Pervasive real-time intelligent system for tracking critical events with intensive care patients

• Autor(a) principal: Portela, Filipe
• Data de Publicação: 2014
• Outros Autores: Gago, Pedro, Santos, Manuel, Machado, José Manuel, Abelha, António, Silva, Álvaro, Fernando Rua
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/41716
• topic: Critical events; Adverse events; Intensive care units; INTCare; Real-Time data Pprocessing; Pervasive systems; Technology acceptance methodology; Tracking systems; Decision making process

Nowadays it is fundamental in critical areas as is Intensive Medicine to have intelligent systems that are able to support the decision making process (DMP) giving important information in the right moment. Some of the biggest problems faced by such systems are related both to the number and the different types of data sources present in Intensive Care Units (ICU). Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually for some others. In order to help the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system changed the way of the information is collected and presented. Taking advantage of the change of the environment and the data acquisition system, a system for critical events tracking was developed as the use of information regarding critical events to support decision making in Intensive Care Units is considered very useful. The tracking system was deployed in a particular module of INTCare – Electronic Nursing Record (ENR) and it is accessible anywhere and anytime. The system allows for the calculation of the critical events regarding five variables that are usually monitored in an ICU. Moreover, this system is composed by a grid that shows the events by type and duration, a warning system to alert the doctors and intuitive graphics that allow them to follow the patient evolution. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Methodology (TAM). This paper presents the tracking system, its interface and the results achieved with TAM.