Designing for Risk Assessment Systems for Patient Triage in Primary Health Care:A Literature Review

Background: This literature review covers original journal papers published between 2011 and 2015. These papers review the current status of research on the application of human factors and ergonomics in risk assessment systems’ design to cope with the complexity, singularity, and danger in patient triage in primary health care. Objective: This paper presents a systematic literature review that aims to identify, analyze, and interpret the application of available evidence from human factors and ergonomics to the design of tools, devices, and work processes to support risk assessment in the context of health care. Methods: Electronic search was performed on 7 bibliographic databases of health sciences, engineering, and computer sciences disciplines. The quality and suitability of primary studies were evaluated, and selected papers were classified according to 4 classes of outcomes. Results: A total of 1845 papers were retrieved by the initial search, culminating in 16 selected for data extraction after the application of inclusion and exclusion criteria and quality and suitability evaluation. Conclusions: Results point out that the study of the implications of the lack of understanding about real work performance in designing for risk assessment in health care is very specific, little explored, and mostly focused on the development of tool

Methodologies of Legacy Clinical Decision Support System -A Review

Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

This paper aims to develop a novel model to assess the risk factors of maritime supply chains by incorporating a fuzzy belief rule approach with Bayesian networks. The new model, compared to traditional risk analysis methods, has the capability of improving result accuracy under a high uncertainty in risk data. A real case of a world leading container shipping company is investigated, and the research results reveal that among the most significant risk factors are transportation of dangerous goods, fluctuation of fuel price, fierce competition, unattractive markets, and change of exchange rates in sequence. Such findings will provide useful insights for accident prevention

Information technologies for pain management

Millions of people around the world suffer from pain, acute or chronic and this raises the importance of its screening, assessment and treatment. The importance of pain is attested by the fact that it is considered the fifth vital sign for indicating basic bodily functions, health and quality of life, together with the four other vital signs: blood pressure, body temperature, pulse rate and respiratory rate. However, while these four signals represent an objective physical parameter, the occurrence of pain expresses an emotional status that happens inside the mind of each individual and therefore, is highly subjective that makes difficult its management and evaluation. For this reason, the self-report of pain is considered the most accurate pain assessment method wherein patients should be asked to periodically rate their pain severity and related symptoms. Thus, in the last years computerised systems based on mobile and web technologies are becoming increasingly used to enable patients to report their pain which lead to the development of electronic pain diaries (ED). This approach may provide to health care professionals (HCP) and patients the ability to interact with the system anywhere and at anytime thoroughly changes the coordinates of time and place and offers invaluable opportunities to the healthcare delivery. However, most of these systems were designed to interact directly to patients without presence of a healthcare professional or without evidence of reliability and accuracy. In fact, the observation of the existing systems revealed lack of integration with mobile devices, limited use of web-based interfaces and reduced interaction with patients in terms of obtaining and viewing information. In addition, the reliability and accuracy of computerised systems for pain management are rarely proved or their effects on HCP and patients outcomes remain understudied. This thesis is focused on technology for pain management and aims to propose a monitoring system which includes ubiquitous interfaces specifically oriented to either patients or HCP using mobile devices and Internet so as to allow decisions based on the knowledge obtained from the analysis of the collected data. With the interoperability and cloud computing technologies in mind this system uses web services (WS) to manage data which are stored in a Personal Health Record (PHR). A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness of the proposed computerised monitoring system. The six weeks RCT evidenced the advantages provided by the ubiquitous access to HCP and patients so as to they were able to interact with the system anywhere and at anytime using WS to send and receive data. In addition, the collected data were stored in a PHR which offers integrity and security as well as permanent on line accessibility to both patients and HCP. The study evidenced not only that the majority of participants recommend the system, but also that they recognize it suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented treatments with reduced therapist time. The study also revealed that the guidance of HCP at the beginning of the monitoring is crucial to patients' satisfaction and experience stemming from the usage of the system as evidenced by the high correlation between the recommendation of the application, and it suitability to improve pain management and to provide medical information. There were no significant differences regarding to improvements in the quality of pain treatment between intervention group and control group. Based on the data collected during the RCT a clinical decision support system (CDSS) was developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer) with an imputation model based on linear regression. This system resulted in fully accuracy related to decisions suggested by the system compared with the medical diagnosis, and therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems capability to deal with different complex data sources with varied complexities and accuracies, an innovative model was proposed. This model is characterized by a qualitative analysis stemming from the data fusion method combined with a quantitative model based on the comparison of the standard deviation together with the values of mathematical expectations. This model aimed to compare the effects of technological and pen-and-paper systems when applied to different dimension of pain, such as: pain intensity, anxiety, catastrophizing, depression, disability and interference. It was observed that pen-and-paper and technology produced equivalent effects in anxiety, depression, interference and pain intensity. On the contrary, technology evidenced favourable effects in terms of catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy to implement and low time and resources consuming. Further work is needed to evaluate the proposed system to follow up participants for longer periods of time which includes a complementary RCT encompassing patients with chronic pain symptoms. Finally, additional studies should be addressed to determine the economic effects not only to patients but also to the healthcare system

