La puesta en marcha será escalonada y se prolongará durante un lustro
Sílvia Cordomí's insight:
El Registro de Actividad de Atención Sanitaria Especializada, aprobado este viernes por el Consejo de Ministros, arrancará el 1 de enero de 2016, según refleja el real decreto que ha publicado el Ministerio de Sanidad en el Boletín Oficial del Estado. El plazo de inicio de este ‘big data’ será escalonado y se prolongará hasta 2020. Los primeros datos que se inscribirán serán los de hospitalización y cirugía ambulatoria de los hospitales generales y especializados de agudos, públicos y privados, a partir del 1 de enero del próximo año. El mismo día, pero de 2017, comenzará la fase de registro de la hospitalización de media y larga estancia y de salud mental y tratamiento de toxicomanías, y ya no será hasta 2018 cuando se metan los contactos de los procedimientos ambulatorios de alta complejidad, los de hospital de día, la hospitalización domiciliaria y las urgencias de los hospitales públicos generales y especializados.
Age-related cognitive decline is common and may affect memory, orientation, attention, abstract thinking and perception, which may lead to substantial difficulties and disabilities in everyday life. Much evidence suggests that computerized cognitive training interventions may mitigate decline by improving neuropsychological outcomes in older adults, but there is clearly a need for large-scale, methodologically rigorous comparative effectiveness trials in the area. This article underscores that need and reviews eight trials that met a set of predetermined criteria before highlighting two novel and complementary analytic methods - big data analytics and network meta-analysis - that may be used to facilitate decisions regarding which cognitive training programs should serve as candidate interventions for large comparative effectiveness trials.
IBM’s Watson, a cognitive computing system originally designed to vanquish human competitors on Jeopardy in 2011, has been winding its way into more and more healthcare and health-related use cases. But like most novel technologies, a number of people still don’t understand exactly what Watson is or does — or what IBM’s roadmap for the technology is. On the sidelines of the Health 2.0 event in Santa Clara, California last week, MobiHealthNews sat down with John Wolpert, product manager for IBM’s Watson Ecosystem, to answer some of these questions.
Back in 2011, rheumatologist Jennifer Frankovich, MD, and colleagues at Lucile Packard Children’s Hospital Stanford used aggregate patient data from e (Examining the potential of big data to transform health care http://t.co/wR2DydeabO...
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Throughout this year, we've seen a surge in wearable tech, to the point that it's even managed to eclipse smartphones and tablets (at least, in PR impact) at various electronic shows over the past few months.
However, there's certain care that needs to go into how it's being marketed, according to a new report titled "Wearables: 10 Insights on Device Adoption and Business Opportunities," compiled by eMarketer.
International Data Corporation's projections indicate that wearable device shipments will triple in number throughout the remainder of this year, with 19.2 billion units shipped worldwide. However, there are two things that keep the enthusiasm at bay....
This study describes the characteristics of extramural and transmural telemonitoring projects on chronic heart failure in Belgium. It describes to what extent these telemonitoring projects coincide with the Chronic Care Model of Wagner.Background: The Chronic Care Model describes essential components for high-quality health care. Telemonitoring can be used to optimise home care for chronic heart failure. It provides a potential prospective to change the current care organisation.Results: The projects were situated in primary and secondary health care. Their primary goal was to reduce the number of readmissions for chronic heart failure. None of these projects succeeded in a final implementation of telemonitoring in home care after the pilot phase. Not all the projects were initiated to accomplish all of the dimensions of the Chronic Care Model. A central role for the patient was sparse.Conclusion: Limited financial resources hampered continuation after the pilot phase. Cooperation and coordination in telemonitoring appears to be major barriers but are, within primary care as well as between the lines of care, important links in follow-up. This discrepancy can be prohibitive for deployment of good chronic care. Chronic Care Model is recommended as basis for future.
The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with biomedical data in order to promote personalized medicine and to provide better treatments. Efficient analysis and interpretation of Big Data opens new avenues to explore molecular biology, new questions to ask about physiological and pathological states, and new ways to answer these open issues. Such analyses lead to better understanding of diseases and development of better and personalized diagnostics and therapeutics. However, such progresses are directly related to the availability of new solutions to deal with this huge amount of information. New paradigms are needed to store and access data, for its annotation and integration and finally for inferring knowledge and making it available to researchers. Bioinformatics can be viewed as the "glue" for all these processes. A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge.
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