Optimization, high availability and redundancy in information communications and technology using networks and systems virtualization architectures

Mestrado em Engenharia Informática

Qualidade dos sistemas de informação no serviço de radiologia do Hospital de Curry Cabral: um estudo de caso

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

Localização óptima do futuro Hospital de Sintra: aplicação de modelos de location-allocation no planeamento de cuidados de saúde.

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Organizational culture influence on information quality - use of business intelligence systems relationship: portuguese context

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

How to improve urban mobility in Lisbon: intermodality and information and ticketing systems

Urban mobility is one of the main challenges facing urban areas due to the growing population and to traffic congestion, resulting in environmental pressures. The pathway to urban sustainable mobility involves strengthening of intermodal mobility. The integrated use of different transport modes is getting more and more important and intermodality has been mentioned as a way for public transport compete with private cars. The aim of the current dissertation is to define a set of strategies to improve urban mobility in Lisbon and by consequence reduce the environmental impacts of transports. In order to do that several intermodal practices over Europe were analysed and the transport systems of Brussels and Lisbon were studied and compared, giving special attention to intermodal systems. In the case study was gathered data from both cities in the field, by using and observing the different transport modes, and two surveys were done to the cities users. As concluded by the study, Brussels and Lisbon present significant differences. In Brussels the measures to promote intermodality are evident, while in Lisbon a lot still needs to be done. It also made clear the necessity for improvements in Lisbon’s public transports to a more intermodal passenger transport system, through integration of different transport modes and better information and ticketing system. Some of the points requiring developments are: interchanges’ waiting areas; integration of bicycle in public transport; information about correspondences with other transport modes; real-time information to passengers pre-trip and on-trip, especially in buses and trams. After the identification of the best practices in Brussels and the weaknesses in Lisbon the possibility of applying some of the practices in Brussels to Lisbon was evaluated. Brussels demonstrated to be a good example of intermodality and for that reason some of the recommendations to improve intermodal mobility in Lisbon can follow the practices in place in Brussels.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Measuring potentially avoidable hospital readmissions.

The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Information integration and its impacts on logistics performance - Using information technology and systems to facilitate information integration

Fast changing environment sets pressure on firms to share large amount of information with their customers and suppliers. The terms information integration and information sharing are essential for facilitating a smooth flow of information throughout the supply chain, and the terms are used interchangeably in research literature. By integrating and sharing information, firms want to improve their logistics performance. Firms share information with their suppliers and customers by using traditional communication methods (telephone, fax, Email, written and face-to-face contacts) and by using advanced or modern communication methods such as electronic data interchange (EDI), enterprise resource planning (ERP), web-based procurement systems, electronic trading systems and web portals. Adopting new ways of using IT is one important resource for staying competitive on the rapidly changing market (Saeed et al. 2005, 387), and an information system that provides people the information they need for performing their work, will support company performance (Boddy et al. 2005, 26). The purpose of this research has been to test and understand the relationship between information integration with key suppliers and/or customers and a firm’s logistics performance, especially when information technology (IT) and information systems (IS) are used for integrating information. Quantitative and qualitative research methods have been used to perform the research. Special attention has been paid to the scope, level and direction of information integration (Van Donk & van der Vaart 2005a). In addition, the four elements of integration (Jahre & Fabbe-Costes 2008) are closely tied to the frame of reference. The elements are integration of flows, integration of processes and activities, integration of information technologies and systems and integration of actors. The study found that information integration has a low positive relationship to operational performance and a medium positive relationship to strategic performance. The potential performance improvements found in this study vary from efficiency, delivery and quality improvements (operational) to profit, profitability or customer satisfaction improvements (strategic). The results indicate that although information integration has an impact on a firm’s logistics performance, all performance improvements have not been achieved. This study also found that the use of IT and IS have a mediocre positive relationship to information integration. Almost all case companies agreed on that the use of IT and IS could facilitate information integration and improve their logistics performance. The case companies felt that an implementation of a web portal or a data bank would benefit them - enhance their performance and increase information integration.

