Data can be intimidating and overwhelming in general, especially when dealing with a large or complex dataset. This can be a massive undertaking for any individual in any role across the health system. As alluded to, the contours of big data can be intimidating and overwhelming, a case in point is the US with a population of around 300 million citizens, indeed, in terms of the data that can be generated from such a population is enormous and huge, hence, the need to underscore what it entails, especially in the health system, of big data coupled with often conflicting debate of Human Intelligence and the Artificial Intelligence, in addition to reviewing the question, is there a need for a collegial working situation between Human Intelligence and Artificial Intelligence? 

For a better understanding of what I will be discussing in this paper, it is surreally important that I define the various terminologies that one will encounter with as they traverse the contours of this work. The first on is Big Data; big data is defined as a term applied to data sets that are too large or complex for traditional databases or data-processing application software to capture, manage, and process with low-latency. Big Data comes from sensors, devices, video/audio, networks, log files, transactional applications, web and social media-much of it generated in real time and at a very large scale. Analysis of big data may reveal patterns, trends, and associations especially relating to human behavior and interactions.

Further, Artificial Intelligence is the simulated intelligence in machines that is programmed to think, work and react like human beings. Artificial Intelligence works in tandem with the Internet of Things which is the lot that encompasses everything connected to the internet, for example devices from simple sensors to smart phones and wearables, connected together.

In order to understand how effective data is, the litmus test is the use of the 4 Vs that is the volume, validity, velocity and variety. On the volume, it is estimated that the data contained in EMRs and Data Imaging was 1.2 to 2.4 exabytes and the same was projected to increase to around 25000 petabytes by 2020.

On the Velocity of the data growth, especially in the health systems, it entails the continued availability of Physiological devices such as glucose monitor, cardiovascular monitors, patient digital persona among others and how they are constantly being updated in the real time, and which is yielding data faster than can be contemplated.

On the Variety of the available data, the data comes from a diverse range of sources and the sources cannot be rendered interoperable whatsoever. Finally, on the Validity of the data, it is a requirement that the available data is accurate and there is the existence of common semantics in health data for it to be valid, in the sense that the data should be a clean and correct one, there is the validity of variables, potential benefits and possible negative outcomes.


With the foregoing in mind, it is therefore important to look at the implications of Big Data which should align with the value based care. It is the common ground the tutors in the webinar that data to be used should define a process of care in what they call the precision medicine and the population health management which should determine the outcomes of the applied intervention for a rational decision making which should be cost effective.

The other area to address is on how to retrieve the signals from the noise. This entails sieving what is important from what is irrelevant. The major comparison here is between Artificial Intelligence and Human Intelligence. The question to be answered is whether the Artificial Intelligence will dehumanize the Human Intelligence. The answer to that issue is that, at all material times, Artificial Intelligence cannot since humans have a major role to play in the future of medicine and that the union of Human Mind and machines learning that bring together complementary strength that is required for successful use of big data to improve health and wellness in our society is key.


However, this all process is not short of challenges, it is faced with the issue of bias and misrepresentation of data, the tutors in the webinar go into a deep length and elaborate on these two challenges. That notwithstanding, on the challenge of bias, it is beyond peradventure that algorithms and machine learning in the Artificial Intelligence only operate as long as the Human Intelligence feeds data and information into it and since humans are not short of biases, the same can be reflected which leads to the various (un)palatable conclusions.

On the issue of misrepresentation, the line between individual security of their personal data on health and the desired public comments with the view to gathering accurate data without misrepresentation is blurred with a lot of penumbras, and there is a very low threshold for failure when it comes to securing data and protecting the privacy of the same as it is coupled with a lot of mistrusts.


Big Data if well managed will allow us, the USA citizens to pull back from that very narrow focused view of individual represented by precision medicine to examine larger trends across a given population, such a point is critical if at all we are to make any rational decisions about what preventive strategies will have the highest priority and greatest impact on the population at large.

There is a need for the unification of human mind and learning with machine intelligence, since, at no time will the Machine intelligence replace human radiologists in the world of medicine. However, in the world of medicine, this should be a wakeup call to the various players that imagers who do not learn how to leverage their capabilities of artificial intelligence and big data in their work will be replaced by those imagers who do embrace big data and learn how to integrate it into their work.


Healthbox Webinar: Harnessing Big Data in Healthcare, available on YouTube

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