We all know that big data has been slowly changing the way we do things. We now have the ability and capacity to aggregate massive amounts of data via social hubs like Facebook, Twitter and even Google. The ability to collect such massive amounts of data provides a conduit for us to accurately access trends on both the macro and micro level. Nowhere is this of more important than healthcare research.
Healthcare research has been consistently been plagued with the problem of accurate data. The question on the back of every scientists mind has always been, “is my data accurate?”.
Now we are finally beginning to start a conversation that says “yes” according to massive amounts of data gathered and cross referenced between hundreds of sources we can adequately say that our data is legitimate and highly valid.
In a recent Forbes article we saw, ” The last decade has seen huge advances in the amount of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. The intersection of these trends is what we call “Big Data” and it is helping businesses in every industry to become more efficient and productive.
Healthcare is no different. Beyond improving profits and cutting down on wasted overhead, Big Data in healthcare is being used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. With the world’s population increasing and everyone living longer, models of treatment delivery are rapidly changing, and many of the decisions behind those changes are being driven by data. The drive now is to understand as much about a patient as possible, as early in their life as possible – hopefully picking up warning signs of serious illness at an early enough stage that treatment is far more simple (and less expensive) than if it had not been spotted until later.
So to take a journey through Big Data in healthcare, let’s start at the beginning – before we even get ill.”
Aside from just simply the process of patient records and process-based efficiency, we see much bigger implications to big data in medicine. Arguably one of the biggest limiting factors in modern medicine is the understanding of causation and correlation. Humans are subject to all manner of biases and misjudgments that make data acquisition and evaluation a very tricky business.
As described in a McKinsley article, “One of the main limitations with medicine today and in the pharmaceutical industry is our understanding of the biology of disease. Big data comes into play around aggregating more and more information around multiple scales for what constitutes a disease—from the DNA, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems. Those are the scales of the biology that we need to be modeling by integrating big data. If we do that, the models will evolve, the models will build, and they will be more predictive for given individuals.
It’s not going to be a discrete event—that all of a sudden we go from not using big data in medicine to using big data in medicine. I view it as more of a continuum, more of an evolution. As we begin building these models, aggregating big data, we’re going to be testing and applying the models on individuals, assessing the outcomes, refining the models, and so on. Questions will become easier to answer. The modeling becomes more informed as we start pulling in all of this information. We are at the very beginning stages of this revolution, but I think it’s going to go very fast, because there’s great maturity in the information sciences beyond medicine.”
All of this information provides us with a wealth of information and also hope – hope for a future that is better informed than we are. As technology expands and offers we are better equipped to understand information at a level that we never have been before. With products like DAQ medical providers are able to finally look into the macro and micro trends and see what is happening.