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BackgroundThe 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. ObjectiveThis 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. MethodsTwo 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. ResultsTechnical 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. ConclusionsTechnology 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.

Clinical Practice Guidelines (CPGs) gather latest evidence-based results to guide and support clinicians over the decision-making process to provide best care. Nevertheless, clinical cases may be subject to some biases (understood as noncompliance with CPGs) that can lead to adapt care delivery. In this work an experience-based decision support leaning on the structuration of the Decisional Event concept for tracking and storing each clinical decision is presented. Moreover, a visual analytics tool is provided in order to facilitate the visualization of biases from guideline-based decision support and the identification and inclusion of realworld evidence into the reasoning process by augmenting the knowledge formalized in the implemented guidelines. [ABSTRACT FROM AUTHOR]

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 multimodal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the terminoontological 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. [ABSTRACT FROM AUTHOR]

Technologies such as decision support systems are expected to help clinicians implement clinical practice guidelines (CPGs) with the aim of decreasing practice variations and improving clinical outcomes. However, if CPGs provide recommendations to improve patient care, they may fail to take into account actual clinical outcomes associated to the recommended treatment, such as adverse events or secondary effects. In this paper, we present a novel experiencebased decision support approach applied to the management of breast cancer, the most commonly diagnosed cancer among women worldwide. Capitalizing on the clinical know-how of physicians and the modeling of patient's outcomes and toxicities in a computer interpretable way, we are able to discover new knowledge that helps improving patient-centered clinical care. This work is conducted within the EU Horizon 2020 project DESIREE. [ABSTRACT FROM AUTHOR]