A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach

As traditional concept in management, decision support had a remarkable role in competitiveness or survival of organisations and following, as modern impression, nowadays business intelligence (BI) has various applications in achieving desirable decision supports. Consequently, assessing BI competencies of enterprise systems can enable decision support in firms. This paper presents a practical framework for assessing the business intelligence capabilities of enterprise systems based on a set of novel factors and utilising fuzzy analytic network process (FANP). Through this, the construct of BI competency is decomposed into three main competency parts including ‘managerial’, ‘technical’ and ‘system enabler’ sub-goals, five main factors and 26 criteria. Using this framework, the BI competency level of enterprise systems can be determined which can help the decision makers to select the enterprise system that best suits organisations’ intelligence decision support needs. In order to validate the proposed model, it is applied to a real Iranian international offshore engineering and construction company in the oil industry to select and acquire ERP system. This research provides a complete frame (factors, criteria and procedures) for firms to assess their proposed software and systems in the field of BI competencies and functions

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part II. Clinical implementation and evaluation

Patients emerging from cardiac surgery can display varying degrees of cardiovascular instability arising from potentially complex, multi-factorial and interlinked causes. Stabilization and control of the cardiovascular system are currently managed by healthcare experts using experiential knowledge, and, in some centers, manually inputted decision pathway algorithms. This paper describes a clinical trial undertaken to determine the basic functioning of a clinical decision support system (CDSS) designed and constructed by the authors to facilitate the control of the major cardiovascular components in the early post-operative phase. Part II follows Part I's description of the software and simulation testing of the CDSS, and describes the hardware setup of a patient monitoring and CDSS. The system is evaluated on three post-cardiac surgery intensive care patients whom had all undergone cardio-pulmonary bypassPeer reviewe

Implementación de un Sistema Inteligente Semiautomático para la asistencia de pacientes en unidades de cuidados críticos

