The amount of global healthcare data is expected to increase dramatically in 2020. According to research conducted in 2018 by Statista, estimates from 2013 suggest that there were about 153 exabytes of healthcare data generated in that year. However, projections indicate that there could be as much as 2,314 exabytes of new data generated in 2020. To lend some perspective to the magnitude of this growth, it is helpful to know that one exabyte of data is equal to one billion gigabytes. The challenge for medical practitioners, providers, suppliers and researchers is how do they mine this data in a way that enables them to put it to work for themselves, their partners, and most importantly, their patients.
As everyone is well aware, the COVID-19 pandemic has only fueled the need to meet and overcome this challenge more quickly and effectively. The good news is that as this avalanche of data is occurring, inexpensive, large-scale computing power is now available to process this data at the edge. And with all this compute and storage available at the edge, it has created the perfect environment to employ artificial intelligence (AI) and machine learning (ML) to meet, and even exceed the challenges that the healthcare industry is facing. Better yet, through the strategic and creative use of AI and ML, will come exciting new breakthroughs and innovations–some yet to even be conceived.