A Systematic Evaluation of Literature on Internet of Things (IoT) and Smart Technologies with Multiple Dimensions

The advent of state of the art advanced technologies is necessitated by the ever-increasing onset and infiltration of our lives by the smart devices and gadgets for providing an array of services. The conventional methods and techniques already becoming obsolete and the consistent and persistent demand for provision of high end services with a greater degree of accuracy by various sectors, paves the way for collaboration of smart technologies such as Internet of things, Internet of everything, Internet of Vehicles etc. with the smart gadgets and devices. This systematic review tries to explore the avenues for research and multiple streaming of segments by the analysis of allied smart systems comprising of smart devices and multi-dimensional IoT, IoE, IoV etc.&nbsp

System Integration for Medical Data Dissemination and Multimedia Communication in the Implementation of Tele-ECG and Teleconsultation

One of the options to extend medical services coverage is deploying a telemedicine system, where medical personnel make use of ICT (Information and Communication Technology) to overcome distance and time constraints. The implementation of telemedicine systems in Indonesia faces challenges posed by the lack of ICT infrastructure availability, such as communication networks, data centres, and other computing resources. To deal with these challenges, a telemedicine innovation needs to produce a modular and flexible system that is adaptive to medical services needed and the available ICT infrastructure. This paper presents research and development of a telemedicine system prototype for tele-electrocardiography (tele-ECG) and teleconsultation. The contributions offered are integrating system from various open-source modules and the system operational feasibility based on its function and performance. The research is conducted on a testbed which represents various components involved in the telemedicine system operation. Experiments are carried out to assess the system functionality and observe whether tele-ECG and teleconsultation reach their expected performance. Experiment results show that the system works properly and recommend several multimedia communication modes to achieve the target quality based on the available network bandwidth

Implementing an institutional change in a regional context : A case study of telemedicine implementation and diffusion in Agder

Master's thesis Information Systems IS501 - University of Agder 2018High life expectancy, aging population and an increasing chronic diseases generates new challenges for the current global healthcare (Brouard, Bardo, Vignot, Bonnet, & Vignot, 2014). Public agencies and business corporates have shown interest inadvances technological research and innovation projects to remedy the problem. Innovating in public sector is highly challengingand results shouldn’t be taken for granted (Dacin, Goodstein, & Scott, 2002). Further, there is a lack of understanding how public innovations are created (Sørensen & Torfing, 2011). Theoretically the thesis builds on two strands of theories: institutional entrepreneurship and eHealth. This thesis builds on theassumption that if disposing enough knowledge on institutional entrepreneurship work, public entrepreneurs may be able to plan, organize and facilitate project success. This thesis focuses on “change agents” who initiate divergent changes defined as changes that break the institutional status quo in a field of activity and thereby possibly contribute to transforming existing institutions or creating new ones’(Battilana, Leca, & Boxenbaum, 2009). To investigate how public innovations are created, thefollowing research question was formulated:What are the critical factors enabling institutional entrepreneurs to create and sustain a technological innovation project in regional healthcare context?Built on a qualitative case study research, a total of 11in-depth,thematic interviews were conducted with actors involved in developing a common telemedicine solution for Agder. The results show how challengingit is to implement change in established organizations. Empirical data indicate that the process of implementing institutional change in a regional context involves a multi-dimensional process of institutional entrepreneurship work including political work, technical work and cultural work. Further, the findingsreveal that stakeholder management and a predisposed organizational structure and capabilities are critical factors enabling successful institutional change in large scale health projects. This research adds to the existing institutional entrepreneurship literature by suggesting toaddstakeholder management.Keywords: Institutional change, institutional entrepreneurship, institutional entrepreneurship work, innovation inpublic sector, eHealth, telemedicin

Systematic Review about QoS and QoE in Telemedicine and eHealth Services and Applications

International audienc

Aplicación de Técnicas de Machine Learning en la Predicción de Hospitalizaciones y Reingresos de pacientes con Esquizofrenia en Castilla y León

