Implementing Guideline-based, Experience-based, and Case-based approaches to enrich decision support for the management of breast cancer patients in the DESIREE project

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes

Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations

Case-based decision support system for breast cancer management

Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations

Développement d'un système avancé d'aide à la décision clinique : enrichir la connaissance issue des guides de pratique clinique avec l'expérience

Evidence-Based Medicine has been formalized as Clinical Practice Guidelines, which define workflows and recommendations to be followed for a given clinical domain. These documents were formalized aiming to standardize healthcare and seeking the best patient outcomes. Nevertheless, clinicians do not adhere as expected to these guidelines due to several clinical and implementation limitations. On one hand, clinicians do not feel familiar, agree with and or are unaware of guidelines, hence doubting their self-efficacy and outcome expectancy compared to previous or more common practices. On the other hand, maintaining these guidelines updated with the most recent evidence requires continuous versioning of these paper-based documents. Clinical Decision Support Systems are proposed to help during the clinical decision-making process with the computerized implementation of the guidelines to promote their easy consultation and increased compliance. Even if these systems help improving guideline compliance, there are still some barriers inherited from paper-based guidelines that are not solved, such as managing complex cases not defined within the guidelines or the lack of representation of other external factors that may influence the provided treatments, biasing from guidelines’ recommendations (i.e. patient preferences). Retrieving observational data and patients’ quality of life outcomes related to the provided healthcare during routine clinical practice could help to identify and overcome these limitations and would generate Real World Data representing the real population and going beyond the limitations of the knowledge reported in the Randomized Clinical Trials. This thesis proposes an advanced Clinical Decision Support System for coping with the purely guideline-based support limitations and going beyond the formalized knowledge by analyzing the clinical data, outcomes, and performance of all the decisions made over time. To achieve these objectives, an approach for modeling the clinical knowledge and performance in a semantically validated and computerized way has been presented, leaning on an ontology and the formalization of the Decisional Event concept. Moreover, a domain-independent framework has been implemented for easing the process of computerizing, updating and implementing Clinical Practice Guidelines within a Clinical Decision Support System in order to provide clinical support for any queried patient. For addressing the reported guideline limitations, a methodology for augmenting the clinical knowledge using experience has been presented along with some clinical performance and quality evaluation over time, based on different studied clinical outcomes, such as the usability and the strength of the rules for evaluating the clinical reliability behind the formalized clinical knowledge. Finally, the accumulated Real World Data was explored to support future cases, promoting the study of new clinical hypotheses and helping in the detection of trends and patterns over the data using visual analytics tools. The presented modules had been developed and implemented in their majority within the European Horizon 2020 project DESIREE, in which the use case was focused on supporting Breast Units during the decision-making process for Primary Breast Cancer patients management, performing a technical and clinical validation over the presented architecture, whose results are