Interoperability for IoT Health Systems Using HL7 Standard

Internet of Things or IoT is revolutionizing the world we are living in, similarly the way Internet and the web did few decades ago. It is changing how we interact with the things surrounding us. Electronic health and remote patient monitoring are the ways of utilizing these technological improvements towards the healthcare. There are many applications of IoT in eHealth such as, it will open the gate to provide healthcare to the remote areas of the world, where healthcare through traditional hospital systems cannot be provided. To connect these new eHealth IoT systems with the existing healthcare information systems, we can use the existing interoperability standards commonly used in healthcare information systems. In this thesis we implemented an eHealth IoT system based on Health Level 7 interoperability standard for continuous data transmission. There is not much previous work done in implementing the HL7 for continuous sensor data transmission. Some of the previous work was limited to sensors which are not continuous in nature and some of it is only theatrical architecture. This thesis aims to prove that it is possible to implement an eHealth IoT system by using sensors which require continues data transmission, such as respiratory sensors, and to connect it with the existing eHealth information system semantically by using HL7 interoperability standard. This system will be beneficial in implementing eHealth IoT systems for those patients, who requires continuous healthcare personal monitoring. This includes elderly people and patients, whose health need to be monitored constantly. To implement the architecture, HL7 v2.5 is selected due to its ease of implementation and low size. We selected some open source technologies because of their open licenses and large developer community. We will also review the most efficient technology available in every layer of eHealth IoT system and will propose an efficient system.

Desenlaces clínicos del cuidado perioperatorio de reemplazos articulares según grupos relacionados de diagnóstico en un hospital de cuarto nivel

Introducción Los Grupos Relacionados de Diagnóstico (GRD) se han usado para determinar la calidad de la atención en varios sistemas de salud. Esto ha llevado a que se obtengan resultados en el mejoramiento continuo de la atención y del cuidado. El objetivo de este estudio es determinar desenlaces clínicos de los pacientes a quienes se les había realizado reemplazo de articulares según la complejidad clínica definida mediante GRD. Métodos Se realizó un estudio longitudinal descriptivo en el cual se incluyeron todos los pacientes que tuvieron cirugía de reemplazo total de hombro, cadera y rodilla entre 2012 y 2014. Se realizó la estratificación de los pacientes de acuerdo a tres niveles de complejidad dados por el sistema de GRD y se determinaron las proporciones de pacientes para las variables de estancia hospitalaria, enfermedad trombo-embólica, cardiovascular e infección del sitio operatorio. Resultados Se realizaron en total 886 reemplazos articulares de los cuales 40 (4.5%) presentaron complicaciones. Los eventos más frecuentes fueron las complicaciones coronarias, con una presencia de 2.4%. El GRD1, sin complicaciones ni comorbilidades, fue el que presentó mayor número de eventos. La estancia hospitalaria fue de 3.8 a 9.3 días para todos los reemplazos. Conclusiones Contrario a lo planteado en la hipótesis de estudio, se encontró que el primer GRD presentó el mayor número de complicaciones, lo que puede estar relacionado con el tamaño del grupo. Es necesario realizar nuevas investigaciones que soporten el uso de los GRD como herramienta para evaluar desenlaces clínicos.

An ontology-based approach for data integration in regionally interoperable healthcare systems

In order to best utilize the limited resource of medical resources, and to reduce the cost and improve the quality of medical treatment, we propose to build an interoperable regional healthcare systems among several levels of medical treatment organizations. In this paper, our approaches are as follows:(1) the ontology based approach is introduced as the methodology and technological solution for information integration; (2) the integration framework of data sharing among different organizations are proposed(3)the virtual database to realize data integration of hospital information system is established. Our methods realize the effective management and integration of the medical workflow and the mass information in the interoperable regional healthcare system. Furthermore, this research provides the interoperable regional healthcare system with characteristic of modularization, expansibility and the stability of the system is enhanced by hierarchy structure.

The Influence of Enterprise Systems on Business and Information Technology

Business strategy is important to all organizations. Nearly all Fortune 500 firms are implementing Enterprise Resource Planning (ERP) systems to improve the execution of their business strategy and to improve integration with its information technology (IT) strategy. Successful implementation of these multi-million dollar software systems are requiring new emphasis on change management and on Business and IT strategic alignment. This paper examines business and IT strategic alignment and seeks to explore whether an ERP implementation can drive business process reengineering and business and IT strategic alignment. An overview of business strategy and strategic alignment are followed by an analysis of ERP. The “As-Is/To-Be” process model is then presented and explained as a simple, but vital tool for improving business strategy, strategic alignment, and ERP implementation success.