Serum PSA (Prostate Specific Antigen) level is used for prediction of prostatic carcinoma, but it suffers from weak sensitivity and specificity. We applied logistic regression, artificial neural networks, decision tree, and genetic algorithm to prostate cancer prediction problem to design a model for Turkish population. A hybrid model of logistic regression and decision tree has been designed. The model could prevent 33 biopsies (4.4% of our patients who have PSA level between 0 and 10) from our data set without a loss from sensitivity. The prepared online decision support tool and a questionnaire were published on a website. Fifty urologists have completed the questionnaire. Cronbach&rsquo / s alpha was 0.770. On a five graded Likert scale, the mean score of &ldquo / attitude to computer use in healthcare&rdquo / (ACH) was 4.2. The mean of eight responses related to the online tool (Attitude to Decision Support Tool / ADST), was 3.7. ADST was correlated with ACH (r=0.351, p=0.013). Physicians who have positive attitude to computer use in healthcare tend to use the tool (r=0.459, p=0.001). The first factor influencing the opinions of the urologists was the attitude of the user to computer use in healthcare, the other factor was the attitude of the user to the decision support tool itself. To increase the acceptance, education and training of physicians in the use of information technologies in healthcare, informing users about the logic of the decision support tool, and redesigning the system according to user feedback may be helpful.

Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.

Purpose:The thesis will shed light on the overall need of CDSSs in the Swedish health care system,  and  it  will  also  present  a  specific  efficiency  problem  that  could  be  solved  by implementing a CDSS. The need for a CDSS is where an implementation would improve patient outcome, by delivering the right care at the right time, and where the CDSS could reduce the cost of the delivered care. A better understanding of the current need could help eliminate the existing empirical gap and ultimately lead to better and more efficient health care in Sweden. The research question was formulated as: Where within Swedish health care can a need for increased efficiency be met through the implementation of a realistic CDSS system? Design and methodology: The  thesis  is  a  case  study  where qualitative data, collected through a literature review and interviews, was used to answer the research question. The methodology used was tailored to the unique setting of the research and in accordance to the purpose of the study. The method was divided into five phases. (1) Finding an area of focus, such as a specific diagnosis, within the health care system where the need for a CDSS system is deemed high. (2) Mapping the care chain of the identified area of interest. (3) Developing hypotheses concerning where in the care chain challenges could be solved using a clinical decision support system. (4) Confirming or rejecting the proposed hypotheses through interviews with relevant experts. (5) Presenting the specific efficiency problem that could be solved using a CDSS and a presentation of the design of said CDSS. Findings: The efficiency problem that could be solved using a CDSS was identified to be within the area of heart failure treatment. There were a multitude of areas of improvement found along the care chain and a number of them could be solved by developing and using specific CDSSs. A CDSS that could help physicians, within the primary care system, to identify patients that  could benefit from  being  assessed  by  cardiology specialist was  proposed  as  the  most beneficial  CDSS  system.  The  proposed  CDSS  would  be  both  beneficial  and  realistically implementable. / Syftet med uppsatsen är att belysa det övergripande behovet av kliniska beslutsstödssystem inom den svenska vården och slutligen finna det mest trängande behovet. En bättre förståelse för detta behov kan hjälpa att minska det existerande empiriska gapet och slutligen leda till en bättre och mer effektiv vård i Sverige. Forskarfrågan formulerades som uppdraget att finna ett behov för ökad effektivitet inom svensk sjukvård, som kan lösas genom implementering av ett realistiskt kliniskt beslutsstöd. Design och metodologi: Uppsatsen är en casestudie där kvalitativ data, samlad genom en litteraturstudie samt intervjuer, användes för att besvara forskningsfrågan. Metodologin som brukades var anpassad efter den unika naturen för forskningen, samt i enighet med syftet av studien. Metoden delades in i fem faser. (1) Finna ett fokusområde, exempelvis en specifik diagnos, där behovet av ett kliniskt beslutsstöd bedömdes högt. (2) Kartlägga vårdkedjan för den identifierade diagnosen. (3) Utveckla hypoteser angående