Big Data and Data Sharing

Hello, everyone. In this chapter we are going to learn about big data and about how we can share the data? And what are the rules or regulations? which things we should consider? So first of all, what is Big Data? Big Data is a collection of data sets so large and complex that it becomes difficult to process using hand database management tools or traditional data processing applications. So it is actually multi-dimensional temporal and geographical. Usually, Big Data contains six “V”. Volume means the amount of data. Velocity, speed of data, transaction and accumulation. Variety, which is like range of data ties and sources, Veracity the trust work needs of data sources. Value its relevance to health topics.

And variability the changing nature of health events. And nowadays because there is a booming of artificial intelligence as well, So we cannot use the traditional method. So we need to use some new technologies. So what is AI? It has, you know, potential. AI can alleviate the human resources crisis in healthcare by facilitating diagnostics, Big Data analytics and Decision-making. However it is not without its technological, legal and ethical obstacles For example, IBM Watson is using. They use BIG Data to aid Doctors in decision making. And IBM Watson can read up to two hundred million papers in under three seconds. monitors real time data and catches articles as published, Wide variety of data recorded such as EMR of patients, clinical data, genomics.

So what is data sharing? You know previously data was sharing one to one. You know, and between students and faculty within a small lab teamwork or resource compiled intended for a publication. But currently it’s shared among the public; widespread through the internet; no limit to one team of researchers; and data considered a district community. So why we should share data? Actually because needed by government funding agencies, publishers and institutions. Optimizes the impact and visibility of research. A public investment excellent resource. Decreases duplication of effort. Optimize transparency of research findings. And critical to efforts of collaborative research. So the more data we’ll share the better it is.

There are various sources of data such as claim data, clinical trials data, genomic, EHR and EMR or wearable devices as well. Nowadays people are using variable devices a lot. So it is predicted that the quantity of health data health-related data will increase exponentially with advancements in sophisticated genome sequencing techniques, Big Data analytics, and AI. You know, there are predictive and preventive medicine as well And existing database for example like genomics, clinical trials, claims, and EMRs and currently mobile phones, variable devices, Internet of Things and EHRs. These all are, you know, creating a lot of data which is called Big Data. So implementing medicines it could help to identify the problems reduce readmissions and hospital-acquired infections for predictive medicine.

It could help like correct diagnosis and decrease cost overtime and mass treatment to the patients.