Der Einsatz numerischer Simulationen zur Bearbeitung klinischer Fragestellungen ist eine innovative Vorgehensweise. Im Rahmen der vorliegenden Arbeit wurde eine Methode zur Auswertung von Ergebnissen einer Finite-Elemente-Analyse zum Stentgraftverhalten konzipiert, implementiert und im Rahmen einer deutschlandweiten Benutzerstudie getestet. Für einen Vergleich unterschiedlicher Stentgraftkonfigurationen im Kontext mit dem patientenspezifischen Gefäß wurden Stentgraftbewertungsgrößen eingeführt. Hierzu gehören die Fixierungskraft und der Kontaktstatus zwischen Stentringen und Blutgefäßbestandteilen. Für eine Bereitstellung der Ergebnisgrößen im gefäßmedizinischen Arbeitsumfeld wurde eine graphische Mensch-Maschine-Schnittstelle entwickelt. Diese ermöglicht eine quantitative und qualitative Auswertung von Stentgraftbewertungsgrößen. Hierfür wurden Module zur automatisierten Auswertung von Fixierungskräften sowie zur 2D- und 3D- Ergebnisvisualisierung implementiert. Im Rahmen der Benutzerstudie wurde die Anwendung der entwickelten Methode für die Ermittlung des Einsatzpotenzials numerischer Simulationen zur Unterstützung der Stentgraftauswahl demonstriert. Im Ergebnis wurde als wesentliches Einsatzpotenzial die Festlegung eines Mindestmaßes an Überdimensionierung, die Optimierung der Schenkellänge sowie der Ver- gleich unterschiedlicher Stentgraftdesigns ermittelt. Weiterhin konnten grundlegende Anforderungen an ein System zur Generierung und Bewertung von Stentgraftkonfigurationen im klinischen Alltag definiert werden. Zu den wesentlichen Funktionen, die der Implanteur für einen Vergleich von Stentgrafts benötigt, zählen eine Übersichtskarte zu farbkodiertem Migrationsrisiko pro Stentgraft und Landungszone, die Visualisierung des Abdichtungszustandes der Stentkomponenten sowie die Darstellung von Stentgraft- und Gefäßdeformationen im 3D-Modell.

Text in English / In recent years, there have been tremendous advances in information technology, robotics, communication technology, nanotechnology, and artificial intelligence, resulting in the merging of physical, digital, and biological worlds that have come to be known as the "fourth industrial revolution”. In this context, the present study engages such technology in the green economy and to tackle the techno-economic environmental impact assessments challenges associated with floating solar system applications in the agricultural sector of South Africa. In response, this exploratory study aimed to examine the development of a Geographical Information System (GIS)-based support platform for Environmental Impact Assessment (EIA) and due-diligence analyses for future planned agricultural floating solar systems, especially with the goal to address the vast differences between the environmental impacts for land-based and water-based photovoltaic energy systems. A research gap was identified in the planning processes for implementing floating solar systems in South Africa’s agricultural sector. This inspired the development of a novel GIS-based modelling tool to assist with floating solar system type energy infrastructure planning in the renewable energy discourse. In this context, there are significant challenges and future research avenues for technical and environmental performance modelling in the new sustainable energy transformation. The present dissertation and geographical research ventured into the conceptualisation, designing and development of a software GIS-based decision support tool to assist environmental impact practitioners, project owners and landscape architects to perform environmental scoping and environmental due-diligence analysis for planned floating solar systems in the local agricultural sector. In terms of the aims and objectives of the research, this project aims at the design and development of a dedicated GIS toolset to determine the environmental feasibility around the use of floating solar systems in agricultural applications in South Africa. In this context, the research objectives of this study included the use of computational modelling and simulation techniques to theoretically determine the energy yield predictions and computing environmental impacts/offsets for future planned agricultural floating solar systems in South Africa. The toolset succeeded in determining these aspects in applications where floating solar systems would substitute Eskom grid power. The study succeeded in developing a digital GIS-based computer simulation model for floating solar systems capable of (a) predicting the anticipated energy yield, (b) calculating the environmental offsets achieved by substituting coal-fired generation by floating solar panels, (c) determining the environmental impact and land-use preservation benefits of any floating solar system, and (d) relating these metrics to water-energy-land-food (WELF) nexus parameters suitable for user project viability analysis and decision support. The research project has demonstrated how the proposed GIS toolset supports the body of geographical knowledge in the fields of Energy and Environmental Geography. The new toolset, called EIAcloudGIS, was developed to assist in solving challenges around energy and environmental sustainability analysis when planning new floating solar installations on farms in South Africa. Experiments conducted during the research showed how the geographical study in general, and the toolset in particular, succeeded in solving a real-world problem. Through the formulation and development of GIS-based computer simulation models embedded into GIS layers, this new tool practically supports the National Environmental Management Act (NEMA Act No. 107 of 1998), and in particular, associated EIA processes. The tool also simplifies and semi-automates certain aspects of environmental impact analysis processes for newly envisioned and planned floating solar installations in South Africa. / Geography / M.Sc. (Geography)