Schizophrenia is a severe mental disorder characterized by symptoms such as hallucinations, delusions, thought and behavior disorders. People with schizophrenia are associated with an increased risk of substance abuse, suicide, and mortality compared to the general population. They present hospitalization rates of 20-40% in a year, which results in high costs in the health system and affects the life quality of patients and family members. In Spain, hospital stay accounts for 37.6% of total healthcare costs. The use of Machine Learning (ML) techniques makes it possible to analyze data patterns using statistical methods and to create models that learn and generalize the behavior of the data. In Castilla y León (CyL), reducing the number of hospitalizations and readmissions is of great importance for psychiatric services. Therefore, in this Doctoral Thesis it is hypothesized that the application of ML algorithms helps to identify risk factors for hospitalization and predict readmission of patients with schizophrenia. Consequently, the main objective of this research is to develop and evaluate new predictive models using ML algorithms, in order to help in the prediction of hospitalizations and readmissions of patients with schizophrenia in CyL. To achieve this objective, 11,126 administrative records were used, corresponding to 5,412 hospitalized patients with schizophrenia from 11 public hospitals in CyL, in two different time periods. The records are global data, not based on the clinical psychopathology of the patient; they include demographic information, characteristics of hospitalization episodes, diagnoses and procedures concerning the hospitalized patient. These records were automatically analyzed using ML classification techniques, and predictive models were created to predict the readmission risk of these patients. In this sense, a methodological approach was proposed where a preprocessing and feature selection phase is applied where the predictive variables of the research were determined. The cross-validation method was used in the validation of the models and the ROC curves for their interpretation. Finally, a web application was developed to transfer the main contribution of this Doctoral Thesis to clinical practice. The different models created based on their performance metrics were compared, and the Random Forest (RF) algorithm was found to be the best predictor of the readmission risk of patients with schizophrenia in CyL. This RF model achieved an accuracy of 0.817 and an area under the ROC curve (AUC) of 0.879. These values suggest that the model has a reasonable discrimination capacity to predict the readmission of these patients. Variables such as age, length of stay, V-code diagnoses, substance abuse, and mental disorders were identified as the most predictive variables of the model. These variables indicate possible risk factors associated with the readmission of patients with schizophrenia. Therefore, the results obtained in this Doctoral Thesis suggest that ML algorithms such as RF have the ability to learn complex features from the data and predict the risk of readmission of hospitalized patients with schizophrenia in CyL. It is considered that the developed models can help decision-making, improving the quality of patient care and developing preventive treatments in function of reducing the number of hospitalizations. In addition, the implementation of the web application developed in this research, in public hospitals in CyL, can be very useful to health personnel in terms of reducing the costs associated with these hospitalizations.La esquizofrenia es un trastorno mental grave que se caracteriza por síntomas como las alucinaciones, delirios, trastornos del pensamiento y la conducta. Las personas con esquizofrenia se asocian con un mayor riesgo de abuso de sustancias, suicidio y mortalidad en comparación con la población general. Presentan tasas de hospitalización de un 20-40% en un año, lo que deriva en altos costes en el sistema sanitario y afecta la calidad de vida de los pacientes y los familiares. En España, la estancia hospitalaria corresponde al 37.6% de los costes sanitarios totales. El uso de técnicas de Machine Learning (ML), permite analizar patrones de los datos mediante métodos estadísticos, y crear modelos que aprenden y generalizan el comportamiento de los datos. En Castilla y León (CyL), reducir el número de hospitalizaciones y de reingresos es de suma importancia para los servicios de psiquiatría. Por tanto, en esta Tesis Doctoral se plantea la hipótesis que la aplicación de algoritmos de ML ayuda a identificar los factores de riesgo de hospitalización y predecir el reingreso de pacientes con esquizofrenia. En consecuencia, el objetivo principal de esta investigación es desarrollar y evaluar nuevos modelos predictivos utilizando algoritmos de ML, con el fin de ayudar en la predicción de hospitalizaciones y reingresos de pacientes con esquizofrenia en CyL. Para alcanzar este objetivo, se utilizaron 11 126 registros administrativos que corresponden a 5 412 pacientes hospitalizados con esquizofrenia, de 11 hospitales públicos de CyL, en dos períodos de tiempo diferentes. Los registros son datos globales, no están basados en la psicopatología clínica del paciente; incluyen información demográfica, características de episodios de hospitalización, diagnósticos y procedimientos referentes al paciente hospitalizado. Estos registros se analizaron automáticamente utilizando técnicas de clasificación de ML, y se crearon modelos predictivos para predecir el riesgo de reingreso de estos pacientes. En este sentido, se propuso un enfoque metodológico donde se aplica una fase de preprocesamiento y de selección de características donde se determinaron las variables predictivas de la investigación. El método de validación cruzada se utilizó en la validación de los modelos y las curvas ROC para su interpretación. Por último, se ha desarrollado una aplicación web que permite trasladar la principal contribución de esta Tesis Doctoral a la práctica clínica. Se compararon los diferentes modelos creados a partir de sus métricas de rendimiento, y se obtuvo que el algoritmo Random Forest (RF) es el que mejor predice el riesgo de reingreso de los pacientes con esquizofrenia en CyL. Este modelo RF alcanzó una exactitud (accuracy) de 0.817 y un área bajo la curva ROC (AUC) del 0.879. Estos valores sugieren que el modelo tiene una capacidad de discriminación razonable para predecir el reingreso de estos pacientes. Variables como la edad, la duración de la estancia, diagnósticos con códigos V, de abuso de sustancias y trastornos mentales, se identificaron como las variables más predictivas del modelo. Estas variables indican posibles factores de riesgo asociados al reingreso de pacientes con esquizofrenia. Por tanto, los resultados obtenidos en esta Tesis Doctoral sugieren que algoritmos de ML como el RF, tienen la capacidad de aprender características complejas de los datos y predecir el riesgo de reingreso de pacientes hospitalizados con esquizofrenia, en CyL. Se considera que los modelos desarrollados pueden ayudar a la toma de decisiones, mejorando la calidad de la atención al paciente y desarrollando tratamientos preventivos en función de reducir el número de hospitalizaciones. Además, la implementación de la aplicación web desarrollada en esta investigación, en los hospitales públicos de CyL, puede ser de gran utilidad al personal sanitario en función de reducir los costos asociados a estas hospitalizaciones.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione