Big Data in Healthcare (Part 1)


Too much information:

As advancement in medical healthcare continues to evolve it has been difficult for traditional methods of data management to keep up. So, it is no wonder that medical professionals, pharmaceutical companies, software developers and observers see big data analytics as a perfect tool to further the scope of healthcare in this area.

Computer scientists have coined the phrase big data to refer to the huge amount of digital data that has accrued during the digital age. There is more data available now, than ever before. But raw data is useless on its own, a way of managing this resource and extrapolating pertinent information from it, was needed and so data management and analytics have evolved to accomplish this task.

Big data and healthcare are a logical match for each other. The healthcare sector has long been accustomed to meticulously documenting patient’s records for compliance, regulation and research reasons and with the advent of the digitisation of these record a huge data bank has grown up. In fact, in US healthcare, the amount of data could become unworkable with present tools as the growth of big data continues unabated.  IBM among others has built big data models to assist the dissemination in processing it. They see big data and analytics as only being separated by matters of volume, velocity, variety and velocity.

IBM break big data into four dimensions: volume, variety, velocity and veracity.


Diagram 1: Source: IBM

The Four V’s

Volume – Simply represents the sheer amount of data that is out there and is flowing into the system.

Velocity – Refers to the speed at which the data is arriving and being analysed.

Variety – The amount of different types of data from patient records to costing analysis, drug records, and so on needs to be addressed.

Veracity – The assumption that the data is completely accurate is the one major problem for the health care sector. Similar to financial data, a judgement has to be made as to whether the information is reliable or not.

To be continued…………..