<p>The internal and external challenges construction companies face such as variability, low productivity, inefficient processes, waste, uncertainties, risks, fragmentation, adversarial contractual relationships, competition, and those resulting from internal and external challenges such as cost overruns and delays negatively affect company performance and profitability. Though research publications abound, these challenges persist, which indicates that the following gaps exist. Lean construction, process improvement, and performance improvement research have been conducted wherein improvement principles, and best practices are used to ameliorate performance issues, but several knowledge gaps exist. Few companies use these improvement principles and best practices. For those companies applying improvements, there is no established link between these improvements and performance/profitability to guide companies. Further, even when companies use improvement principles and best practices, they apply only one or two, whereas an integrated application of these improvement principles and best practices would be more effective. The other gap the author identified is the lack of strategic tools that construction companies can use to improve and manage their profitability. This thesis tried to fill the knowledge gap, at least partially, by developing a two-part excellence model for profitability improvement of construction companies. The excellence model lays out strategies that would enable companies to overcome the challenges and improve their profitability. The excellence model also gives an iterative and recursive continuous improvement model and flowchart to improve the profitability of construction companies. The researcher used high impact principles, guidelines, and concepts from the literature on organizational effectiveness, critical success factors, strategic company profitability growth enablers, process improvement, and process maturity models, performance improvement, and organizational excellence guidelines to develop the two-part excellence model.</p> <p>The author also translated the two-part excellence model into the diagnostic tool and Decision Support System (DSS) by use of process diagrams, fishbone diagrams, root cause analysis, and use of improvement principles, countermeasures and best practices at the most granular (lowest intervention) levels to do away with root causes of poor performance. The author developed the diagnostic tool and Decision Support System (DSS) in Access 2016 to serve as a strategic tool to improve and manage the profitability of construction companies. The researcher used improvement principles, and best practices from scientific and practitioner literature to develop company and project process flow diagrams, and fishbone (cause and effect) diagrams for company, department, employee, interactions and project performance for the profitability improvement, which are the engines of the diagnostic tool and DSS. The diagnostic tool and DSS use continuous improvement cycles iteratively and recursively to improve the profitability of construction companies from the current net profit of 2-3 percent to a higher value.</p>

Tillståndsprocessen för att bedriva miljöfarlig verksamhet är manuell och ineffektiv vilket hämmar svenska företag i deras klimatarbete. Geospatial information har till följd av lokaliseringsprincipen i miljöbalken en central roll inom samhällsbyggnad och dess planering för placering av investeringar. Det finns i dagsläget inget geospatialt beslutsstödsystem (SDSS) som ämnar att underlätta för verksamhetsutövare i tillståndsprocessen vid beslut som rör placering av nya investeringar i industri. Syftet med studien var att utveckla en IT-artefakt med intentionen att stödja processen samt beslutsfattande för industriföretag i skapandet av en tillståndsansökan för miljöfarlig verksamhet. Detta genom att ta fram en webbapplikation som ska fungera som ett processtöd för användaren genom att redogöra de nödvändiga stegen som ingår i en miljötillståndsansökan med fokus på de aspekter som inkluderar geospatial data och information. Målet är att artefakten i dessa steg ska fungera som ett hjälpmedel för verksamhetsutövaren att fatta strategiska beslut kring geografisk plats för nya investeringar i industri. Studien använder sig av Design Science Research Methodology (DSRM) och har hämtat in empiri genom fokusgruppsintervjuer. Arbetet resulterade i en IT-artefakt som visar att det är möjligt att implementera denna typ av lösning på problemet samt de identifierade designprinciperna som implementerades. / The permit process for conducting environmentally hazardous activities is manual and inefficient, which impedes Swedish companies in their climate action. As a result of the “location principle” in the Swedish Environmental Code, spatial information has a central role in community building and its planning for location of investments. There is currently no spatial decision support system (SDSS) that aims to make it easier for operators to make decisions regarding the location of new investments in industry during the permit process. The purpose of the study was to develop an IT artefact with the intention to support the process and decision making for industrial companies in the creation of permit applications for environmentally hazardous activities. This by developing a web application that will function as a process support for the user by describing the necessary steps that are included in an environmental permit application with a focus on the aspects that include spatial data and information. The aim is that the artifact in these steps should function as an aid for the operator to make strategic decisions about the geographical location for new investments in industry. This research uses Design Science Research Methodology (DSRM) and has obtained empirical data through focus group interviews. The work resulted in an IT artifact that proves that it is possible to implement this kind of solution to the problem and the identified design principles that were implemented.