var inom vårdkedjan som  utmaningar skulle kunna lösas med ett kliniskt beslutsstöd. (4) Bekräfta eller förkasta ypoteserna genom intervjuer med relevanta experter. (5) Presentera problemet med det mest trängande behovet efter ett kliniskt beslutsstöd och hur ett sådans skulle utformas. Fynd: Effektivitetsproblemet som kunde lösas bäst via ett kliniskt beslutsstöd identifierades att vara inom området hjärtsviktsbehandling. Det fanns flertalet områden med utvecklingspotential som urskiljdes ur vårdkedjan för hjärtsviktspatienter, och vissa av dessa utmaningar kunde lösas genom utveckling och implementering av specifika kliniska beslutsstöd. Det kliniska beslutsstöd som skulle lösa det mest trängande behovet inom vården idag föreslås vara ett system som hjälper läkare inom vårdcentralerna att identifiera patienter som skulle gagnas av en remiss till en kardiolog. Det föreslagna kliniska beslutsstödet skulle vara både fördelaktigt för vårdpersonal samt patienter samt är realistiskt implementerbart.

Sistemas de suporte a decisão são utilizados em cenários com incertezas. Uma decisão normalmente é auxiliada por resultados obtidos com ações passadas em problemas semelhantes. Quando um sistema de suporte a decisão incorpora conhecimento específico de uma área, estes recebem o nome de sistemas especialistas. Tal conhecimento especifico é utilizado para inferência juntamente com as informações de entrada a respeito do problema. O objetivo deste trabalho é a avaliação e modelagem de sistemas de auxílio a decisão, foram analisadas duas abordagens para um mesmo problema alvo, sendo uma de gerenciamento do problema e outra de detecção do problema. A abordagem de gerenciamento utiliza redes Bayesianas para modelagem, tanto do conhecimento específico quanto para a inferência. As variáveis utilizadas, as relações de dependência e as probabilidades condicionais entre as variáveis foram extraídas da literatura. A abordagem de detecção do problema utilizou imagens para extração de características seguida de um algoritmo de agrupamento para comparação com a classificação de um especialista. Uma das áreas de aplicação de sistemas especialistas é na área clínica, podendo auxiliar tanto na detecção, diagnóstico e tratamento de doenças. A cárie dental é um problema generalizado que afeta a maioria das pessoas, tanto em países ricos, como em países pobres. Existem poucos sistemas para auxílio no processo de diagnóstico da cárie, sendo a maior parte dos sistemas existentes determinísticos, focando apenas na detecção da lesão. O sistema de gerenciamento da cárie desenvolvido foi apresentado a dois profissionais da odontologia, a opinião deles mostra que está abordagem é promissora e aplicável em campos como a educação e a atenção básica a saúde. Além da apresentação aos profissionais, foram utilizados casos bem estabelecidos da literatura para analisar as sugestões fornecidas pela Rede, e o resultado foi coerente com o cenário real de tomada de decisão. A metodologia de detecção da cárie resultou em um alto valor de acurácia, 96.88%, mostrando que tal metodologia é promissora em comparação com outros trabalhos da área. Além da contribuição para a área de informática odontológica, os resultados mostram que a extração da estrutura e das probabilidades condicionais da rede a partir da literatura é uma metodologia que pode ser utilizada em outras áreas com cenário similar ao do diagnóstico da cárie. Nos próximos passos do projeto alguns pontos referentes a modelagem de sistemas e redes Bayesianas serão analisados, como escalabilidade e testes de validação, tanto quantitativamente como qualitativamente, isto inclui o desenvolvimento de métodos computacionalmente efetivos para a geração de casos aleatórios utilizando o Método de Monte Carlo / Decision support systems are used in uncertainty scenarios; normally a decision is choose using similar problems actions results. Decision support systems could incorporate specific knowledge; such systems are called expert systems. The specific knowledge is used for inference about the problem scenario. This work objective is the evaluation and modeling of decision support systems, we analyzed two distinct approaches for the same problem, one for detection, another for management. The management approach uses Bayesian networks for modeling the specific knowledge and the inference engine. The variables choice, the dependences relationship and the conditional probabilities were extracted from the scientific literature. The detection approach used images and feature extraction to perform a clustering and compare the output labels with a specialist classification. One application of expert systems is clinical, supporting diseases detection, diagnosis and treatment. Dental caries is a generalized problem that affects major part of the population, few