Programa Oficial de Doutoramento en Ciencias da Saúde. 5007V01[Resumen] El soporte hemodinámico de los pacientes en las unidades de cuidados intensivos (UCI) de Anestesia cardiaca (Reanimación) resulta complejo por la cantidad de variables aportadas por los diversos dispositivos con escasa integración, que permiten al clínico evaluar las necesidades en la variación del tratamiento. La valoración de todos los parámetros clínicos disponibles y la ejecución de las modificaciones necesarias en las infusiones de fármacos intravenosos supone un esfuerzo de tiempo necesario que no aporta valor a la asistencia a pacientes. La integración de datos, facilitando la toma de decisiones y la ayuda en agilizar las variaciones del tratamiento puede suponer una optimización del trabajo que permita conseguir rangos de objetivos más precisos y continuos de los pacientes. El uso de los sistemas de apoyo a la decisión clínica (CDSS) para ayudar a los clínicos puede contribuir a mejorar la calidad y la eficiencia de la atención. Este trabajo describe el desarrollo y la implementación de un CDSS denominado IOSC3 (Sistema Inteligente basado en Ontologías en Cuidados Críticos), para el manejo de pacientes de la unidad de cuidados intensivos cardíacos. Este CDSS implementó un sistema basado en conocimiento experto para ofrecer consejos terapéuticos en tiempo real basados en la monitorización continua de las constantes vitales cardiovasculares de los pacientes. Cuando la propuesta es evaluada por el clínico, se determina su adecuación siendo aceptada, el sistema actúa de manera semiautomática, controlando de manera las bombas de infusión de fármacos modificando la cantidad de fármacos suministrados al paciente. IOSC3 ha sido probado en pacientes en tiempo real de la UCI del ComplejoHospitalario Universitario de Vigo. El sistema IOSC3 fue integrado y aceptado por el personal de la UCI por representar una ayuda a la toma de decisiones, ya que las recomendaciones de dosis propuestas son aceptadas en el 90% de los casos. Es visto como una herramienta útil para su trabajo diario. Será necesario seguir investigando en diferentes escenarios clínicos para ver si el sistema IOSC3 representa puntos finales más ventajosos en aspectos como la administración total de dosis, estancias más cortas o mortalidad.[Resumo] O soporte hemodinámico dos pacientes nas unidades de coidados intensivos (UCI) de Anestesia cardíaca (Reanimación) resulta complexo pola cantidade de variables achegadas polos diversos dispositivos con escasa integración, que permiten ao clínico avaliar as necesidades na variación do tratamento. A valoración de todos os parámetros clínicos dispoñibles e a execución das modificacións necesarias nas infusións de fármacos intravenosos supón un esforzo de tempo necesario que non achega valor á asistencia a pacientes. A integración de datos, facilitando a toma de decisións e a axuda en axilizar as variacións do tratamento pode supoñer unha optimización do traballo que permita conseguir rangos de obxectivos máis precisos e continuos dos pacientes. O uso dos sistemas de apoio á decisión clínica (CDSS) para axudar aos clínicos pode contribuír a mellorar a calidade e a eficiencia da atención. Este traballo describe o desenvolvemento e a implementación dun CDSS denominado IOSC3 (Sistema Intelixente baseado en Ontologías en Coidados Críticos), para o manexo de pacientes da unidade de coidados intensivos cardíacos. Este CDSS implementou un sistema baseado en coñecemento experto para ofrecer consellos terapéuticos en tempo real baseados na monitorización continua das constantes vitais cardiovasculares dos pacientes. Cando a proposta é avaliada polo clínico, determínase a súa adecuación sendo aceptada, o sistema actúa de maneira semiautomática, controlando de maneira as bombas de infusión de fármacos modificando a cantidade de fármacos fornecidos ao paciente. IOSC3 foi probado en pacientes en tempo real da UCI do Complexo Hospitalario Universitario de Vigo. O sistema IOSC3 foi integrado e aceptado polo persoal da UCI por representar unha axuda á toma de decisións, xa que as recomendacións de doses propostas son aceptadas no 90% dos casos. É visto como unha ferramenta útil para o seu traballo diario. Será necesario seguir investigando en diferentes escenarios clínicos para ver se o sistema IOSC3 representa puntos finais máis vantaxosos en aspectos como a administración total de dose, estancias máis curtas ou mortalidade.[Abstract] Hemodynamic support of patients in cardiac anesthesia (resuscitation) intensive care units (ICU) is complex due to the number of variables provided by the various devices with little integration, allowing the clinician to assess the needs for treatment variation. The assessment of all available clinical parameters and the execution of the necessary modifications in intravenous drug infusions is a time-consuming effort that does not add value to patient care. The integration of data, facilitating decision making and helping to streamline treatment variations can optimize work to achieve more accurate and continuous patient target ranges. The use of clinical decision support systems (CDSS) to assist clinicians can help improve the quality and efficiency of care. This paper describes the development and implementation of a CDSS called IOSC3 (Intelligent Ontology-based System in Critical Care), for the management of cardiac intensive care unit patients. This CDSS implemented an expert knowledge-based system to provide real-time therapeutic advice based on continuous monitoring of patients' cardiovascular vitals. When the proposal is evaluated by the clinician, its appropriateness is determined and accepted, the system acts semi-automatically, controlling the drug infusion pumps and modifying the number of drugs delivered to the patient. IOSC3 has been tested in real time on patients in the ICU of the Complejo Hospitalario Universitario de Vigo. The IOSC3 system was integrated and accepted by the ICU staff as an aid to decision making, since the proposed dosage recommendations are accepted in 90% of the cases. It is seen as a useful tool for their daily work. Further research will be needed in different clinical scenarios to see if the IOSC3 system represents more advantageous endpoints in aspects such as total dose administration, shorter stays or mortality

A Novel Engineering Approach to Modelling and Optimizing Smoking Cessation Interventions

abstract: Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.Dissertation/ThesisDoctoral Dissertation Bioengineering 201