Publications by authors named "Zoe Valero-Ramon"

Participatory design (PD) is increasingly used to support design and development of digital health solutions. The involves representatives of future user groups and experts to collect their needs and preferences and ensure easy to use and useful solutions. However, reflections and experiences with PD in designing digital health solutions are rarely reported.

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Objectives: LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators.

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Introduction: Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes.

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Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

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Article Synopsis
  • - Process mining techniques analyze business processes using execution data, particularly in healthcare to evaluate diagnostic, treatment, and organizational workflows.
  • - Despite the vast data generated in hospitals, rigorous adoption of process mining is limited to specific case studies, pointing to a lack of systematic integration in healthcare settings.
  • - The Process-Oriented Data Science in Healthcare Alliance aims to enhance research and application of process mining in healthcare by addressing unique challenges, such as process variability and patient focus.
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Article Synopsis
  • * Current risk models typically use static health data, limiting personalized care; this research aims to develop dynamic models reflecting ongoing patient behaviors through Process Mining techniques.
  • * The study identified three dynamic models for hypertension, obesity, and diabetes, advocating for a shift from generic treatments to personalized medicine based on individual patient behaviors and sensor data.
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In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals.

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The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes.

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