Tesis por compendio / [ES] Los métodos multicriterio proporcionan un enfoque analítico y estructurado para la toma de decisiones en la gestión de la cadena de suministro, que permiten evaluaciones basadas en múltiples criterios, esenciales para gestionar socios comerciales sostenibles. El objetivo de esta tesis es contribuir a la gestión sostenible de la cadena de suministro desarrollando nuevos modelos y técnicas multicriterio para evaluar proveedores y clientes. Se han diseñado modelos que incorporan las preferencias empresariales para tomar decisiones colaborativas en la selección y clasificación transparente de alternativas basadas en criterios sostenibles. También se han desarrollado métodos para clasificar las alternativas en grupos ordenados y evaluar su calidad. Tanto los modelos como los métodos se han validado mediante casos empíricos y comparado con enfoques alternativos. La metodología se basa en una profunda revisión bibliográfica y en el conocimiento experto de profesionales en la cadena de suministro. Los modelos multicriterio propuestos emplean técnicas como el Analytic Hierarchy Process (AHP), la Multi-Attribute Utility Theory (MAUT) y PROMETHEE. También se han desarrollado tres algoritmos para la clasificación de alternativas (nominal y ordenada). En primer lugar, se ha propuesto un modelo multicriterio híbrido y se ha validado con datos reales para homologar y seleccionar proveedores de tecnología, así como para su priorización y clasificación. Este modelo integra métodos compensatorios (AHP, MAUT) y no compensatorios (PROMETHEE, FlowSort) en una jerarquía con criterios de sostenibilidad. La validación del modelo en un contexto real y su comparación con un modelo alternativo ha demostrado su capacidad para proporcionar información relevante y transparente en la toma de decisiones para la evaluación sostenible de proveedores de tecnología en el sector bancario. En segundo lugar, se ha diseñado un nuevo algoritmo, denominado Global Local Net Flow sorting (GLNF sorting), que clasifica alternativas en grupos ordenados a partir de los flujos netos generados en búsquedas globales y locales con PROMETHEE. Adicionalmente, se ha diseñado el algoritmo SILhouette for Sorting (SILS) para calcular un índice de calidad en las clasificaciones. Ambos algoritmos se han validado empíricamente en la segmentación de proveedores y sus resultados se han comparado con otros métodos publicados. Por una parte, GLNF sorting destaca al mejorar la discriminación entre proveedores cercanos a los perfiles limitantes de los grupos, aprovechando el nivel de similitud preferencial entre alternativas. Por otra, SILS mejora la calidad de las asignaciones y permite un análisis detallado que facilita la toma de decisiones. En tercer lugar, se ha propuesto un sistema de segmentación de clientes B2B basado en transacciones y colaboración, aplicando AHP y GLNF sorting. Validado con 8,157 clientes de una multinacional, se ha evaluado con SILS y estadística descriptiva. Comparado con K-means, el modelo genera clasificaciones más homogéneas y robustas. Esta herramienta permite a las empresas automatizar decisiones y llevar a cabo análisis detallados para mejorar las relaciones con los clientes, alineándose con sus estrategias de colaboración y enfoques de mercado. En cuarto lugar, las búsquedas globales y locales se han utilizado para proponer un algoritmo de clasificación nominal basado dos dimensiones, que proporciona una matriz estratégica muy útil para los gestores de cadena de suministro. Por último, se ha desarrollado el paquete de software PrometheeTools en R, que automatiza la aplicación de PROMETHEE, GLNF sorting y SILS para resolver problemas multicriterio de priorización y clasificación de alternativas. Este paquete se ha validado con éxito y destaca por su eficiencia en PROMETHEE con miles de alternativas. Está disponible en acceso abierto en el repositorio CRAN para su utilización por investigadores y profesionales interesados en toma de decisiones multicriterio. / [CA] Els mètodes multicriteri proporcionen un enfocament analític i estructurat per a la presa de decisions en la gestió de la cadena de subministrament, que permeten avaluacions basades en múltiples criteris, essencials per a gestionar socis comercials sostenibles. L'objectiu d'aquesta tesi és contribuir a la gestió sostenible de la cadena de subministrament desenvolupant nous models i tècniques multicriteri per a avaluar proveïdors i clients. S'han dissenyat models que incorporen les preferències empresarials per a prendre decisions col·laboratives en la selecció i classificació transparent d'alternatives basades en criteris sostenibles. També s'han desenvolupat mètodes per a classificar les alternatives en grups ordenats i avaluar-ne la qualitat. Tant els models com els mètodes s'han validat mitjançant casos empírics i comparat amb enfocaments alternatius. La metodologia es basa en una profunda revisió bibliogràfica i en el coneixement expert de professionals en la cadena de subministrament. Els models multicriteri proposats empren tècniques com ara el procés analític jeràrquic (AHP), la teoria d'utilitat multiatribut (MAUT) i PROMETHEE. També s'han desenvolupat tres algoritmes per a la classificació d'alternatives (nominal i ordenada). En primer lloc, s'ha proposat un model multicriteri híbrid i s'ha validat amb dades reals per a homologar i seleccionar proveïdors de tecnologia, així com per a la seua priorització i classificació. Aquest model integra mètodes compensatoris (AHP, MAUT) i no compensatoris (PROMETHEE, FlowSort) en una jerarquia amb criteris de sostenibilitat. La validació del model en un context real i la seua comparació amb un model alternatiu n'ha demostrat la capacitat per a proporcionar informació rellevant i transparent en la presa de decisions per a l'avaluació sostenible de proveïdors de tecnologia en el sector bancari. En segon lloc, s'ha dissenyat un nou algoritme, denominat Global Local Net Flow sorting (GLNF sorting), que classifica alternatives en grups ordenats a partir dels fluxos nets generats en cerques globals i locals amb PROMETHEE. Addicionalment, s'ha dissenyat l'algoritme SILhouette for Sorting (SILS) per a calcular un índex de qualitat en les classificacions. Ambdós algoritmes s'han validat empíricament en la segmentació de proveïdors i els seus resultats s'han comparat amb altres mètodes publicats. D'una banda, GLNF sorting destaca en millorar la discriminació entre proveïdors pròxims als perfils limitants dels grups, que aprofita el nivell de similitud preferencial entre alternatives. De l'altra, SILS millora la qualitat de les assignacions i permet una anàlisi detallada que facilita la presa de decisions. En tercer lloc, s'ha proposat un sistema de segmentació de clients B2B basat en transaccions i col·laboració, aplicant AHP i GLNF sorting. Validat amb 8,157 clients d'una multinacional, s'ha avaluat amb SILS i estadística descriptiva. Comparat amb K-means, el model genera classificacions més homogènies i robustes. Aquesta eina permet a les empreses automatitzar decisions i portar a cap anàlisis detallades per a millorar les relacions amb els clients, que s'alineen amb les seues estratègies de col·laboració i enfocaments de mercat. En quart lloc, les cerques globals i locals s'han utilitzat per a proposar un algoritme de classificació nominal basat en dues dimensions, que proporciona una matriu estratègica molt útil per als gestors de la cadena de subministrament. Finalment, s'ha desenvolupat el paquet de programari PrometheeTools en R, que automatitza l'aplicació de PROMETHEE, GLNF sorting i SILS per a resoldre problemes multicriteri de priorització i classificació d'alternatives. Aquest paquet s'ha validat amb èxit i destaca per la seua eficiència en PROMETHEE amb milers d'alternatives. Està disponible en accés obert en el repositori CRAN per a la utilització per investigadors i professionals interessats en la presa de decisions multicriteri. / [EN] Multicriteria methods provide an analytical and structured approach to decision making in supply chain management. These techniques allow multicriteria evaluations, which are essential for choosing and managing sustainable business partners. The aim of this thesis is to contribute to sustainable supply chain management by developing new multicriteria models and techniques to assess suppliers and customers. Models have been designed in order to incorporate business preferences to make collaborative decisions in the transparent selection and ranking of alternatives based on sustainable criteria. New methods have also been developed to classify alternatives into ordered groups and to assess their quality. Both models and methods have been validated using empirical cases and compared with alternative approaches. The methodology is based on an in-depth literature review, as well as the expertise of supply chain professionals. The proposed multicriteria models integrate techniques such as the Analytical Hierarchical Process (AHP), Multi-Attribute Utility Theory (MAUT) and the PROMETHEE method. Three new algorithms have also been developed for classifying alternatives into nominal and ordered groups (sorting problem). Firstly, a hybrid multicriteria model has been proposed and validated with real data for technology supplier qualifying, selection and ranking. This model integrates compensatory (AHP, MAUT) and non-compensatory (PROMETHEE, FlowSort) methods in a hierarchy with sustainability criteria to evaluate products, suppliers and manufacturers. Validation of the model in a real context and its comparison with an alternative model has demonstrated its ability to provide relevant and transparent information for decision making in the sustainable evaluation of technology suppliers in the banking sector. Secondly, a new algorithm has been designed, called Global Local Net Flow sorting (GLNF sorting), which classifies alternatives into ordered groups based on the net flows generated in global and local searches with PROMETHEE. In addition, the SILhouette for Sorting (SILS) algorithm has been designed to calculate a quality index in the classifications. Both algorithms have been empirically validated in supplier segmentation and their results compared with other published methods. On the one hand, the GLNF sorting algorithm excels in improving the discrimination between suppliers close to the limiting profiles by exploiting the level of preference similarity between alternatives. On the other, SILS improves the quality of alternative assignments to groups, allows for a detailed analysis of suppliers and facilitates decision making. Thirdly, a customer segmentation model based on transactions and collaboration has been proposed in the Business to Business context, applying AHP and GLNF sorting. Validated with 8,157 customers of a multinational company, it has been assessed by SILS and descriptive statistics. This model generates more homogeneous and robust groups than the K-means cluster method. This tool enables companies to automate decisions and perform detailed analysis to improve customer relationships, aligning with their collaboration strategies and market approaches. Fourthly, global and local searches have been used to propose an algorithm for nominal classification based on two dimensions, which provides a very useful strategic matrix for supply chain managers. Finally, the PrometheeTools software package has been developed in R, which automates the implementation of PROMETHEE, GLNF sorting and SILS to solve multicriteria problems of alternatives ranking and classification. This package has been successfully validated and stands out for the efficiency in PROMETHEE and especially when solving problems with thousands of alternatives. It is available by open access in the CRAN repository for use by researchers and practitioners interested in multicriteria decision making. / Barrera Jimenez, IF. (2024). Multicriteria Techniques for Sustainable Supply Chain Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202879 / Compendio

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can be used to improve decision making through structured decision making and adaptive management approaches at the agency scale. In CHAPTER II, my results show that the goals of inland fisheries agencies tend to be more similar than different but have expanded and diversified since the 1970s. I suggest that changes in perspectives and communication technology, as well as provisions within nationwide funding mechanisms, have led to goals becoming more homogenous across the USA and more diverse within each bureau. In CHAPTER III, I found that standardized collection and careful curation of data has allowed one inland fisheries bureau to acquire a large fish and fisheries database and that managers use this database to summarize common fish population parameters and indices, craft objectives, and set targets. The regimentation of data management and analysis has helped managers within the inland fisheries bureau to assess fish populations and fisheries efficiently and effectively across waterbodies within their districts and state. In CHAPTER IV, I extend CHAPTERS II and III to show that biological and non-biological management objectives and their associated measurable attributes and management actions can be synthesized into a common set of decision elements. I demonstrate how common decision elements enable managers to easily structure decisions and help to address common problems at the agency scale. Using a subset of common decision elements, I demonstrate how existing agency operations (e.g., monitoring) can be used to expedite learning and improve decision making for a common problem faced by managers in multiple, similar systems.

La prescription électronique, définie comme la saisie et la transmission électronique de diverses données de prescriptions (médicaments, requêtes de laboratoires, imagerie), est une technologie qui promet d’augmenter la productivité de l’exécution d’une prescription, de diminuer les erreurs reliées à l’illisibilité des prescriptions manuscrites et d’améliorer l’usage approprié des médicaments. Toutefois, la réalisation des bénéfices associés à cette technologie dépend grandement du contexte local de l’implantation et la configuration du système, qui doivent être adaptés aux besoins de l’établissement de santé et aux pratiques locales des professionnels. Bien que la prescription électronique soit implantée depuis plus d’une décennie dans plusieurs établissements de santé à travers le monde, il s’agit d’une technologie émergente au Québec et au Canada. Le Centre hospitalier universitaire (CHU) Sainte-Justine est l’un des premiers établissements de santé au Québec qui a implanté un système informatisé d’entrée d’ordonnances (SIEO) en 2019. L’outil, développé par un fournisseur local, a été adapté spécifiquement aux besoins de cet hôpital pédiatrique. Ainsi, l’objectif principal de ce mémoire est d’évaluer les effets de ce SIEO sur la sécurité de l’usage des médicaments. Plus spécifiquement, ce mémoire vise à 1) mesurer et décrire les problèmes liés à l’usage des médicaments avant et après l’implantation du SIEO, 2) identifier les caractéristiques du SIEO qui influencent la sécurité de l’usage des médicaments et 3) formuler des recommandations pour optimiser les bénéfices de l’outil de prescription électronique pour les patients et les utilisateurs. Afin de répondre à ces objectifs, ce travail présente deux études distinctes : 1. Une première analyse heuristique de l’utilisabilité portant spécifiquement sur la vulnérabilité du système a été effectuée en préimplantation du SIEO. Des scénarios visant à identifier les vulnérabilités du système ont été élaborés, puis un score permettant de noter la capacité du système à pallier ces vulnérabilités a été attribué par trois experts indépendants, afin de formuler des recommandations sur le design des fonctionnalités clés de cet outil. 2. Une étude observationnelle pré-post a été menée dans la période précédant l'implantation du système, et suivant l'implantation du système, dans l'unité pilote de pédiatrie générale. L’étude observationnelle est composée de deux volets, soit : a) une analyse des erreurs liées aux prescriptions de médicaments pour un échantillon d’ordonnances rédigées pendant une semaine par une analyse des interventions des pharmaciens et un audit de conformité des prescriptions et b) une analyse pré-post des erreurs liées au circuit du médicament, à partir des rapports d’incidents et accidents déclarés en lien avec le médicament. Les types d'erreurs ont été analysés afin de bien comprendre leur nature, ainsi que le rôle potentiel de la technologie sur la sécurité de l’usage des médicaments. Ces analyses ont été contextualisées par une description des fonctionnalités du SIEO (par l’utilisation d’outils validés pour l’évaluation des SIEO), des flux cliniques (par l’observation directe), et du projet d’implantation (par l’analyse de documents et des discussions avec les parties prenantes) afin de formuler des recommandations visant à optimiser les bénéfices du SIEO. Le premier article rapporte l'analyse de l'utilisabilité (étude 1) et des problèmes liés à la prescription de médicaments (étude 2a). Les résultats suggèrent que le système d’aide à la décision intégré au SIEO ne disposait pas de fonctionnalités recommandées pour limiter les vulnérabilités liées à l’usage de ce type d’outil. Néanmoins, les erreurs de conformité, qui représentaient la majorité des problèmes de prescription avant l’implantation ont été complètement éliminées par le nouveau SIEO. Toutefois, il n’y a pas eu de différence sur les erreurs de dosage et les autres interventions des pharmaciens. Ainsi, les résultats obtenus confirment qu’il est nécessaire de configurer un système d’aide à la décision avancé et adapté aux soins hospitaliers pédiatriques afin de réduire davantage les erreurs cliniques liées aux ordonnances de médicaments. Le deuxième article présente l’analyse des rapports d’incidents et accidents (étude 2b), et vise à estimer les effets du SIEO sur la sécurité de l'usage des médicaments, ainsi que mieux comprendre les erreurs de médicaments dans l’ensemble du processus des soins. L’article met en évidence le rôle important de la prescription électronique dans la simplification des étapes de la relève, de la transmission et de la transcription de la prescription. De plus, l'amélioration de l’utilisabilité de la feuille d’administration des médicaments électronique (FADMe) pourrait contribuer à réduire davantage le nombre d'erreurs liées au médicament. Ces deux articles permettent d’explorer les liens entre les caractéristiques du SIEO et les effets sur la sécurité de l’usage des médicaments, durant l’étape de prescription spécifiquement ainsi qu’à travers l’entièreté du circuit du médicament. Des recommandations sur l’utilisabilité du système et des stratégies de prévention sont présentées afin de réduire les erreurs liées au médicament. / Computerized provider order entry (CPOE), defined as a system used for entering and transmitting orders (e.g., for drugs, imaging, or lab requests) electronically, is a technology that can increase the productivity of order dispensing, reduce errors related to the illegibility of handwritten prescriptions and increase the appropriate use of medication. However, achieving the benefits associated with this technology depends on the local context of the implementation and configuration of the system, which must be adapted to the needs of the healthcare institution and the local practices of the healthcare professionals. Although CPOEs have been implemented for more than a decade in many healthcare institutions worldwide, it is an emerging technology in Quebec and Canada. The Centre hospitalier universitaire (CHU) Sainte-Justine is one of the first healthcare institutions in Quebec to implement a CPOE system in 2019. The CPOE, which was developed by a local vendor, was tailored specifically to meet the needs of the CHU Sainte-Justine's pediatric inpatient population. Thus, this study aims to evaluate the effects of the CPOE on medication safety. More specifically, this study seeks to 1) measure and describe problems related to medication use before and after the implementation of the CPOE, 2) identify the characteristics of the CPOE that influence medication safety, and 3) provide recommendations to optimize the benefits of the CPOE for patients and users. To address these objectives, two studies were conducted: 1. An expert-based heuristic vulnerability analysis of the system was performed to analyze the usability of the CPOE in the pre-implementation phase. Scenarios to identify system vulnerabilities were developed, and a score to rate the CPOE's ability to address these vulnerabilities was assigned by three independent experts to make recommendations on the design of the CPOE's key features. 2. A pre-post observational study was conducted prior to and following the CPOE implementation in the general pediatrics unit. The observational study included two components: a) An analysis of medication orders problems for a sample of prescriptions ordered for one week through the documentation of pharmacists’ interventions and a prescription conformity audit; b) An analysis of medication-related incident and accident reports throughout the year in pre and post implementation. The types of errors were described to understand their nature, as well as the potential role of technology on the safety of medication use. The analyses were contextualized with descriptions of the CPOE features (through the use of validated tools for CPOE evaluation), clinical workflows (through direct observation) and implementation project (through secondary document analysis and discussions with stakeholders) in order to make recommendations to improve medication safety. The first article covers the vulnerability analysis (study 1) and the medication orders problems at the prescribing step (study 2a). The results show that the clinical decision support system (CDSS) integrated into the CPOE lacked the recommended features to identify pediatric order errors. Conformity errors, which accounted for most prescribing errors, were completely eliminated by the prescriber implementation. However, there was no difference in dosing errors and other pharmacist interventions. Thus, the results obtained from these two components suggest the need to configure an advanced CDSS tailored to pediatric hospital care to further reduce clinical errors. The second article, focused on the analysis of incident and accident reports (study 2b), aims to estimate the impacts of the electronic prescriber on medication safety, as well as to better understand medication errors in the overall care process. The article highlights the importance of simplifying the acknowledgment, transmission, and transcription steps by implementing a CPOE. Improving the usability of the electronic medication administration record (eMAR) could further reduce medication errors. These two articles explore the relationship between the characteristics of the CPOE and their impact on medication safety, specifically at the prescribing step and throughout the entire medication management process. Recommendations on system usability and other prevention strategies are presented to improve medication safety.

[ES] El síndrome metabólico se define como un conjunto de trastornos (e.g., niveles elevados de presión arterial, niveles elevados de glucosa en sangre, exceso de grasa abdominal o niveles elevados de colesterol o triglicéridos) que afectan a un individuo al mismo tiempo. La presencia de uno de estos factores no implica un riesgo elevado para la salud, sin embargo, presentar varios de ellos aumenta la probabilidad de sufrir enfermedades secundarias como la enfermedad cardiovascular o la diabetes tipo II. Las enfermedades difusas hepáticas son todas aquellas enfermedades que afectan a las células funcionales del hígado, los hepatocitos, alterando, de este modo, la función hepática. En estos procesos, los hepatocitos se ven sustituidos por adipocitos y tejido fibroso. La enfermedad de hígado graso no alcohólico es una afección reversible originada por la acumulación de triglicéridos en los hepatocitos. El alcoholismo, la obesidad, y la diabetes son las causas más comunes de esta enfermedad. Este estado del hígado es reversible si se cambia la dieta del paciente, sin embargo, si este no se cuida, la enfermedad puede ir avanzando hacia estadios más severos, desencadenando fibrosis, cirrosis e incluso carcinoma hepatocelular (CHC). La temprana detección de todos estos procesos es de gran importancia en la mejora del pronóstico de los pacientes. Así, las técnicas de imagen en combinación con modelos computacionales permiten caracterizar el tejido mediante la extracción de parámetros objetivos, conocidos como biomarcadores de imagen, relacionados con estos procesos fisiológicos y patológicos, permitiendo una estadificación más precisa de las enfermedades. Además, gracias a las técnicas de inteligencia artificial, se pueden desarrollar algoritmos de segmentación automática que permitan realizar dicha caracterización de manera completamente automática y acelerar, de este modo, el flujo radiológico. Por todo esto, en la presente tesis doctoral, se presenta una metodología para el desarrollo de modelos de segmentación y cuantificación automática, siendo aplicada a tres casos de uso. Para el estudio del síndrome metabólico se propone un método de segmentación automática de la grasa visceral y subcutánea en imágenes de tomografía computarizada (TC), para el estudio de la enfermedad hepática difusa se propone un método de segmentación hepática y cuantificación de la grasa y hierro hepáticos en imágenes de resonancia magnética (RM), y, finalmente, para el estudio del CHC, se propone un método de segmentación hepática y cuantificación de los descriptores de la curva de perfusión en imágenes de RM. Todo esto se ha integrado en una plataforma que permite su integración en la práctica clínica. Así, se han adaptado los algoritmos desarrollados para ser ejecutados en contenedores Docker de forma que, dada una imagen de entrada, generen los parámetros cuantitativos de salida junto con un informe que resuma dichos resultados; se han implementado herramientas para que los usuarios puedan interactuar con las segmentaciones generadas por los algoritmos de segmentación automática desarrollados; finalmente, éstos se han implementado de forma que generen dichas segmentaciones en formatos estándar como DICOM RT Struct o DICOM Seg, para garantizar la interoperabilidad con el resto de sistemas sanitarios. / [CA] La síndrome metabòlica es defineix com un conjunt de trastorns (e.g., nivells elevats de pressió arterial, nivells elevats de glucosa en sang, excés de greix abdominal o nivells elevats de colesterol o triglicèrids) que afecten un individu al mateix temps. La presència d'un d'aquests factors no implica un risc elevat per a la salut, no obstant això, presentar diversos d'ells augmenta la probabilitat de patir malalties secundàries com la malaltia cardiovascular o la diabetis tipus II. Les malalties difuses hepàtiques són totes aquelles malalties que afecten les cèl·lules funcionals del fetge, els hepatòcits, alterant, d'aquesta manera, la funció hepàtica. En aquests processos, els hepatòcits es veuen substituïts per adipòcits i teixit fibrós. La malaltia de fetge gras no alcohòlic és una afecció reversible originada per l'acumulació de triglicèrids en els hepatòcits. L'alcoholisme, l'obesitat, i la diabetis són les causes més comunes d'aquesta malaltia. Aquest estat del fetge és reversible si es canvia la dieta del pacient, no obstant això, si aquest no es cuida, la malaltia pot anar avançant cap a estadis més severs, desencadenant fibrosis, cirrosis i fins i tot carcinoma hepatocel·lular (CHC). La primerenca detecció de tots aquests processos és de gran importància en la millora del pronòstic dels pacients. Així, les tècniques d'imatge en combinació amb models computacionals permeten caracteritzar el teixit mitjançant l'extracció paràmetres objectius, coneguts com biomarcadores d'imatge, relacionats amb aquests processos fisiològics i patològics, permetent una estratificació més precisa de les malalties. A més, gràcies a les tècniques d'intel·ligència artificial, es poden desenvolupar algorismes de segmentació automàtica que permeten realitzar aquesta caracterització de manera completament automàtica i accelerar, d'aquesta manera, el flux radiològic. Per tot això, en la present tesi doctoral, es presenta una metodologia per al desenvolupament de models de segmentació i quantificació automàtica, sent aplicada a tres casos d'ús. Per a l'estudi de la síndrome metabòlica es proposa un mètode de segmentació automàtica del greix visceral i subcutani en imatges de tomografia computada (TC), per a l'estudi de la malaltia hepàtica difusa es proposa un mètode segmentació hepàtica i quantificació del greix i ferro hepàtics en imatges de ressonància magnètica (RM), i, finalment, per a l'estudi del CHC, es proposa un mètode de segmentació hepàtica i quantificació dels descriptors de la corba de perfusió en imatges de RM. Tot això s'ha integrat en una plataforma que permet la seua integració en la pràctica clínica. Així, s'han adaptat els algorismes desenvolupats per a ser executats en contenidors Docker de manera que, donada una imatge d'entrada, generen els paràmetres quantitatius d'eixida juntament amb un informe que resumisca aquests resultats; s'han implementat eines perquè els usuaris puguen interactuar amb les segmentacions generades pels algorismes de segmentació automàtica desenvolupats; finalment, aquests s'han implementat de manera que generen aquestes segmentacions en formats estàndard com DICOM RT Struct o DICOM Seg, per a garantir la interoperabilitat amb la resta de sistemes sanitaris. / [EN] Metabolic syndrome is defined as a group of disorders (e.g., high blood pressure, high blood glucose levels, excess abdominal fat, or high cholesterol or triglyceride levels) that affect an individual at the same time. The presence of one of these factors does not imply an elevated health risk; however, having several of them increases the probability of secondary diseases such as cardiovascular disease or type II diabetes. Diffuse liver diseases are all those diseases that affect the functional cells of the liver, the hepatocytes, thus altering liver function. In these processes, the hepatocytes are replaced by adipocytes and fibrous tissue. Non-alcoholic fatty liver disease is a reversible condition caused by the accumulation of triglycerides in hepatocytes. Alcoholism, obesity, and diabetes are the most common causes of this disease. This liver condition is reversible if the patient's diet is changed; however, if the patient is not cared for, the disease can progress to more severe stages, triggering fibrosis, cirrhosis and even hepatocellular carcinoma (HCC). Early detection of all these processes is of great importance in improving patient prognosis. Thus, imaging techniques in combination with computational models allow tissue characterization by extracting objective parameters, known as imaging biomarkers, related to these physiological and pathological processes, allowing a more accurate statification of diseases. Moreover, thanks to artificial intelligence techniques, it is possible to develop automatic segmentation algorithms that allow to perform such characterization in a fully automatic way and thus accelerate the radiological workflow. Therefore, in this PhD, a methodology for the development of automatic segmentation and quantification models is presented and applied to three use cases. For the study of metabolic syndrome, a method of automatic segmentation of visceral and subcutaneous fat in computed tomography (CT) images is proposed; for the study of diffuse liver disease, a method of liver segmentation and quantification of hepatic fat and iron in magnetic resonance imaging (MRI) is proposed; and, finally, for the study of HCC, a method of liver segmentation and quantification of perfusion curve descriptors in MRI is proposed. All this has been integrated into a platform that allows its integration into clinical practice. Thus, the developed algorithms have been adapted to be executed in Docker containers so that, given an input image, they generate the quantitative output parameters together with a report summarizing these results; tools have been implemented so that users can interact with the segmentations generated by the automatic segmentation algorithms developed; finally, these have been implemented so that they generate these segmentations in standard formats such as DICOM RT Struct or DICOM Seg, to ensure interoperability with other health systems. / Jimenez Pastor, AM. (2023). Aprendizaje profundo y biomarcadores de imagen en el estudio de enfermedades metabólicas y hepáticas a partir de resonancia magnética y tomografía computarizada [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202602

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection.

<p dir="ltr">Axial spondyloarthritis (axSpA), a disease that due to its complexity and rarity, presents challenges in diagnosis. With a focus on integrating expert knowledge into an intelligent diagnostic system, the research explores the intricate nature of axSpA, emphasizing the challenges associated with its diverse clinical presentation. By leveraging a comprehensive knowledge base curated by domain experts, encompassing insights into pathophysiology, genetic factors, and evolving diagnostic criteria of axSpA, the expert system strives to provide a nuanced understanding of the disease. The methodology employs a hybrid reasoning approach, combining both forward and backward chaining techniques. Forward chaining iteratively processes clinical data and available evidence, applying logical rules to infer potential diagnoses and refine hypotheses, while backward chaining starts with the desired diagnostic goal and works backward through the knowledge base to validate or refute hypotheses. Additionally, certainty theory is incorporated to manage uncertainty in the diagnostic process, assigning confidence levels to conclusions based on the strength of evidence and expert knowledge. By integrating knowledge base, forward and backward chaining, and certainty theory, the research aims to enhance diagnostic precision for this less common yet impactful inflammatory rheumatic condition, emphasizing the importance of early and accurate identification for effective management and improved patient outcomes. The results indicate a significant improvement in diagnostic accuracy, sensitivity, and specificity compared to traditional methods. The system's potential to enhance early diagnosis and treatment outcomes is discussed, along with suggestions for future research to further refine and expand the system.</p>