A belief rule-based decision support system for clinical risk assessment of cardiac chest pain

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases. (C) 2011 Elsevier BY. All rights reserved.ManagementOperations Research & Management ScienceSCI(E)EI0ARTICLE3564-57321

Clinical Decision Support Systems (CDSS) applications in psychological suitability assessments.

Psychological suitability assessments are an integral component of public safety recruitment and selection processes. Psychological suitability assessments can benefit from the implementation of decision support systems. Providing an estimate of the likelihood for outcomes can act to support psychologists in decision making processes. This thesis critiques a content focused approach to psychological suitability assessment, develops a classification algorithm that estimates profile outcome likelihoods of several possible psychological suitability decision categories, and assesses the effect of providing estimates to psychologists on psychological decision making processes when determining profile outcomes. The critique outlines that the assessment model dimensions were developed to suit the needs of the organization at the time and connected psychological data with anticipated behaviours and activities. However, the model was not developed within a systematic process and relied heavily on subjective accounts of on duty officers. The assessment model could be improved with the implementation of new and updated tools, such as the MMPI-2RF or the MPULSE. The classification algorithm established by the discriminant function analysis accurately classified 86.75% of cases (p0.05). Experience conducting these types of assessments was a significant covariate for the time it took to render a decision, and the time it took to render a decision was not affected by the presentation of a likelihood estimate. Overall, this study suggests that decision support systems can be implemented to support a temporary clinical lead in navigating the psychological data such that candidates are evaluated more consistently for psychological suitability evaluations. Implementing a decision support system in practice can act as a guide for interpreting psychological data, reduce the error of both experienced and inexperienced assessors, and improve the integrity of the assessment.Master of Arts (MA) in Psycholog

A process model for quality in use evaluation on clinical decision support systems

