Improving nursing practice through interoperability and intelligence

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 paper 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 user interfaces. The developed tool incorporates two main features: the real-time visualization of the data, and the storage of the patient's historic during an inpatient care episode. The storage capability allows keeping the data updated among hospital shifts. Moreover, this work also highlights the required adaptability of this platform for each health units inside a hospital center according with its needs.This work has been supported by Compete POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2013

Data mining for prediction of length of stay of cardiovascular accident inpatients

The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit.Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013