JM and MH led the design of the study with all authors contributing to the study design and ethics submission. JM recruited study participants. JM and/or MH led the collective intelligence workshops. All authors reviewed the initial set of barriers and solutions and suggested on the final representation. JM drafted the manuscript with all authors contributing to the review and edit. All authors have critically read and approved the final manuscript. MD and LB provided senior supervision and mentorship on research activity planning and execution.
The study may contribute to international research on UHC in two ways. First, to the best of our knowledge, this is the first attempt to measure the extent to which countries have systemic learning capacities for UHC. Our framework tries to embrace a comprehensive view of the attributes that matter for systemic learning, including how knowledge can be converted into action and how actions can feed the collective memory of the system knowledge. The comparison within countries, and to a lesser extent across countries, confirms the merits of our tool. Second, we have adopted an original approach that has maximised participation across and within countries. We involved national UHC actors systematically and deeply, from the identification of the broad research question of the overall project to data analyses and interpretation through the validation workshops. With this project, we have ourselves practiced what we recommend, that is to implement activities in such a way that they consolidate the autonomous learning capacity of the national UHC system.
Collective Intelligence In Action Epub File
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We believe that our study also sets out an interesting direction for other countries. Progress towards UHC will require that national actors work together and develop enough collective intelligence to ensure that strategic decisions are grounded in knowledge. This vision implies a new culture and other ways of organising health systems. The ambition should be to create environments, practical processes and a leadership supportive to systemic learning.
Nowadays, various biomedical and healthcare tools such as genomics, mobile biometric sensors, and smartphone apps generate a big amount of data. Therefore, it is mandatory for us to know about and assess that can be achieved using this data. For example, the analysis of such data can provide further insights in terms of procedural, technical, medical and other types of improvements in healthcare. After a review of these healthcare procedures, it appears that the full potential of patient-specific medical specialty or personalized medicine is under way. The collective big data analysis of EHRs, EMRs and other medical data is continuously helping build a better prognostic framework. The companies providing service for healthcare analytics and clinical transformation are indeed contributing towards better and effective outcome. Common goals of these companies include reducing cost of analytics, developing effective Clinical Decision Support (CDS) systems, providing platforms for better treatment strategies, and identifying and preventing fraud associated with big data. Though, almost all of them face challenges on federal issues like how private data is handled, shared and kept safe. The combined pool of data from healthcare organizations and biomedical researchers have resulted in a better outlook, determination, and treatment of various diseases. This has also helped in building a better and healthier personalized healthcare framework. Modern healthcare fraternity has realized the potential of big data and therefore, have implemented big data analytics in healthcare and clinical practices. Supercomputers to quantum computers are helping in extracting meaningful information from big data in dramatically reduced time periods. With high hopes of extracting new and actionable knowledge that can improve the present status of healthcare services, researchers are plunging into biomedical big data despite the infrastructure challenges. Clinical trials, analysis of pharmacy and insurance claims together, discovery of biomarkers is a part of a novel and creative way to analyze healthcare big data. 2ff7e9595c
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