Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system.

Desarrollo de un modelo integral de evaluación para instituciones de educación superior y análisis de su impacto en la planificación y gestión institucional

El propósito de esta tesis doctoral es el desarrollo de un modelo integral de evaluación de la gestión para instituciones de educación superior (IES), fundamentado en valorar la gestión de diferentes subsistemas que la integran, así como estudiar el impacto en la planificación y gestión institucional. Este Modelo de Evaluación Institucional fue denominado Modelo Integral de Evaluación de Gestión de las IES (MIEGIES), que incorpora la gestión de la complejidad, los aspectos gerenciales, el compromiso o responsabilidad social, los recursos, además de los procesos propios universitarios con una visión integral de la gestión. Las bases conceptuales se establecen por una revisión del contexto mundial de la educación superior, pasando por un análisis sobre evaluación y calidad en entornos universitarios. La siguiente reflexión conceptual versó sobre la gestión de la complejidad, de la gestión gerencial, de la gestión de responsabilidad social universitaria, de la gestión de los recursos y de la gestión de los procesos, seguida por un aporte sobre modelaje y modelos. Para finalizar, se presenta un resumen teórico sobre el alcance de la aplicación de ecuaciones estructurales para la validación de modelos. El desarrollo del modelo conceptual, dimensiones e indicadores, fue efectuado aplicando los principios de la metodología de sistemas suaves –SSM. Para ello, se identifica la definición raíz (DR), la razón sistémica de ser del modelo, para posteriormente desarrollar sus componentes y principios conceptuales. El modelo quedó integrado por cinco subsistemas, denominados: de la Complejidad, de la Responsabilidad Social Universitaria, Gerencial, de Procesos y de Recursos. Los subsistemas se consideran como dimensiones e indicadores para el análisis y son los agentes críticos para el funcionamiento de una IES. Los aspectos referidos a lo Epistemetodológico, comenzó por identificar el enfoque epistemológico que sustenta el abordaje metodológico escogido. A continuación se identifican los elementos clásicos que se siguieron para llevar a cabo la investigación: Alcance o profundidad, población y muestra, instrumentos de recolección de información y su validación, para finalizar con la explicación procedimental para validar el modelo MIEGIES. La población considerada para el estudio empírico de validación fueron 585 personas distribuidas entre alumnos, docentes, personal administrativo y directivos de una Universidad Pública Venezolana. La muestra calculada fue de 238 individuos, número considerado representativo de la población. La aplicación de los instrumentos diseñados y validados permitió la obtención de un conjunto de datos, a partir de los cuales se validó el modelo MIEGIES. La validación del Modelo MIGEIES parte de sugerencias conceptuales para el análisis de los datos. Para ello se identificaron las variables relevantes, que pueden ser constructos o conceptos, las variables latentes que no pueden ser medidas directamente, sino que requiere seleccionar los indicadores que mejor las representan. Se aplicó la estrategia de modelación confirmatoria de los Modelos de Ecuaciones Estructurales (SEM). Para ello se parte de un análisis descriptivo de los datos, estimando la fiabilidad. A continuación se aplica un análisis factorial exploratorio y un análisis factorial confirmatorio. Para el análisis de la significancia del modelo global y el impacto en la planificación y gestión, se consideran el análisis de coeficientes de regresión y la tabla de ANOVA asociada, la cual de manera global especifica que el modelo planteado permite explicar la relación entre las variables definidas para la evaluación de la gestión de las IES. Así mismo, se encontró que este resultado de manera global explica que en la evaluación institucional tiene mucha importancia la gestión de la calidad y las finanzas. Es de especial importancia destacar el papel que desarrolla la planificación estratégica como herramienta de gestión que permite apoyar la toma de decisiones de las organizaciones en torno al quehacer actual y al camino que deben recorrer en el futuro para adecuarse a los cambios y a las demandas que les impone el entorno. El contraste estadístico de los dos modelos ajustados, el teórico y el empírico, permitió a través de técnicas estadísticas multivariables, demostrar de manera satisfactoria, la validez y aplicación del modelo propuesto en las IES. Los resultados obtenidos permiten afirmar que se pueden estimar de manera significativa los constructos que definen la evaluación de las instituciones de educación superior mediante el modelo elaborado. En el capítulo correspondiente a Conclusiones, se presenta en una de las primeras instancias, la relación conceptual propuesta entre los procesos de evaluación de la gestión institucional y de los cinco subsistemas que la integran. Posteriormente se encuentra que los modelos de ecuaciones estructurales con base en la estrategia de modelación confirmatoria es una herramienta estadística adecuada en la validación del modelo teórico, que fue el procedimiento propuesto en el marco de la investigación. En cuanto al análisis del impacto del Modelo en la Planificación y la Gestión, se concluye que ésta es una herramienta útil para cerrar el círculo de evaluación institucional. La planificación y la evaluación institucional son procesos inherentes a la filosofía de gestión. Es por ello que se recomienda su práctica como de necesario cumplimiento en todas las instancias funcionales y operativas de las Instituciones de Educación Superior. ABSTRACT The purpose of this dissertation is the development of a comprehensive model of management evaluation for higher education institutions (HEIs), based on evaluating the management of different subsystems and study the impact on planning and institutional management. This model was named Institutional Assessment Comprehensive Evaluation Model for the Management of HEI (in Spanish, MIEGIES). The model incorporates the management of complexity, management issues, commitment and social responsibility and resources in addition to the university's own processes with a comprehensive view of management. The conceptual bases are established by a review of the global context of higher education, through analysis and quality assessment in university environments. The following conceptual discussions covered the management of complexity, management practice, management of university social responsibility, resources and processes, followed by a contribution of modeling and models. Finally, a theoretical overview of the scope of application of structural equation model (SEM) validation is presented. The development of the conceptual model, dimensions and indicators was carried out applying the principles of soft systems methodology (SSM). For this, the root definition (RD), the systemic rationale of the model, to further develop their components and conceptual principles are identified. The model was composed of five subsystems, called: Complexity, University Social Responsibility, Management, Process and Resources. The subsystems are considered as dimensions and measures for analysis and are critical agents for the functioning of HEIs. In matters relating to epistemology and methodology we began to identify the approach that underpins the research: Scope, population and sample and data collection instruments. The classic elements that were followed to conduct research are identified. It ends with the procedural explanation to validate the MIEGIES model. The population considered for the empirical validation study was composed of 585 people distributed among students, faculty, staff and authorities of a public Venezuelan university. The calculated sample was 238 individuals, number considered representative of the population. The application of designed and validated instruments allowed obtaining a data set, from which the MIEGIES model was validated. The MIGEIES Model validation is initiated by the theoretical analysis of concepts. For this purpose the relevant variables that can be concepts or constructs were identified. The latent variables cannot be measured directly, but require selecting indicators that best represent them. Confirmatory modeling strategy of Structural Equation Modeling (SEM) was applied. To do this, we start from a descriptive analysis of the data, estimating reliability. An exploratory factor analysis and a confirmatory factor analysis were applied. To analyze the significance of the overall models the analysis of regression coefficients and the associated ANOVA table are considered. This comprehensively specifies that the proposed model can explain the relationship between the variables defined for evaluating the management of HEIs. It was also found that this result comprehensively explains that for institutional evaluation quality management and finance are very important. It is especially relevant to emphasize the role developed by strategic planning as a management tool that supports the decision making of organizations around their usual activities and the way they should evolve in the future in order to adapt to changes and demands imposed by the environment. The statistical test of the two fitted models, the theoretical and the empirical, enabled through multivariate statistical techniques to demonstrate satisfactorily the validity and application of the proposed model for HEIs. The results confirm that the constructs that define the evaluation of HEIs in the developed model can be estimated. In the Conclusions section the conceptual relationship between the processes of management evaluation and the five subsystems that comprise it are shown. Subsequently, it is indicated that structural equation models based on confirmatory modeling strategy is a suitable statistical tool in validating the theoretical model, which was proposed in the framework of the research procedure. The impact of the model in Planning and Management indicates that this is a useful tool to complete the institutional assessment. Planning and institutional assessment processes are inherent in management philosophy. That is why its practice is recommended as necessary compliance in all functional and operational units of HEIs.

