Pervasive intelligent decision support system: technology acceptance in intensive care units

Intensive Care Units are considered a critical environment where the decision needs to be carefully taken. The real-time recognition of the condition of the patient is important to drive the decision process efficiently. In order to help the decision process, a Pervasive Intelligent Decision Support System (PIDSS) was developed. To provide a better comprehension of the acceptance of the PIDSS it is very important to assess how the users accept the system at level of usability and their importance in the Decision Making Process. This assessment was made using the four constructs proposed by the Technology Acceptance Methodology and a questionnaire-based approach guided by the Delphi Methodology. The results obtained so far show that although the users are satisfied with the offered information recognizing its importance, they demand for a faster system.Fundação para a Ciência e a Tecnologia (FCT

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance

Pervasive Business Intelligence: A New Trend in Critical Healthcare

In the field of intensive medicine, presentation of medical information is identified as a major concern for the health professionals, since it can be a great aid when it is necessary to make decisions, of varying gravity, for the patient's state. The way in which this information is presented, and especially when it is presented, may make it difficult for the intensivists within intense healthcare units to understand a patient's state in a timely fashion. Should there be a need to cross various types of clinical data from various sources, the situation worsens considerably. To support the health professional's decision-making process, the Pervasive Business Intelligence (PBI) Systems are a forthcoming field. Based on this principle, the current study approaches the way to present information about the patients, after they are received in a BI system, making them available at any place and at any time for the intensivists that may need it for the decision-making. The patient's history will, therefore, be available, allowing examination of the vital signs data, what medicine that they might need, health checks performed, among others. Then, it is of vital importance, to make these conclusions available to the health professionals every time they might need, so as to aid them in the decision-making. This study aims to make a stance by approaching the theme of PBI in Critical Healthcare. The main objective is to understand the underlying concepts and the assets of BI solutions with Pervasive characteristics. Perhaps consider it a sort of guide or a path to follow for those who wish to insert Pervasive into Business Intelligence in Healthcare area.Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013info:eu-repo/semantics/publishedVersio

Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.- Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013 . The authors would like to thank FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II

Pervasive and intelligent decision support in Intensive Medicine – the complete picture

Series : Lecture notes in computer science (LNCS), vol. 8649In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don’t make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients’ critical events and for evaluating medical scores automatically and in real-time.(undefined

Why Big Data? Towards a project assessment framework

Technological evolution and consequent increasing of society and organization information dependency was an important driver to escalation of data volume and variety. At same time, market evolution requires capability to find new paths to improve products/services, client satisfaction and to avoid cost increase associated with it. The relevance of using Big Data for different industries and knowing how to implement it, is something that raises many doubts and discussion. In a Big Data context it is important to evaluate organization needs and understanding if those needs are satisfied with Big Data capabilities, i.e., if they really need Big Data. For that reason, this paper presents a framework, namely BigDAF, capable of measuring a technological/business problem in a Big Data spectrum. This framework aims to help organizations and researchers to understand, not only the meaning of Big Data issue, but also the key factors that determines the necessity of a "Big Data investment". The framework assessment was applied using three case studies.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.FCT - Fundação para a Ciência e Tecnologia within the P roject Scope UID/CEC/00319/201

Predict hourly patient discharge probability in intensive care units using data mining

The length of stay (LOS) is an important metric to manage hospital units since a correct prevision of the LOS can contribute to reduce costs and optimize resources. This metric become more fundamental in intensive care units (ICU) where controlling patient condition and predict clinical events is very di cult. A set of experiences was made using data mining techniques in order to predict something more ambitious than LOS. Using the data provided by INTCare system it was possible to induce models with a very good sensitivity (95%) in order to predict the probability of a patient be discharged in the next hour. The results achieved also allow for predicting the bed occupancy rate in ICU for the next hour. The work done represents a novelty in this area and contributes to improve the decision making process providing new knowledge in real time.This work has been supported by FCT Fundação para a Ciência e Tecnologia in the scope of the project: PEstOE/EEI/UI0319/2014. The authors would like to thank FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II)

M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems

Currently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS have adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine post-adoption users' satisfaction and users’ intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users’ intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model’s factors. The authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed

Real-time intelligent decision support and monitoring system of critical patients

RESUMO - Os cuidados intensivos são unidades clínicas onde os sinais vitais dos doentes são monitorizados continuamente e onde é registada uma multiplicidade de parâmetros clínicos. Este trabalho apresenta como principal objetivo estudar a possibilidade e desenvolver um sistema inteligente que promova a monitorização e a criação de novo conhecimento útil para o processo de decisão fulcral para a prestação de um melhor tratamento ao doente. Este artigo apresenta os resultados obtidos, em particular o sistema desenvolvido com o propósito de monitorizar os dados clínicos e recorrer a tecnologias de data mining, para prever eventos clínicos com grande sensibilidade (90–100%), nomeadamente a probabilidade de falência orgânica, reinternamentos e sépsis.ABSTRACT - Intensive care units are places where patients’ vital signs are continuously monitored and recorded alongside a multiplicity of clinical parameters. The main goal of this work is to study and develop an intelligent system to promote new decision-making knowledge crucial to provide better treatment to the patient. This article presents the achieved goals; in particular, the system developed for monitoring the clinical data and, using data mining technologies, for predicting clinical events with great sensitivity (90–100%), including organ failure probability, readmissions, and sepsis.info:eu-repo/semantics/publishedVersio