Developing or purchasing software is an expensive investment and needs to be justified. Furthermore, the software must be useful in its purpose, reliable, efficient and, among other characteristics, meet the expectations of users [1, 2]. It would be no different in the case of a clinical Decision Support System - CDSS. CDSS are systems developed to support clinicians and other health professionals in a medical decision making [3]. They are developed within a clinical context, following medical guidelines, with varied purposes such as diagnoses [4, 5, 6] patient monitoring [7, 8, 9], prevention [10] and disease treatment [11, 12]. Conversely, even with all the benefits offered by a CDSS, its acceptance in the medical field is still a matter of debate [13, 14]. The CDSS acceptance is linked to the perception of the end user, such as 1) the system’s ease of use and utility, 2) the quality of its results and its reliability [14], 3) the contextual accessibility of the system, sometimes not included in the health professional’s routine and workflow, and 4) the fact that numerous CDSSs are not integrated with existing systems [15]. One manner to extend the use and disseminate positive contributions of CDSSs to the medical world is to develop them in a reliable and useful way. For this, one must follow the best practices of software engineering (SE, acronym in English) [16] and be concerned with its quality, both in the design and development process and in its effective use. Evaluating the quality of the software is to measure its characteristics and sub-characteristics of quality. In order to better structure the assessment, a series of international standards, with models and frameworks, were developed for assisting software developers in assessing the quality of software products. The latest series is the ISO/IEC 25000 - System and Software Quality Requirements and Evaluation (SQuaRE) [17]. Two of the SQuaRE divisions are addressed in this thesis: 1) Division of quality models standard (ISO/IEC 25010) [18], and 2) Quality measurement division standard (ISO/IEC 25022) [19]. The ISO/IEC 25010 are divided in product quality model and the quality model in use. Quality in use (QiU), a model of ISO/IEC 25010, is the focus of this study, through its evaluation in the context of a CDSS. The quality in use model refers to the quality of the software when executed, mentioning the result of the interaction between users and the software system/product in a specific context. This model consists of five quality characteristics: • Effectiveness - means the level of precision and completeness with which users achieve their specific goals when using the system; • Efficiency - refers to the resources spent to achieve the goals and its measure is related to the level of effectiveness achieved with the consumed resources; • Satisfaction - refers to whether user requirements are satisfied in a particular context of system use; • Freedom from risk - refers to the degree to which the quality of a system reduces or avoids potential risks to human life, the economic situation, and health of the environment; • Context coverage - deals with the use of the system in all specific contexts and/or in contexts that extend beyond the initially identified contexts. Context completeness and flexibility are the sub-characteristics that represent context coverage. Thus, when measuring the quality of a CDSS, we must consider both the context of use and the choice of the characteristic and sub-characteristic that best suits the purpose of the measurement [20]. The QiU model provides a powerful contribution to the practice of evaluating a system and determining its quality. According to Harrison et al. [21], Effectiveness, Efficiency and Satisfaction are considered the key criteria to reflect the quality of use. Therefore, these QiU characteristics meet the needs and expectations of the users of the systems, in our case of CDSSs, as they consider the user experience. As a contribution, we proposed a process model to evaluate two QiU characteristics in a CDSS: satisfaction and efficiency. We believe these characteristics are important in the evaluation of a CDSS because, due to its links with the user experience and the usability of the system, when measured, can corroborate the quality of the CDSS and mitigate the non-use and non-acceptance of this type of software. Other contributions from our work are 1) in the academic context, a significant study in the area of software quality, focusing on its characteristics, especially on the quality in use. A guideline for collecting and measuring these characteristics was built into our process model; 2) in the area of software development, professionals can make use of a simple and adaptable process, applicable to other types of systems, to measure the quality-in-use characteristics of their products.Desenvolver ou adquirir software é um investimento caro e precisa ser justificado. Além de útil, o sistema deve ser confiável, eficiente e, entre outras características, atender às expectativas dos usuários [1, 2]. Não seria diferente no caso de um sistema de apoio à decisão clínica (CDSS, acrônimo em inglês), sistemas desenvolvidos para apoiar médicos e outros profissionais de saúde na tomada de uma decisão médica [3]. CDSSs são elaborados dentro de um contexto clínico, seguindo guidelines com propósitos variados, sejam para diagnósticos [4, 5, 6], acompanhamento do paciente [7, 8, 9], na prevenção [10] e tratamento de doenças [11, 12]. No entanto, apesar de todo os benefícios oferecidos por um CDSS, sua aceitação na área médica ainda é motivo de debate [13, 14]. Essa aceitação está ligada à percepção do usuário final, como 1) a facilidade de uso e utilidade do sistema; 2) a qualidade dos resultados produzidos e sua confiabilidade [14]; 3) a acessibilidade contextual do sistema, muitas vezes não incluída na rotina e no fluxo de trabalho do profissional de saúde, e 4) o fato de muitos CDSSs não estarem integrados aos sistemas existentes [15]. Uma forma de estender o uso de CDSSs e disseminar suas contribuições positivas entre os profissionais de saúde é garantir a confiabilidade de seus resultados e a satisfação do usuáriofinal. Para tal deve-se seguir as melhores práticas da engenharia