Analítica Visual y machine learning para toma de decisiones en ecosistemas de Salud (AVisSA)

Information systems in complex environments, such as health, have a data-intensive component that requires merging, synthesizing, representing and visualizing into interfaces with which users interact for high-impact decision making. All data management processes, from acquisition to visualisation, therefore take on an essential role in exploiting their strategic value. Technological ecosystems are the natural evolution of information systems to deploy highly complex services while sustaining their inherently evolutionary nature. However, the challenge remains at the high-level interfaces to create control panels or dashboards that adapt to the evolving and changing nature of data. The line that has been developed in recent projects has advanced in the definition of a framework for a technological ecosystem as a support service for corporate knowledge management (project DEFINES - A Digital Ecosystem Framework for an Interoperable NEtworkbased Society, ref.: TIN2016-80172-R). This framework has been set up in a technological ecosystem for the care domain to give comprehensive and remote support to the needs of formal and informal care providers (e.g., family members) of dependent older people (TE-CUIDA project - TEchnological ecosystem for support to caregivers, ref.: SA061P17). The next objective, following this argument, is to tackle the development of a system of automatic dashboard generation (meta-dashboard) with Domain  Engineering and Artificial Intelligence techniques, in order to obtain dashboards adapted to each case and application domain from the flow of data in technological ecosystems that automatically adapts to the needs of analysis and knowledge management in heterogeneous contexts. Specifically, following the experience of the TE-CUIDA project and other experiences in the field of health, this domain will be taken as a reference due to its complexity and the diversity of information management needs, which appear in the different medical specialities, in order to improve these processes within the health system, with a remarkable impact on the decision-making processes. The implementation of the meta-dashboard will make intensive use of user experience testing throughout its development, which will allow for the guaranteed incorporation of other actors into the ecosystem as stakeholders (public administration, health managers, etc.). These actors will be able to make use of the data for decision-making and design improvements in health provision. The exploitation of the data will make it possible to promote the ecosystem from its initial focus on primary users (doctors, carers and patients) to secondary and tertiary users (managers, public administration, research networks), significantly increasing the viability of the initial proposal. Thus, these tools that support decision-making will improve both the quality of the services provided and their economic efficiency. Initially, the care environment (following the progress made in the TE-CUIDA project) and diagnosis based on cardiology images will be taken as experimental domains.