It is measured by vitality, by depth of feeling and by depth of thought. But most of all, it is measured by the experience of participation. Get Flash Now An engagement with the spiritual dimensions of life has always been an essential component of health and wellbeing.
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Abstract The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research.
It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems.
Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm.
In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed.
Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined. Healthcare is a prime example of how the three Vs of data, velocity speed of generation of datavariety, and volume [ 4 ], are an innate aspect of the data it produces.
This data is spread among multiple healthcare systems, health insurers, researchers, government entities, and so forth. Furthermore, each of these data repositories is siloed and inherently incapable of providing a platform for global data transparency.
To add to the three Vs, the veracity of healthcare data is also critical for its meaningful use towards developing translational research.
Despite the inherent complexities of healthcare data, there is potential and benefit in developing and implementing big data solutions within this realm. Two-thirds of the value would be in the form of reducing US healthcare expenditure [ 5 ].
Historical approaches to medical research have generally focused on the investigation of disease states based on the changes in physiology in the form of a confined view of certain singular modality of data [ 6 ]. Although this approach to understanding diseases is essential, research at this level mutes the variation and interconnectedness that define the true underlying medical mechanisms [ 7 ].
New technologies make it possible to capture vast amounts of information about each individual patient over a large timescale. However, despite the advent of medical electronics, the data captured and gathered from these patients has remained vastly underutilized and thus wasted.
Important physiological and pathophysiological phenomena are concurrently manifest as changes across multiple clinical streams. This results from strong coupling among different systems within the body e.
Thus, understanding and predicting diseases require an aggregated approach where structured and unstructured data stemming from a myriad of clinical and nonclinical modalities are utilized for a more comprehensive perspective of the disease states.
An aspect of healthcare research that has recently gained traction is in addressing some of the growing pains in introducing concepts of big data analytics to medicine. Researchers are studying the complex nature of healthcare data in terms of both characteristics of the data itself and the taxonomy of analytics that can be meaningfully performed on them.
In this paper, three areas of big data analytics in medicine are discussed.
These three areas do not comprehensively reflect the application of big data analytics in medicine; instead they are intended to provide a perspective of broad, popular areas of research where the concepts of big data analytics are currently being applied.
Medical images are an important source of data frequently used for diagnosis, therapy assessment and planning [ 8 ]. Computed tomography CTmagnetic resonance imaging MRIX-ray, molecular imaging, ultrasound, photoacoustic imaging, fluoroscopy, positron emission tomography-computed tomography PET-CTand mammography are some of the examples of imaging techniques that are well established within clinical settings.
Medical image data can range anywhere from a few megabytes for a single study e.
Such data requires large storage capacities if stored for long term. It also demands fast and accurate algorithms if any decision assisting automation were to be performed using the data.Benefits & Compensation International• DECEMBER • 3 Employee Benefit Trends in India – challenges and opportunities Naveen Kumar Midha Naveen Midhais the Employee Benefits Practice Leader for Willis caninariojana.com career has.
Older people and Primary Health Care (PHC) of demographic trends. Dealing with the increasing burden of chronic diseases requires health promotion and disease prevention intervention at community level as well as disease management strategies within their health care system.
WHO recognizes the critical role that PHC centres play in. Health Management Information Systems Executives: Roles and Responsibilities of Chief Executive Officers and Chief Information Officers in Healthcare Services Organizations.
Because addressing spiritual issues can make such a difference in an individual’s experience of illness — and often in health outcomes as well — weaving spirituality into medical education has become a priority among integrative medicine leaders.
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More Risky. Less Risky. Relevant therapeutic issues or socio-cultural factors (e.g. diagnosis, client’s religion and traditions, family situation and dynamics).