presented in this thesis. Nevertheless, some of the modules have been also used in other medical domains such as Gestational Diabetes guidelines development, highlighting the interoperability and flexibility of the presented work.La médecine fondée sur les preuves a permis de formaliser des guides de pratique clinique qui définissent des flux de travail et des recommandations à suivre pour un domaine clinique concis. Ces guides se sont construits dans le but de standardiser les soins de santé et d'obtenir les meilleurs résultats possibles pour les patients. Néanmoins, les médecins n’adhèrent pas toujours à ces directives en raison de diverses limitations cliniques et de mise-en-œuvre. D’une part, les médecins n’ont pas toujours familiarisés ou en accord avec les lignes directrices des guides de pratique clinique, doutant ainsi de leur efficacité et des résultats attendus par rapport aux pratiques antérieures. D'autre part, maintenir ces guides à jour en incluant les dernières preuves établies requiert une gestion continue d’une documentation établie sur support papier. Les systèmes d'aide à la décision clinique sont ainsi proposés comme aide durant le processus de prise de décision clinique, par la mise en œuvre informatisée des guides pour promouvoir leur consultation et l’adhésion des médecins. Bien que ces systèmes aident à améliorer la conformité des guides, il subsiste certains obstacles hérités des guides sur support papier qui ne sont pas résolus avec leur mise en œuvre informatisée, comme le traitement des cas complexes non-définis dans les directives ou le manque de représentation d'autres facteurs externes qui peuvent influer sur les traitements fournis et faire dévier des recommandations des guides (c.-à-d. les préférences du patient). La présente thèse propose un système avancé d'aide à la décision clinique pour faire face aux limitations du soutien purement basé en guides et aller au-delà des connaissances formalisées en analysant les données cliniques, les résultats et les performances de toutes les décisions prises au fil du temps. Pour atteindre ces objectifs, une approche de modélisation des connaissances et performances cliniques de manière sémantique validée et informatisée a été présentée, en s'appuyant sur une ontologie et avec la formalisation du concept d'Événement Décisionnel. De plus, un cadre indépendant du domaine a été mis en place pour faciliter le processus d'informatisation, de mise à jour et de mise en œuvre des guides de pratique clinique au sein d'un système d'aide à la décision clinique afin de fournir un soutien clinique à pour chaque patient interrogé. Pour répondre aux limites des guides, une méthodologie permettant d’augmenter les connaissances cliniques en utilisant l'expérience a été présentée ainsi qu'une évaluation de la performance clinique et de la qualité au fil du temps, en fonction des différents résultats cliniques étudiés, tels que l'utilisabilité et la fiabilité clinique derrière les connaissances cliniques formalisées. Enfin, les données du monde réel accumulées ont été explorées pour soutenir les cas futurs, promouvoir l'étude de nouvelles hypothèses cliniques et aider à la détection des tendances et des modèles sur les données à l'aide d'outils d'analyse visuelle. Les modules présentés ont été développés et mis en œuvre dans leur majorité dans le cadre du projet européen Horizon 2020 DESIREE, dans lequel le cas d'utilisation était axé sur le soutien des unités de soins du sein au cours du processus décisionnel pour la prise en charge des patientes atteintes d'un cancer du sein primaire, en effectuant une validation technique et clinique sur l'architecture présentée, dont les résultats sont présentés dans cette thèse. Néanmoins, certains des modules ont également été utilisés dans d'autres domaines médicaux tels que le développement des guides de pratique clinique pour le diabète gestationnel, mettant en évidence l'interopérabilité et la flexibilité du travail présenté

Development of an advanced clinical decision support system : enriching the guideline-based knowledge with experience

La médecine fondée sur les preuves a permis de formaliser des guides de pratique clinique qui définissent des flux de travail et des recommandations à suivre pour un domaine clinique concis. Ces guides se sont construits dans le but de standardiser les soins de santé et d'obtenir les meilleurs résultats possibles pour les patients. Néanmoins, les médecins n’adhèrent pas toujours à ces directives en raison de diverses limitations cliniques et de mise-en-œuvre. D’une part, les médecins n’ont pas toujours familiarisés ou en accord avec les lignes directrices des guides de pratique clinique, doutant ainsi de leur efficacité et des résultats attendus par rapport aux pratiques antérieures. D'autre part, maintenir ces guides à jour en incluant les dernières preuves établies requiert une gestion continue d’une documentation établie sur support papier. Les systèmes d'aide à la décision clinique sont ainsi proposés comme aide durant le processus de prise de décision clinique, par la mise en œuvre informatisée des guides pour promouvoir leur consultation et l’adhésion des médecins. Bien que ces systèmes aident à améliorer la conformité des guides, il subsiste certains obstacles hérités des guides sur support papier qui ne sont pas résolus avec leur mise en œuvre informatisée, comme le traitement des cas complexes non-définis dans les directives ou le manque de représentation d'autres facteurs externes qui peuvent influer sur les traitements fournis et faire dévier des recommandations des guides (c.-à-d. les préférences du patient). La présente thèse propose un système avancé d'aide à la décision clinique pour faire face aux limitations du soutien purement basé en guides et aller au-delà des connaissances formalisées en analysant les données cliniques, les résultats et les performances de toutes les décisions prises au fil du temps. Pour atteindre ces objectifs, une approche de modélisation des connaissances et performances cliniques de manière sémantique validée et informatisée a été présentée, en s'appuyant sur une ontologie et avec la formalisation du concept d'Événement Décisionnel. De plus, un cadre indépendant du domaine a été mis en place pour faciliter le processus d'informatisation, de mise à jour et de mise en œuvre des guides de pratique clinique au sein d'un système d'aide à la décision clinique afin de fournir un soutien clinique à pour chaque patient interrogé. Pour répondre aux limites des guides, une méthodologie permettant d’augmenter les connaissances cliniques en utilisant l'expérience a été présentée ainsi qu'une évaluation de la performance clinique et de la qualité au fil du temps, en fonction des différents résultats cliniques étudiés, tels que l'utilisabilité et la fiabilité clinique derrière les connaissances cliniques formalisées. Enfin, les données du monde réel accumulées ont été explorées pour soutenir les cas futurs, promouvoir l'étude de nouvelles hypothèses cliniques et aider à la détection des tendances et des modèles sur les données à l'aide d'outils d'analyse visuelle. Les modules présentés ont été développés et mis en œuvre dans leur majorité dans le cadre du projet européen Horizon 2020 DESIREE, dans lequel le cas d'utilisation était axé sur le soutien des unités de soins du sein au cours du processus décisionnel pour la prise en charge des patientes atteintes d'un cancer du sein primaire, en effectuant une validation technique et clinique sur l'architecture présentée, dont les résultats sont présentés dans cette thèse. Néanmoins, certains des modules ont également été utilisés dans d'autres domaines médicaux tels que le développement des guides de pratique clinique pour le diabète gestationnel, mettant en évidence l'interopérabilité et la flexibilité du travail présenté.Evidence-Based Medicine has been formalized as Clinical Practice Guidelines, which define workflows and recommendations to be followed for a given clinical domain. These documents were formalized aiming to standardize healthcare and seeking the best patient outcomes. Nevertheless, clinicians do not adhere as expected to these guidelines due to several clinical and implementation limitations. On one hand, clinicians do not feel familiar, agree with and or are unaware of guidelines, hence doubting their self-efficacy and outcome expectancy compared to previous or more common practices. On the other hand, maintaining these guidelines updated with the most recent evidence requires continuous versioning of these paper-based documents. Clinical Decision Support Systems are proposed to help during the clinical decision-making process with the computerized implementation of the guidelines to promote their easy consultation and increased compliance. Even if these systems help improving guideline compliance, there are still some barriers inherited from paper-based guidelines that are not solved, such as managing complex cases not defined within the guidelines or the lack of representation of other external factors that may influence the provided treatments, biasing from guidelines’ recommendations (i.e. patient preferences). Retrieving observational data and patients’ quality of life outcomes related to the provided healthcare during routine clinical practice could help to identify and overcome these limitations and would generate Real World Data representing the real population and going beyond the limitations of the knowledge reported in the Randomized Clinical Trials. This thesis proposes an advanced Clinical Decision Support System for coping with the purely guideline-based support limitations and going beyond the formalized knowledge by analyzing the clinical data, outcomes, and performance of all the decisions made over time. To achieve these objectives, an approach for modeling the clinical knowledge and performance in a semantically validated and computerized way has been presented, leaning on an ontology and the formalization of the Decisional Event concept. Moreover, a domain-independent framework has been implemented for easing the process of computerizing, updating and implementing Clinical Practice Guidelines within a Clinical Decision Support System in order to provide clinical support for any queried patient. For addressing the reported guideline limitations, a methodology for augmenting the clinical knowledge using experience has been presented along with some clinical performance and quality evaluation over time, based on different studied clinical outcomes, such as the usability and the strength of the rules for evaluating the clinical reliability behind the formalized clinical knowledge. Finally, the accumulated Real World Data was explored to support future cases, promoting the study of new clinical hypotheses and helping in the detection of trends and patterns over the data using visual analytics tools. The presented modules had been developed and implemented in their majority within the European Horizon 2020 project DESIREE, in which the use case was focused on supporting Breast Units during the decision-making process for Primary Breast Cancer patients management, performing a technical and clinical validation over the presented architecture, whose results are presented in this thesis. Nevertheless, some of the modules have been also used in other medical domains such as Gestational Diabetes guidelines development, highlighting the interoperability and flexibility of the presented work

