool’s weaknesses and develop new funcionalities. In this dissertation was also searched an approach that would allow the development of a link between the doctors and the hospital pharmacy, and that this system could be repicable and easily reproduced in every other health institution. The system development was supervised by a pediatrician, being tested and refined all along. At last, an aplication version to tests is launched as well as an avaliation questionnaire

A data mining study on pressure ulcers

Nurses follow well-defined guidelines in order to avoid the occurrence of pressure ulcers (pU) in patients under their care, not being always successful. This work intends to produce prediction models using Data Mining (DM) techniques in order to anticipate uP treatment. The work was conducted in the Oporto Hospital Center (CHP). For the construction of this DM study, the phases of the CRISP DM methodology were taken into account. In particular, the DM focus is to show that the time factor and frequency of interventions may influence the prediction of pU classification models. To prove this, we used a data set (containing 1339 records) where different classification techniques were applied using WEKA tool. Through the classification technique (decision tree), it was possible to create a guideline that contains all the scenarios and instructions that the professional can use in order to avoid patients to develop pU. For its construction we used the model that presented a higher percentage of sensitivity (number of positive cases correctly classified as "NO" developed pU). The conclusions were: the factors studied are good predictors of PU and the guideline obtained, through automatic techniques, can help professionals apply care to the patient more quickly.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 And by the project Deus Ex Machina: NORTE-01-0145-FEDER-000026, supported by Norte Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Data extraction and exploration tools for business intelligence

Business intelligence (BI) has undergone constant changes currently, due to the increasing emergence of new technologies, which are introduced to improve the processes inherent in decision-making in organizations. However, not all users are familiar with the tools of a typical BI system, so there is a heavy reliance on the assistance of information technology (IT) technicians in the area of data extraction and exploitation (DEE), for ad hoc analyses. In this article, we intend to analyze some DEE tools on the market and their applicability to resolve and help these user’s issues in their work environment. For this purpose, literature survey of these type of users and their requirements was done; six DEE tools were selected, analyzed, and experimented; a topology was defined to evaluate the DEE tools in order to identify the one that best applies to business data extraction and exploitation from data warehouses and data marts, associated with BI system and responds to the requirements of these users.This article is a result of the project Deus Ex Machina: NORTE-01-0145-FEDER-000026, supported by Norte Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Pervasive business intelligence in misericordias – A portuguese case study

The healthcare system is one of the main pillars of any society. However, it carries with it an enormous economic weight. Portugal, alongside with many others, is a country in economic and social restructuration and consequently, the need to increase the efficiency of resource management and services is imperative. With the proven effectiveness of Business Intelligence (BI) in many organisations, the urge to implement such tools in Healthcare arises, specifically in the healthcare of Misericórdia. In addition to the results, it presents a critical analysis of the implementation and the process followed for the development and usage of KPIs. In this work, some concepts associated with the use of BI in Misericórdias were addressed, and the architecture of the developed solution was designed. It is also important to emphasise that the solution presented is pervasive, available anywhere at any time. Through this work, it was possible to gather all the data into a single structure (Data Mart), to identify a set of aspects that can be improved and to have a generalised view of the state of operation of the organisation, as far as health care is concerned. The developed includes ten KPIs in the area of Surgery Production and Waiting List Surgery. The dashboards can be analysed in several dimensions: date, specialities, physicians, service, diagnosis, location and time.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Predictors of Interstitial Lung Disease in Mixed Connective Tissue Disease

Interstitial lung disease (ILD) frequently complicates mixed connective tissue disease (MCTD) and contributes to increased mortality. We aimed to identify predictors of ILD in MCTD patients. This is a nationwide, multicentre, retrospective study including patients with an adult-onset MCTD clinical diagnosis who met Sharp’s, Kasukawa, Alarcón-Segovia, or Kahn’s diagnostic criteria and had available chest high-resolution computed tomography (HRCT) data. Univariate and multivariate analyses were conducted. We included 57 MCTD patients, with 27 (47.4%) having ILD. Among ILD patients, 48.1% were asymptomatic, 80.0% exhibited a restrictive pattern on pulmonary function tests, and 81.5% had nonspecific interstitial pneumonia on chest HRCT. Gastroesophageal involvement (40.7% vs. 16.7%, p = 0.043) and lymphadenopathy at disease onset (22.2% vs. 3.3%, p = 0.045) were associated with ILD. Binary logistic regression identified lymphadenopathy at disease onset (OR 19.65, 95% CI: 1.91–201.75, p = 0.012) and older age at diagnosis (OR 1.06/year, 95% CI: 1.00–1.12, p = 0.046) as independent ILD predictors, regardless of gender and gastroesophageal involvement. This study is the first to assess a Portuguese MCTD cohort. As previously reported, it confirmed the link between gastroesophageal involvement and ILD in MCTD patients. Additionally, it established that lymphadenopathy at disease onset and older age at diagnosis independently predict ILD in MCTD patients