Use of STATCOM in wind farms with fixed-speed generators for grid code compliance

The increasing penetration of wind energy into power systems has pushed grid operators to set new requirements for this kind of generating plants in order to keep acceptable and reliable operation of the system. In addition to the low voltage ride through capability, wind farms are required to participate in voltage support, stability enhancement and power quality improvement. This paper presents a solution for wind farms with fixed-speed generators based on the use of STATCOM with braking resistor and additional series impedances, with an adequate control strategy. The focus is put on guaranteeing the grid code compliance when the wind farm faces an extensive series of grid disturbances.

Environmental risk assessment in open pit blasting operations: compliance with ISO 31010:2010

All activities of an organization involve risks that should be managed. The risk management process aids decision making by taking account of uncertainty and the possibility of future events or circumstances (intended or unintended) and their effects on agreed objectives. With that idea, new ISO Standard has been drawn up. ISO 31010 has been recently issued which provides a structured process that identifies how objectives may be affected, and analyses the risk in term of consequences and their probabilities before deciding on whether further treatment is required. In this lecture, that ISO Standard has been adapted to Open Pit Blasting Operations, focusing in Environmental effects which can be managed properly. Technique used is Fault Tree Analysis (FTA), which is applied in all possible scenarios, providing to Blasting Professionals the tools to identify, analyze and manage environmental effects in blasting operations. Also this lecture can help to minimize each effect, studying each case. This paper also can be useful to Project Managers and Occupational Health and Safety Departments (OH&S) because blasting operations can be evaluated and compared one to each other to determine the risks that should be managed in different case studies. The environmental effects studied are: ground vibrations, flyrock and air overpressure (airblast). Sometimes, blasting operations are carried out near populated areas where environmental effects may impose several limitations on the use of explosives. In those cases, where these factors approach certain limits, National Standards and Regulations have to be applied.