de software (SE, acrônimo em inglês) em sua concepção [16]. Isso implica em preocupar-se com a qualidade do sistema tanto no processo do projeto e desenvolvimento quanto em sua efetiva utilização. Uma forma de certificar se um software obedece a essa premissa é realizando avaliações de qualidade. Avaliar a qualidade do software é medir suas características e subcaracterísticas de qualidade. Para uma melhor estruturação desta medição foram desenvolvidos séries de padrões internacionais como guidelines de avaliação de qualidade de produtos de software. A série mais recente trata-se da ISO/IEC 25000 System and Software Quality Requirements and Evaluation (SQUARE) [17]. Dois padrões desta série foram abordadas nesta tese, sendo 1) o modelos de qualidade de software e sistemas (ISO/IEC 25010) [18], no qual trabalhamos especificamente com o modelo de qualidade em uso, e 2) o padrão de medição da qualidade em uso (ISO/IEC 25022) [19]. Qualidade em uso é o foco desta tese, através de sua avaliação no contexto de utilização de um CDSS. O Modelo de qualidade em uso trata da qualidade do software quando em execução, referindose ao resultado da interação dos usuários e o software em um cenário específico. Este modelo é composto de cinco características de qualidade: • Eficácia (ou efetividade) - esta característica representa o nível de precisão e completude com que os usuários alcançam os objetivos específicos, durante a utilização do sistema ou produto de software; • Eficiência - sua medição representa o nível de eficácia alcançada em relação aos recursos consumidos para o alcance das metas; • Satisfação - trata do quanto as necessidades do usuário são satisfeitas dentro de um determinado contexto de uso do sistema ou produto de software. Esta característica é composta pelas subcaracterísticas Utilidade, Confiança, Prazer e Conforto do usuário em relação ao sistema; • Livre de risco - trata do grau em que a qualidade de um sistema ou produto permite mitigar ou evitar riscos potenciais à vida humana, à situação econômica, à saúde ou ao meio ambiente, sendo estas suas três subcaracterísticas; • Cobertura de contexto - trata do uso do sistema em todos os contextos específicos e/ou em contextos além dos inicialmente identificados, sendo composta pelas subcaracterísticas completude de contexto e flexibilidade do sistema. Assim, para se medir a qualidade de um CDSS deve-se considerar tanto o contexto de utilização quanto a escolha da característica e subcaracterística que melhor condizem ao propósito da avaliação [20]. De acordo com Harrison et al. [21], Eficácia, Eficiência e Satisfação são considerados os principais critérios a serem avaliados para refletir a qualidade de uso. Tais características de qualidades em uso refletem o atendimento das necessidades e expectativas dos usuários dos sistemas, em especial ao usuário primário ou final, uma vez que estão diretamente relacionadas com a experiência do usuário. O modelo de qualidade em uso fornece uma contribuição poderosa para a prática de avaliar um sistema e determinar sua qualidade. Como contribuição, propusemos um modelo de processo para avaliação de qualidade em uso de um CDSS através da medição, a priori, de duas características de qualidade - satisfação e eficiência. Acreditamos que tais características são importantes na avaliação de um CDSS devido estreita relação destas com a experiência do usuário-final e a usabilidade do sistema. Assim, quando mensuradas, tais características podem corroborar com a qualidade do CDSS e mitigar a não utilização e não aceitação desse tipo de software. Nosso modelo proposto é definido por cinco (5) fases, a saber: 1) Identificação de cenário e contexto de uso do sistema, 2) seleção das medidas, métricas e métodos para mensurar as características, 3) a medição da qualidade, 4) a análise dos valores encontrados na medição e 5) a apresentação dos resultados obtidos. O resultado da aplicação do modelo de processo traduz-se em um conjunto de informações que nortearão um melhoramento do software, caso a medição das características fique abaixo de um padrão pré-definido pelos atores envolvidos no processo de medição do sistema. Por outro lado, se a medição for positiva, isso vem ratificar a qualidade do sistema e ações poderão ser tomadas para disseminar esse bom resultado, buscando a adesão de mais utilizadores. Como forma de validação do modelo proposto, após sua utilização para identificação de cenários e contexto-de-uso possíveis de serem mensurados, foi apresentado um CDSS da área oncológica a profissionais de saúde, estudantes de medicina e profissionais da área de qualidade de software que, ao final de sua utilização, responderam a um inquérito com o objetivo de avaliar o sistema. A aplicação se deu de forma online, dado a necessidade de mantermos o distanciamento social e o de cumprirmos as orientações sanitárias. As respostas serviram como fonte de dados para a medição das características de qualidadeem- uso do sistema. Os resultados da aplicação revelou que nosso modelo de processo de avaliação é válido, relevante e de fácil utilização para identificar as características importantes em um sistema, bem como suas medições por meio das funções matemáticas do modelo ISO/IEC 25022. Outras contribuições do nosso trabalho, temos 1) no âmbito acadêmico, um estudo significativo na área de qualidade de software, com foco em suas características, especialmente na qualidade em uso. Uma guideline para a coleta e mensuração dessas características foi construída em nosso modelo de processo; 2) na área de desenvolvimento de software, os profissionais podem contar com um processo simples e adaptável, aplicável a outros tipos de sistema, para mensuração da qualidade em uso de seus produtos.The research has been partially funded by the FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 and Operação Centro 01-0145-FEDER-000019 – C4 – Centro de Competências em Cloud Computing, co-financed by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio à Investigação Científica e Tecnológica – Programas Integrados de IC&DT. I would also like to acknowledge the contribution of the COST Action IC1303: AAPELE—Archi- tectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226; SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)

Integrative Predictive Support Systems for Hospital’s Resource Planning and Scheduling

RÉSUMÉ: Le système de santé du Canada a du mal à gérer le nombre croissant de patients ayant plusieurs maladies chroniques nécessitant l’accès à des soins de longue durée, et cela, principalement en raison de vieillissement de la population. Cela entraîne notamment de longs délais d’attente pour les patients et une augmentation des frais des soins de santé. Comme les hôpitaux représentent la plus grande part du budget de la santé, ils doivent améliorer leur efficacité opérationnelle en utilisant plus efficacement leurs ressources. En particulier, les hôpitaux qui fournissent aux patients des soins directs et un accès à des ressources coûteuses telles que les chirurgiens, les salles d’opération, les unités de soins intensifs et les salles d’opération, ont de la pression pour gérer leurs ressources efficacement. Les chercheurs en recherche opérationnelle ont largement abordé les problèmes liés à la planification et l’ordonnancement des ressources dans les hôpitaux pendant de nombreuses années. Les modèles analytiques conventionnels visent ainsi à améliorer l’efficacité de la prise de décision de planification des ressources hospitalières à des fins stratégiques (à long terme), tactiques (à moyen terme) et opérationnelles (à court terme). Cependant, ces modèles ont du mal à adresser efficacement la complexité, la variabilité, et l’incertitude inhérentes aux opérations hospitalières, car ils utilisent souvent des distributions statistiques simplistes pour émuler ces opérations. Par conséquent, ils sont sous-optimaux dans des contextes réels d’utilisation. Avec l’accroissement continu des quantités massives de données collectées dans les hôpitaux et les systèmes de santé, ainsi que les progrès dans le domaine de la modélisation prédictive, la communauté de la recherche opérationnelle a maintenant l’occasion de mieux analyser, comprendre et reproduire la complexité, la variabilité et l’incertitude des opérations hospitalières. À cette fin, l’objectif principal de cette thèse est de développer des cadres prédictifs intégrés, capables d’analyser et d’extraire des informations à partir de masses de données afin de mieux éclairer la planification et l’ordonnancement des ressources hospitalières aux niveaux stratégique, tactique, et opérationnel. Au meilleur des connaissances de l’auteur, cette thèse est une des premières à proposer des cadres pour la conception de systèmes prédictifs dans les hôpitaux. Au niveau stratégique et tactique, le premier article (chapitre 4) développe un cadre hybride basé sur l’apprentissage machine et la simulation pour prédire la demande personnalisée des patients au niveau des ressources hospitalières. Le cadre reflète notamment la relation à long terme entre les hôpitaux et les patients ayant des maladies chroniques, couvrant ainsi un horizon à long terme et intégrant le fait que les patients ont besoin, non pas d’une, mais de plusieurs visites à l’hôpital et accès à divers types de ressources. Dans cette thèse, nous proposons une approche novatrice basée sur l’apprentissage profond avec notamment un modèle de réseaux de neurones qui modélise les interactions complexes des patients chroniques avec les ressources hospitalières tout au long de leurs trajectoires de traitement. Cette nouvelle approche propose une série de réseaux de neurones où l’entrée de chaque réseau est définie comme la sortie de prédiction de son précédent. Les modèles proposés sont ainsi capables de prédire le traitement suivant du patient avec une précision (« recall ») allant de 68% à 79%. En plus de prévoir la prochaine étape des traitements des patients, nous proposons aussi une deuxième série de réseaux de neurones qui fournissent le temps prévu pour le prochain traitement. Ces trajectoires temporelles ainsi prédites sont ensuite incorporées dans une simulation à base d’agents capable de prédire la demande personnalisée et agrégée en ressources rares des hôpitaux à moyen et long terme en fonctions des profils des patients à traiter. Nous avons appliqué ce cadre intégratif à des données hospitalières réelles et montrons que le cadre proposé prédit efficacement la demande à moyen et à long terme de ressources rares dans les hôpitaux avec une précision de 77% (trajectoire) et de 64% (délai entre étapes), qui surpasse considérablement à la fois les méthodes traditionnelles de prévision demande et les techniques standard d’apprentissage automatique. Au niveau tactique et opérationnel, l’article présenté au chapitre 5 propose un modèle intégratif pour la prédiction des durées d’intervention chirurgicale personnalisées. Ce cadre est le premier de ce genre, et permet d’incorporer des attributs opérationnels et temporels liés à la planification, en plus d’attributs liés aux patients, aux procédures et aux chirurgiens pour prévoir ainsi la durée des interventions chirurgicales. De plus, ce cadre illustre l’efficacité d’algorithmes d’apprentissage automatique, tels que « Random Forest » et « Support Vector Machine » pour capturer les relations complexes entre les prédicteurs de la durée des interventions chirurgicales. Nous avons appliqué ce cadre à des données hospitalières réelles et constaté une amélioration de 31% de la précision des prédictions par rapport à la pratique. De plus, les résultats montrent que les décisions liées à la planification telles que l’ordonnancement des procédures et l’affectation des blocs ont un impact significatif sur les durées d’intervention chirurgicale. Ce résultat a des implications importantes pour la littérature dédiée à la planification et l’ordonnancement des salles d’opération aux niveaux tactique et opérationnel. Autrement dit, ce résultat implique que la planification optimale des salles d’opération n’est possible que si l’on optimise conjointement la durée et l’ordre des chirurgies. Au niveau opérationnel, l’article présenté au chapitre 6 propose un modèle intégratif pour la prédiction du risque de défaillance opérationnelle, et notamment du risque de temps supplémentaire. En pratique, même le plus précis des outils utilisés ne permet pas de prédire la variabilité des processus hospitaliers avec une précision de 100%. Par conséquent, au niveau opérationnel, il est important d’éviter les décisions qui ont un risque élevé d’échec pouvant ainsi entraîner des conséquences négatives significatives, qui peuvent à leur tour impliquer des coûts supplémentaires, une qualité de soins inférieure, et causer une insatisfaction à la fois des patients et du personnel. Dans cette thèse, nous appliquons des techniques d’apprentissage machine probabiliste au problème des heures supplémentaires en salle d’opération. Plus précisément, nous montrons, en utilisant des données hospitalières réelles, que les algorithmes proposés sont capables de classer les horaires des salles d’opération qui entraînent des heures supplémentaires avec une précision de 88%. La performance des prédictions ainsi calculées est de plus améliorée grâce à l’utilisation de techniques d’étalonnage appliquées aux résultats d’algorithmes d’apprentissage automatique. Le modèle de risque proposé a ainsi des implications significatives à la fois pour la pratique de la gestion les ressources au niveau opérationnel, mais aussi pour la littérature académique. Tout d’abord, le modèle de risque proposé peut facilement être intégré dans les systèmes de planification des salles à l’hôpital afin d’aider les décideurs à éviter des horaires risqués. Deuxièmement, le modèle de risque proposé peut être utilisé conjointement avec les modèles existants d’ordonnancement des salles d’opération pour améliorer la performance opérationnelle des solutions.----------ABSTRACT: Canada’s health care system is struggling to manage the increasing demand of patients with multiple chronic issues who require access to long-term care, primarily due to Canada’s aging population. This has resulted in long patient wait times and increasing healthcare costs. Since hospitals represent the largest share of the healthcare budget, they are required to improve their operational efficiency by making better use of their resources. In particular, hospitals that provide patients direct care and access to expensive resources such as surgeons, operating rooms, ICUs and wards are under scrutiny on whether or not they manage their resources effectively. Operations research scholars have extensively addressed problems related to resource planning and scheduling in hospitals for many years. Conventional analytical models aim to improve the efficiency of decision-making in hospital resource planning at strategic (long-term), tactical (mid-term) and operational (short-term) levels. However, these models suffer from limited ability in effectively capturing the inherent complexity, variability and uncertainty of hospital operations because they often assume crude and simplistic statistical distributions to imitate these operations. Consequently, they are suboptimal in real-life settings. With the massive amount of data gathered in the hospitals and healthcare systems and advances in the field of predictive modeling, the operations research community are now given the opportunity to better analyze, understand and replicate the complexity, variability and uncertainty of hospital operations. To this end, the main objective of this thesis is to develop integrate predictive frameworks that are capable of analyzing and extracting important patterns from large-scale data that better inform hospital resource planning and scheduling systems at the