Development of an Advanced Clinical Decision Support System: Enriching the Guideline-Based Knowledge with Experience

This paper provides an overview and update on my PhD re-search, which focuses on developing an experience-based clinical decision sup-port system (CDSS) that extends the implemented clinical practice guidelines knowledge. This method relies on the capitalization of clinicians’ experience. First, a decisional event structure is presented, which models the required in-formation involved in a decision-making process. This information includes the implicit knowledge related to the clinicians’ know-how. Second, a method to process this information and create new experience-based rules is presented. Third, a quality assessment algorithm is introduced to score the experience-based rules based on previous patients’ outcomes. Supervisors: Brigitte Séroussi, Nekane Larburu, Jacques Bouau

Advanced Clinical Decision Support Systems Beyond Clinical Guidelines

ABSTRACT: Healthcare is continuously evolving and currently we are in the evidence based medicine era. Evidence based medicine is a medical practice approach which aims to optimize the given care by using evidence. In this context, clinical practice guidelines, which are based on the best available research evidence [1], are highly recommended and their application has been proven to improve quality of care [2]. Nevertheless, in certain cases the evidence is limited given the fact that clinical studies often have strict inclusion criteria and specific situation are not contemplated. Furthermore, when giving a treatment other external factors may affect the decision, such as patient preferences, medical choices and others [3]. Consequently, clinicians are often not compliant to clinical guidelines, but this non-compliance decisions’ information is lost and not used to treat future patients. To overcome with this issue and augment the guidelines´ knowledge, several studies have proposed different alternatives [4]–[8]. Nonetheless to our best of knowledge the literature does not implement a solution that provides a complete overview of the case and gives clinicians the flexibility to understand best the situation. Therefore, in our study, we propose an integral clinical decision support system (CDSS) that consist of a guideline-based CDSS, an experienced-based CDSS and a case-based CDSS [3]. The result of this study is a generic system, that besides giving guideline based recommendations to clinical practitioners, it also collects and processes all the information stored into the systems, i.e. the decisional history [3]. This decisional history includes the health outcomes of a patient, such as toxicity, relapse and other health aspects. These outcomes, along with the rest of the information, are used by the system to assess the treatments for specific patients and generate new knowledge from previous cases. This study has been implemented within DESIREE EU project, which targets breast cancer cases since it is a complex disease with multiple variants that affects more than 460000 new cases and 130000 deaths in 2012 [9]. In the coming months, the first prototype will be tested with real patients to ensure the well-functioning of the whole system

Augmenting Guideline Knowledge with Non-compliant Clinical Decisions: Experience-Based Decision Support

Guideline-based clinical decision support systems (CDSSs) are expected to improve the quality of care by providing best evidence-based recommendations. However, because clinical practice guidelines (CPGs) may be incomplete and often lag behind the publication time of very last scientific results, CDSSs may not provide up-to-date treatments. It happens that clinical decisions made for specific patients do not comply with CDSS recommendations, whereas they comply with the state of the art. They may also be non-compliant because they rely on some implicit knowledge not covered by CPGs. We propose to capitalize the clinical know-how built from such non-compliant decisions and allow physicians to use it in future similar cases by the development of a decisional event structure that allows the modelling, storage, processing, and reuse of all the information related to a decision-making process. This structure allows the analysis of non-compliant decisions, which generates new experience-based rules. These new rules augment the knowledge embedded in CPGs supporting clinician decision for specific patients poorly covered by CPGs. This work is applied to the management of breast cancer within the EU Horizon 2020 project DESIREE

Architecture for a Multimodal and Domain-Independent Clinical Decision Support System Software Development Kit

International audienc