Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make the deployment and dissemination of them extremely difficult in real practice environments. In this work, a complete computer based architecture to help the representation and execution of Clinical Pathways is suggested. Furthermore, the difficulties inherent to the design of formal Clinical Pathways in this way requires new specific design tools to help making the system useful. Process Mining techniques can help to automatically infer processes definition from execution samples. Yet, the classical Process Mining paradigm is not totally compatible with the Clinical Pathways paradigm. In this paper, a pattern recognition algorithm based in an evolution of the Process Mining classical paradigm is presented and evaluated as a solution to this situation. The proposed algorithm is able to infer Clinical Pathways from execution logs to support the design of Clinical Pathways.
Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.
Information and communication technologies (ICT) offer innovative formats for promoting healthy lifestyles and reinforcing public health initiatives. They can be applied to large population segments without losing the functionality of being tailored to individual fluctuating needs. Advantages of ICT include real-time provision and adaptation of nutrition and health recommendations based on an individual's particular situation, the potential to combine assessment procedures with healthy lifestyle support and the ability to unify psychosocial and cultural dimensions to enhance adherence. Two pilot programs are presented that show the potential for applying ICT to the promotion of healthy eating and physical activity habits.
Chronic cardiovascular diseases directly account for millions of deaths, billions of Euros and a big number of disabilities affecting the world's population. Even though primary and secondary prevention factors are well known, the awareness and the concern of citizens and patients is not big enough to cause a significant change in lifestyle that modifies the increasing trends. Patients and families, professionals and healthcare systems are not prepared to fight against this burden in an effective and aligned way. Some disease management programmes based on ICT solutions have and are currently being tested around the world but their relative impaction has been very limited. This paper proposes a new turn into Personal Health Systems applied to chronic disease management by increasing the capabilities for personalization, providing the patients with motivation and coaching support and enabling the work of the professionals with intelligent tools for strategic and clinical decision making based on the newest medical evidence.
Disease Management (DM) is a system of coordinated healthcare intervention and communications for populations with conditions in which patient self-care efforts are significant. e-DM makes reference to processes of DM based on clinical guidelines sustained in the scientific medical evidence and supported by the intervention of Information and Telecommunication Technology (ICT) in all levels where these plans are developed. This paper discusses the design and implementation of a e-DM system which meets the requirements for the integrated chronic disease management following the recommendations of the Disease Management Association and the American Heart Association.
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