strategic, tactical and operational levels. To the best of the author’s knowledge, this thesis is a pioneer in proposing frameworks for the design of hospital-wide integrative predictive support systems. At the strategic and tactical level, the first article (Chapter 4) develops a hybrid machine learning-simulation framework for predicting personalized patient demand for hospital resources. The framework captures the long-term relationship between hospitals and chronic patients, which spans over a long-term horizon and incorporates the fact that patients will need, not one, but several visits to the hospital and access to various types of resources over a long time period. In this thesis, we propose a novel approach based on deep feedforward neural network model that models the complex interactions of chronic patients with hospital resources during their treatment pathways. The proposed novel approach does so by developing a series of sequential individually trained deep feedforward neural networks, where each network’s input is set as the prediction output of its preceding. The proposed models are capable of predicting patient’s next treatment with an accuracy (measured by “recall”) ranging from 68% to 79%. In addition to predicting the transition of patients between treatments in their clinical pathways, we propose a second series of temporal deep feedforward neural network models that provide the expected receiving time for the next treatment. The trained pathway and temporal predictive models are incorporated into an agent-based simulation which is capable of predicting personalized and aggregated demand for hospitals’ scarce resources for the mid-term and long-term time horizon. We applied the proposed integrative framework to real hospital data and showed that proposed framework effectively predicts mid-term and long-term demand for hospital scarce resources with an accuracy of 77% and 64%, respectively, which dramatically outperforms traditional demand forecasting methods and standard machine learning techniques. At the tactical and operational level, the article proposed in chapter 5 is an integrative predictive model for personalized surgical procedure durations. The framework is the first of its kind to incorporate scheduling-related, operational and temporal attributes in addition to patient specific, procedure specific and surgeon specific attributes to predict surgical procedure durations. Furthermore, the framework illustrates the effectiveness of machine learning algorithms such as Random Forest and Support Vector Machine to capture the complex relationships among the predictors of surgical procedure durations. We applied the proposed framework to real hospital data and found an improvement of 31% in the accuracy of our predictive model compared to its practice benchmark. Furthermore, the results show that scheduling-related decisions such as procedure sequencing and block assignment have a significant impact on surgical procedure durations. This result has significant implications for operating room planning and scheduling literature at both tactical and operational levels. Namely, it indicates that optimal operating room planning is achieved only through joint optimization of surgical duration procedures and schedules. At the operational level, the article presented in chapter 6 proposes an integrative predictive model for operational failure risk assessment. Interestingly, even the most accurate predictive tools used in practice fall short in predicting variability in hospital processes with 100% accuracy. Therefore, at the operational level it is important to avoid decisions that have a high risk of failure which may subsequently result in significant adverse consequences, which, in turn, may incur additional costs, lower quality of care and cause patient and staff dissatisfaction. In this thesis, we apply probabilistic machine learning techniques to the operating room overtime problem. We show that the proposed algorithms are capable of classifying operating room schedules that result in overtime with an accuracy of 88% when applied to real hospital data. The predictive performance is further improved through the use of calibration techniques applied to the output of machine learning algorithms. The proposed risk model has significant implications for practice and operational level resource scheduling literature. First, the proposed risk model can easily be integrated into operating room scheduling systems at the hospital which ultimately assist decision makers in avoiding risky schedules. Second the proposed risk model may be used in conjunction with existing operating room scheduling models to improve the operational performance of commonplace solutions

Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs