Intelligent support system for the provision of inpatient care

Inpatient care is seen as a rigorous healthcare environment, as several daily tasks are performed to provide adequate treatment to inpatients and a minor flaw in these tasks may result in irreversible damage to patients. It is therefore required that the information related to the patient is always updated and available to all health professionals. Thus, comes up the motivation of the project described in this paper, which presents an intelligent system to support the practice of inpatient healthcare through a Web platform that allows the monitoring of patients admitted to a health facility. Thus, the developed system culminates in an application where all relevant information is gathered to monitor the different hospitalization episodes, presenting this information in a simplistic and intuitive way and alerting the professionals to the occurrence of events related to medical exams and analysis, surgical procedures, among others. This paper presents the architecture, the requirements and a SWOT analysis of the solution proposed, the main conclusions and a proposed future work.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2019

An architecture proposal for noncommunicable diseases prevention

Noncommunicable Diseases (NCDs) are a leading global health challenge, causing 41 million deaths per year. Risk factors include genetics, environmental factors, and lifestyle choices. Adopting healthy lifestyles can prevent or delay the onset of NCDs, but health misinformation can lead people to make poor decisions about their health. To combat this, it is proposed to develop an Intelligent System using Artificial Intelligence techniques to collect and analyze data from social media about health topics to combat misinformation in public health and forecast NCDs, providing guidelines to prevent their spread. Methods: A systems overall architecture is presented. An innovative and novel solution that addresses the spread of information concerning health and NCDs contributes to inform public policies and infodemic management strategies.FCT -Fundação para a Ciência e a Tecnologia(PTDC/SAU-ENF/2584/2021

An OpenEHR adoption in a portuguese healthcare facility

The quality and safety of clinical decisions depend to a large extent on the knowledge acquired by the records of health professionals. However, a traditional Electronic Health Record (EHR) has become insufficient in terms of knowledge acquisition and clinical decision support. The development of these aspects may bring marked improvements in the quality and safety of health care. The usage of open models promotes interoperability between systems, minimizing the impact of information systems on the efficient production of knowledge useful for clinical decisions. In this sense, this article describes an implementation project of a system that support the production and use of knowledge in clinical environments, based on OpenEHR two levels modelling open data approach, in a healthcare facility on the north of Portugal.he work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/202

Prediction of COVID-19 diagnosis based on openEHR artefacts

Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.This work is funded by "FCT-Fundacao para a Ciencia e Tecnologia" within the R &D Units Project Scope: UIDB/00319/2020. D.F. thanks the FundacAo para a Ciencia e Tecnologia (FCT), Portugal for the Grant 2021.06308.BD

OpenEHR modelling applied to Complementary Diagnostics Requests

Complementary Diagnostic Requests (CDRs) are required for disease identification, monitoring, and prognosis. Diagnostic tests misuse, on the other hand, can lead to negative health outcomes as well as additional costs. Inappropriate diagnostic test requests are primarily the result of a lack of interoperability between Healthcare Information Systems (HIS). On one hand, clinicians can be mislead into which test is the best option for each clinical case, on the other hand missing previous results, leads to duplication or unnecessary tests. HIS is increasingly relying on standards based on dual architecture to promote interoperability as well as the structuring and consistency of clinical and demographic data. The OpenEHR standard's duo-based architecture allows for concise modelling of archetypes and templates for a given clinical case, which was used in this study. As a result, the purpose of this research was to build an openEHR template for the CDR registration as well as the architecture of a Data Warehouse (DW) system capable of storing all of the information needed for the diagnostic test request process. Afterwards, Business Intelligence (BI) indicators was developed in order to answers the needs for test registration and execution.This work has been supported by “FCT–Fundac¸ao para a Ci ˜ encia e Tecnologia” within the R&D Units Project ˆ Scope: UIDB/00319/2020

OpenEHR modeling: improving clinical records during the COVID-19 pandemic

The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution’s existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed.This work has been supported by FCT - Fundacao para a Ciuencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Plataforma de apoio à prática de cuidados de enfermagem em contexto hospitalar

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)Nas instituições de saúde, um dos serviços mais exigentes em termos de prestação de cuidados de saúde contínuos e atenção por parte dos profissionais, é o serviço de internamento hospitalar. Aqui existe uma constante necessidade de informações atualizadas sobre o registo clínico eletrónico do paciente, através de sistemas de apoio ao ato clínico facilmente utilizáveis. Neste contexto, a presente dissertação apresenta uma nova plataforma web para a monitorização diária de pacientes, projetada para ser usada por profissionais de saúde, especialmente enfermeiros. A aplicação é baseada em React, uma Library Open Source de JavaScript, para criar User Interfaces (UI). A nova plataforma é constituída por duas principais componentes: Um quadro de enfermagem que contém informação de todos os pacientes internados, numa unidade de saúde, num determinado momento. Este quadro baseia-se em indicadores, com o principal objetivo de alertar ações futuras (Exames, Análises, Medicação, Dietas, Jejum e Cirurgias), relativamente a cada paciente. A outra componente designa-se por registo clínico de internamento e contém todas as informações sobre sobre as ações supramenciadas. De notar que cada unidade de saúde deverá adaptar a plataforma às suas principais necessidades.Hospital inpatient care compromises one of the most demanding services in health institutions when providing a careful and continuous healthcare assistance. Such demands require constant update of the patients’ electronic health record allied with support systems responsible for monitoring their clinical information. In this context, this dissertation presents a new web platform for daily monitoring of patients, designed to be used by health professionals, especially nurses. The application is based on React, an open-source JavaScript library for building UI (user interfaces). The new platform consists of two main components: a nursing frame that contains information for all inpatients in a health unit. This component is based on indicators, with the main objective of alerting future actions (Exams, Analysis, Medication, Diets, Fasting and Surgeries), in relation to each patient. The other component is called a clinical internment registry and contains all the information about the above-mentioned actions. These actions can be passed or future, allowing the consultation of the history of a certain episode of hospitalization. It should be noted that the platform must adapt to each health unit and its main needs

Engenharia de conhecimento para registos clínicos eletrónicos interoperáveis

Tese de doutoramento em Biomedical Engineerin

The impact of contingency measures on the COVID-19 reproduction rate

The SARS-CoV-2 virus had a major impact on the health of the world’s population, causing governments to take progressively more cautious measures. All of these measures took into account the pandemic situation in the region in real time, with the aim of slowing down the spread of the infection as much as possible and reducing the associated mortality. This article aims to study the impact of preventive measures on the spread of COVID-19 and the consequent impact on excess deaths. In order to obtain the results presented, Big Data techniques were used for data storage and processing. As a result it can be concluded that Gross Domestic Product (GDP) is directly proportional to the Human Development Index (HDI), Higher GDP per capita are associated with a higher number of new cases of COVID-19 and R-index is inversely proportional to the severity of the contingency measures.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. The grant of Regina Sousa is supported by the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086. Francini Hak thanks the Fundação para a Tecnologia (FCT) for the grant 2021.06230.BD

Step towards interoperability in nursing practice

Hospital inpatient care compromises one of the most demanding services in health institutions for providing a careful and continuous healthcare assistance. Such demands require a constant update of the patients' health record allied with support systems responsible for monitoring their clinical information. In this context, the problem in this study becomes a process of continuous improvement. To define the case study, it was necessary to use research tools such as questionnaires and interviews. With these techniques, it was possible to delineate the state and dimension of the problem. Subsequently, the approach and solution was established and a new web platform for the daily monitoring of patients was proposed focused on nurses. The tool incorporates a real-time data visualization, and a patient record during an inpatient care episode. Moreover, this article also highlights the required adaptability of this platform for each health unit according to needs. With this solution, it is expected to correct many of the problems detected through quantitative results.(undefined