systems exists for support the caries diagnostic process, the major part is deterministic, focusing only the detection problem. The caries management system developed here was shown to two odontology professionals, and they opinion encourage such approach to be applied in fields like odontology education and basic health. Beyond this, we used well-established cases to analyze the network output suggestions, the result obtained was coherent with the real decision making scenario. The caries detection approach resulted in a high accuracy, 96.88%, showing that methodology is promising. Besides the contribution for dental informatics field, the results obtained here shows that the extraction of the network structure from the literature could be used in problems similar with caries diagnoses. The project next steps are to analyze some points of systems modeling and Bayesian networks, like scalability and validation tests, both quantitative and qualitative, and including the development of computational effectives methods for the use of Monte Carlo methodology

Sistemas de suporte a decisão são utilizados em cenários com incertezas. Uma decisão normalmente é auxiliada por resultados obtidos com ações passadas em problemas semelhantes. Quando um sistema de suporte a decisão incorpora conhecimento específico de uma área, estes recebem o nome de sistemas especialistas. Tal conhecimento especifico é utilizado para inferência juntamente com as informações de entrada a respeito do problema. O objetivo deste trabalho é a avaliação e modelagem de sistemas de auxílio a decisão, foram analisadas duas abordagens para um mesmo problema alvo, sendo uma de gerenciamento do problema e outra de detecção do problema. A abordagem de gerenciamento utiliza redes Bayesianas para modelagem, tanto do conhecimento específico quanto para a inferência. As variáveis utilizadas, as relações de dependência e as probabilidades condicionais entre as variáveis foram extraídas da literatura. A abordagem de detecção do problema utilizou imagens para extração de características seguida de um algoritmo de agrupamento para comparação com a classificação de um especialista. Uma das áreas de aplicação de sistemas especialistas é na área clínica, podendo auxiliar tanto na detecção, diagnóstico e tratamento de doenças. A cárie dental é um problema generalizado que afeta a maioria das pessoas, tanto em países ricos, como em países pobres. Existem poucos sistemas para auxílio no processo de diagnóstico da cárie, sendo a maior parte dos sistemas existentes determinísticos, focando apenas na detecção da lesão. O sistema de gerenciamento da cárie desenvolvido foi apresentado a dois profissionais da odontologia, a opinião deles mostra que está abordagem é promissora e aplicável em campos como a educação e a atenção básica a saúde. Além da apresentação aos profissionais, foram utilizados casos bem estabelecidos da literatura para analisar as sugestões fornecidas pela Rede, e o resultado foi coerente com o cenário real de tomada de decisão. A metodologia de detecção da cárie resultou em um alto valor de acurácia, 96.88%, mostrando que tal metodologia é promissora em comparação com outros trabalhos da área. Além da contribuição para a área de informática odontológica, os resultados mostram que a extração da estrutura e das probabilidades condicionais da rede a partir da literatura é uma metodologia que pode ser utilizada em outras áreas com cenário similar ao do diagnóstico da cárie. Nos próximos passos do projeto alguns pontos referentes a modelagem de sistemas e redes Bayesianas serão analisados, como escalabilidade e testes de validação, tanto quantitativamente como qualitativamente, isto inclui o desenvolvimento de métodos computacionalmente efetivos para a geração de casos aleatórios utilizando o Método de Monte Carlo / Decision support systems are used in uncertainty scenarios; normally a decision is choose using similar problems actions results. Decision support systems could incorporate specific knowledge; such systems are called expert systems. The specific knowledge is used for inference about the problem scenario. This work objective is the evaluation and modeling of decision support systems, we analyzed two distinct approaches for the same problem, one for detection, another for management. The management approach uses Bayesian networks for modeling the specific knowledge and the inference engine. The variables choice, the dependences relationship and the conditional probabilities were extracted from the scientific literature. The detection approach used images and feature extraction to perform a clustering and compare the output labels with a specialist classification. One application of expert systems is clinical, supporting diseases detection, diagnosis and treatment. Dental caries is a generalized problem that affects major part of the population, few systems exists for support the caries diagnostic process, the major part is deterministic, focusing only the detection problem. The caries management system developed here was shown to two odontology professionals, and they opinion encourage such approach to be applied in fields like odontology education and basic health. Beyond this, we used well-established cases to analyze the network output suggestions, the result obtained was coherent with the real decision making scenario. The caries detection approach resulted in a high accuracy, 96.88%, showing that methodology is promising. Besides the contribution for dental informatics field, the results obtained here shows that the extraction of the network structure from the literature could be used in problems similar with caries diagnoses. The project next steps are to analyze some points of systems modeling and Bayesian networks, like scalability and validation tests, both quantitative and qualitative, and including the development of computational effectives methods for the use of Monte Carlo methodology

Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabilities of computers. CDSS depend on many techniques from the fields of image acquisition, image processing, pattern recognition, machine learning as well as optimization for medical data analysis to produce efficient diagnoses. In this dissertation, we discuss the current challenges in designing an efficient CDSS as well as a number of the latest techniques (while identifying best practices for each stage of the framework) to meet these challenges by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest and thus aiding in medical diagnosis. To meet these challenges, we propose an extension of conventional clinical decision support system framework, by incorporating artificial immune network (AIN) based hyper-parameter optimization as integral part of it. We applied the conventional as well as optimized CDSS on four case studies (most of them comprise medical images) for efficient medical diagnosis and compared the results. The first key contribution is the novel application of a local energy-based shape histogram (LESH) as the feature set for the recognition of abnormalities in mammograms. We investigated the implication of this technique for the mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features were calculated, and they were fed to support vector machine (SVM) and echo state network (ESN) classifiers. In addition, the impact of selecting a subset of LESH features based on the classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The second key contribution is to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs was selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely the extreme learning machine (ELM), SVM and ESN, were then applied using the LESH extracted features to enable the efficient diagnosis of a correct medical state (the existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, were further benchmarked against state-of-the-art wavelet based features, and authenticated the distinct capability of our proposed framework for enhancing the diagnosis outcome. As the third contribution, this thesis presents a novel technique for detecting breast cancer in volumetric medical images based on a three-dimensional (3D) LESH model. It is a hybrid approach, and combines the 3D LESH feature extraction technique with machine learning classifiers to detect breast cancer from MRI images. The proposed system applies CLAHE to the MRI images before extracting the 3D LESH features. Furthermore, a selected subset of features is fed to a machine learning classifier, namely the SVM, ELM or ESN, to detect abnormalities and to distinguish between different stages of abnormality. The results indicate the high performance of the proposed system. When compared with the wavelet-based feature extraction technique, statistical analysis testifies to the significance of our proposed algorithm. The fourth contribution is a novel application of the (AIN) for optimizing machine learning classification algorithms as part of CDSS. We employed our proposed technique in conjunction with selected machine learning classifiers, namely the ELM, SVM and ESN, and validated it using the benchmark medical datasets of PIMA India diabetes and BUPA liver disorders, two-dimensional (2D) medical images, namely MIAS and INbreast and JSRT chest radiographs, as well as on the three-dimensional TCGA-BRCA breast MRI dataset. The results were investigated using the classification accuracy measure and the learning time. We also compared our methodology with the benchmarked multi-objective genetic algorithm (ES)-based optimization technique. The results authenticate the potential of the AIN optimised CDSS.

L'explosion récente de données d'imagerie cardiaque a été phénoménale. L'utilisation intelligente des grandes bases de données annotées pourrait constituer une aide précieuse au diagnostic et à la planification de thérapie. En plus des défis inhérents à la grande taille de ces banques de données, elles sont difficilement utilisables en l'état. Les données ne sont pas structurées, le contenu des images est variable et mal indexé, et les métadonnées ne sont pas standardisées. L'objectif de cette thèse est donc le traitement, l'analyse et l'interprétation automatique de ces bases de données afin de faciliter leur utilisation par les spécialistes de cardiologie. Dans ce but, la thèse explore les outils d'apprentissage automatique supervisé, ce qui aide à exploiter ces grandes quantités d'images cardiaques et trouver de meilleures représentations. Tout d'abord, la visualisation et l'interprétation d'images est améliorée en développant une méthode de reconnaissance automatique des plans d'acquisition couramment utilisés en imagerie cardiaque. La méthode se base sur l'apprentissage par forêts aléatoires et par réseaux de neurones à convolution, en utilisant des larges banques d'images, où des types de vues cardiaques sont préalablement établies. La thèse s'attache dans un deuxième temps au traitement automatique des images cardiaques, avec en perspective l'extraction d'indices cliniques pertinents. La segmentation des structures cardiaques est une étape clé de ce processus. A cet effet une méthode basée sur les forêts aléatoires qui exploite des attributs spatio-temporels originaux pour la segmentation automatique dans des images 3Det 3D+t est proposée. En troisième partie, l'apprentissage supervisé de sémantique cardiaque est enrichi grâce à une méthode de collecte en ligne d'annotations d'usagers. Enfin, la dernière partie utilise l'apprentissage automatique basé sur les forêts aléatoires pour cartographier des banques d'images cardiaques, tout en établissant les notions de distance et de voisinage d'images. Une application est proposée afin de retrouver dans une banque de données, les images les plus similaires à celle d'un nouveau patient. / The recent growth of data in cardiac databases has been phenomenal. Cleveruse of these databases could help find supporting evidence for better diagnosis and treatment planning. In addition to the challenges inherent to the large quantity of data, the databases are difficult to use in their current state. Data coming from multiple sources are often unstructured, the image content is variable and the metadata are not standardised. The objective of this thesis is therefore to simplify the use of large databases for cardiology specialists withautomated image processing, analysis and interpretation tools. The proposed tools are largely based on supervised machine learning techniques, i.e. algorithms which can learn from large quantities of cardiac images with groundtruth annotations and which automatically find the best representations. First, the inconsistent metadata are cleaned, interpretation and visualisation of images is improved by automatically recognising commonly used cardiac magnetic resonance imaging views from image content. The method is based on decision forests and convolutional neural networks trained on a large image dataset. Second, the thesis explores ways to use machine learning for extraction of relevant clinical measures (e.g. volumes and masses) from3D and 3D+t cardiac images. New spatio-temporal image features are designed andclassification forests are trained to learn how to automatically segment the main cardiac structures (left ventricle and left atrium) from voxel-wise label maps. Third, a web interface is designed to collect pairwise image comparisons and to learn how to describe the hearts with semantic attributes (e.g. dilation, kineticity). In the last part of the thesis, a forest-based machinelearning technique is used to map cardiac images to establish distances and neighborhoods between images. One application is retrieval of the most similar images.

Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.

Riskbedömning är en väsentlig del av klinikerns vardag. Varje patient ska riskbedömas och riskgrupperas, vilket sedan utgör grund för val av behandling, behandlare och revisionsintervall. I tandvården i Sverige idag används det ett flertal olika stödsystem eller riktlinjer för riskbedömning av oral hälsa. Syftet med studien var att ta reda på vilka stödsystem/riktlinjer som finns för riskbedömning av oral hälsa i Sverige idag och ge en beskrivning av de mest frekvent använda systemen samt göra en jämförelse av dessa. Syftet var också att undersöka huruvida dessa system är evidensbaserade och utvärderade samt att kartlägga kunskapsläget, gällande evidensbasering och utvärdering av stödsystem/riktlinjer, för riskbedömning av oral hälsa. För att skapa oss en allmän bild av stödsystem/riktlinjer för riskbedömning inleddes vårt arbete med en litteraturgenomgång. För att svara på frågeställningen om kunskapsläget över stödsystemens/riktlinjernas evidens och utvärdering, gjordes en systematisk litteraturöversikt. En kartläggning över Folktandvården och kontakt med Praktikertjänst gav oss en bild över vilka system som är aktuella i Sverige idag. För information om de utvalda systemen kontaktades så kallade nyckelpersoner för intervju. Data från intervjuerna har sedan analyserats i relation till vår litteraturgenomgång. Resultaten visar att Beslutsstöd R2 är det system som används mest frekvent inom Folktandvården. Andra förekommande system är Datorstödd Riskbedömning Effica och DentiGroup. Inom Praktikertjänst finns ett system tillgängligt för alla som använder sig av Opus journalsystem. Vår slutsats är att det vetenskapliga underlaget, gällande evidensbasering och utvärdering av stödsystem/riktlinjer, är bristfälligt. / Risk assessment is an essential part of dental practice today. Each patient should be assessed and stratified into a well defined group according to risk. This risk assessment should then affect the choice of prevention and treatment, and intervals for recalls and appropriate level of care. Today, in Swedish dental healthcare, different guidelines are being used as support in the assessment of a patient’s oral health. The aim of this study was to find out, compare and describe the most frequent used guidelines/systems for risk assessment of oral health in Sweden today. A second aim was to examine whether these systems are evidence-based and evaluated, and to identify existing knowledge about evidence-based supporting systems for risk assessment of oral health. A systematic review was made where articles published more than 10 years ago and papers which didn’t involve risk assessment of the whole patient were excluded. The Public Dental Service and Praktikertjänst were contacted and enquired about which systems if any were in use. For more in depth information on these systems, persons with key knowledge were interviewed. The information received was then analyzed in relation to the literature review. The results show that the system most frequently used in the Public Dental Service today is Beslutsstöd R2. Other computerbased systems are Datorstödd riskgruppering Effica and DentiGroup. In Praktikertjänst a system is available for all users of Opus Dental practice management system. Our conclusion is that the scientific evidence, regarding evidence-based and evaluation of support / guidelines are inadequate.

Introdução: A Medicina Baseada em Evidências, apesar da grande profusão de publicações da área, enfrenta desafios no intuito de melhorar a qualidade da assistência à saúde. O conhecimento gerado por suas publicações demora a ser posta em prática. Os softwares CDSS de apoio à decisão clínica, podem ser a solução de incorporação das evidências na prática clínica. Esses sistemas já foram associados a melhorias na qualidade de diversos aspectos da assistência à saúde, como a organização, minimização de erros, redução de custos, aumento da eficiência dos cuidados, mas pesquisas com desfechos centrados no paciente ainda são raras. Como outra qualquer intervenção em saúde, as afirmações de que os CDSS são benéficos para o paciente necessitam de confirmação por ensaios clínicos. Objetivos: Verificar se o uso dos CDSS com base em evidências, está associado com melhores resultados clínicos orientados para o paciente. Métodos: Revisão sistemática da literatura dos ensaios clínicos controlados e randomizados que compararam diretamente o uso de CDSS com práticas clínicas convencionais considerando os desfechos clínicos classificados como orientados para o paciente. Resultados: Nossa estratégia de pesquisa identificou 51283 artigos na base MEDLINE-PubMed, sendo 311 selecionados para leitura de título e resumo após a aplicação do filtro para ensaio clínico randomizado, 45 selecionados para leitura do texto completo, dos quais 19 preencheram o critério de elegibilidade. Outros 9 ensaios foram incluídos através da realização de um overview das revisões sistemáticas anteriores. Os ensaios foram publicados entre os anos de 1995 e 2015 e realizados em cinco contextos assistenciais, com duração máxima de 12 meses. A maioria das fontes de evidências que alimentaram os sistemas foram diretrizes de órgão governamental ou sociedades de especialidades. Doze ensaios avaliaram mortalidade, 14 avaliaram hospitalizações ou atendimento de emergência e 6 avaliaram desfechos relacionados a presença de sintomas. Foram realizadas meta-análises de acordo com o contexto assistencial e o tipo de desfecho. Somente uma meta-análise envolvendo a mortalidade de pacientes tratados em ambulatório por diferentes condições clínicas se mostrou estatisticamente significante, favorável ao grupo CDSS, em 3 ensaios randomizados por aglomerado, com risco de viés considerado moderado, que compromete a qualidade da evidência. Conclusões Apesar do potencial dos CDSS no apoio de intervenções de saúde, não há evidência de boa qualidade de que sejam efetivos para aumentar a sobrevida ou a qualidade de vida dos pacientes. O número de ensaios que avaliam esses desfechos, os períodos de tempo pelos quais os pacientes foram seguidos, o número insuficiente de participantes, bem como a heterogeneidade entre os estudos analisados quanto aos cenários clínicos e as fontes de informação que alimentam os softwares não permitiram resultados mais conclusivos / Background: In spite of the wealth of publications in the field, Evidence-Based Medicine faces challenges in order to improve quality of health care. It takes too long for knowledge produced by its publications to be put into practice. Clinical Decision Support Systems (CDSS) may be a solution for incorporation of evidence into clinical practice. These systems have been associated with improvements in quality of various aspects of health care, including its organization, error minimizations, cost reductions and increases in its efficiency, but patient-oriented outcomes are still rare in research literature. Like any other healthcare intervention, claims that CDSS are beneficial for patients need to be confirmed by clinical trials. Objective: To verify whether the use of evidence-based Clinical Decision Support Systems is associated with improved patient-oriented clinical outcomes. Methods: Systematic literature review of randomized controlled trials that directly compared the use of CDSS with usual practice considering clinical outcomes classified as patient-oriented. Results: Our search strategy has identified 51,283 entries in MEDLINE-PubMed and, after filtering for randomized controlled trials 311 papers were selected for title and abstract reading. Forty-five were selected for full-text reading of which 19 have met eligibility criteria. Another nine trials were included after an overview of previous systematic reviews. Trials were published between 1995 and 2015 and performed in five care settings with a maximum follow-up of 12 months. Most evidence sources feeding systems´ knowledge bases were government agency guidelines or specialty societies. Twelve trials have assessed mortality, 14 have assessed hospital admissions and/or emergency visits and nine have assessed symptom-related outcomes. Meta-analyses were performed according to trials´ care setting and outcome types. Only a meta-analysis of three cluster-randomized trials involving mortality among outpatients with different clinical conditions was statistically significant, favouring CDSS group, but risk of bias was moderate, compromising the quality of evidence. Conclusions: Despite the potential of CDSS to improve healthcare quality there is no reliable evidence that they improve patients´ life extension or quality. The insufficient numbers of trials assessing these outcomes, studies´ subjects and follow-up periods, the heterogeneities of clinical settings across studies and knowledge bases feeding the systems impede achieving results that are more conclusive