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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34

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    From spreadsheets to interfaces: redesigning clinical variable definition through interactive workflows
    (CEUR-WS.org, 2025-09-03) Vázquez-Ingelmo, Andrea; Nieto-Campo, Islem Román; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, Pedro L.
    Spreadsheets remain a common but fragile foundation for clinical data management, often leading to errors and inefficiencies in defining and collecting structured variables. This paper presents a user-centered redesign of the variable definition workflow in a platform for managing structured clinical data and medical images. The proposed solution replaces manual spreadsheet-based schema creation with an interactive web interface that enables users to define, categorize, and reuse variables more effectively. It also introduces automated generation of validated spreadsheet templates based on the platform’s internal schema, reducing the likelihood of formatting and semantic errors during data entry. A revised workflow illustrates the improved process, and the system addresses key usability issues previously identified through heuristic evaluations. Remaining limitations, such as continued reliance on offline data entry, are discussed, along with future work directions that include usability validation and AI-assisted variable generation.
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    A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare
    (MDPI, 2020-07-22) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Therón, R.
    Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain
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    Dashboard meta-model for knowledge management in technological ecosystem: a case study in healthcare
    (MDPI, 2019-12-02) Vázquez-Ingelmo, A.; García-Holgado, A.; García-Peñalvo, F. J.; Therón, R.
    Informal caregivers play an important role in healthcare systems in many countries. They have a high impact on reducing care costs related to dependent persons because their support prevents institutionalization. A technological ecosystem has been defined to support informal caregivers using psychoeducation techniques. This ecosystem should include a dashboard to support decision-making processes related to the wellbeing of patients and caregivers. A dashboard meta-model was used to obtain a concrete model for the presented context. This meta-model allows defining dashboards adapted the users' needs and specific data domains