Although many in our industry throw around terms like Big Data and Analytics, the reality is, these systems have been and continue to be static and stupid, only doing the few tasks they have been specifically programming to perform. Step outside of this narrow focus and these systems quickly fail. (Not in the good way) Yet on the flip side, these same systems have delivered tremendous business and societal benefits by automating tabulation and harnessing computational processing as well as programming to deliver huge increases in enterprise and personal productivity.
The days of static, unresponsive computing intelligence is coming to an end. The machines and systems of tomorrow will be cognitive, adaptive and contextual with natural language interfaces capable of making complex decisions involving extraordinary volumes of fast moving, globally disperse data sets. Herein lies potentially the biggest opportunities for disruption.
Recently many hospitals have, for example, started using data analytics to identify cardiac patients at greatest risk while reducing the number of readmissions. Those big data initiatives are tied to cost-saving and resource-conserving efforts as much as they are tied to improving patient’s lives.
Elsewhere in healthcare, technologies like IBM Watson and other Watson-like technologies are now assisting doctors at Memorial Sloan Kettering in diagnosing patients by providing a variety of possible causes for a set of symptoms. Watson can help doctors narrow down the options and pick the best treatments for their patients. The doctor still does most of the thinking. Watson is there to make sense of the data and help make the process faster and more accurate, a kind of sixth sense augmenting their abilities and giving them access to unimaginably vast amounts of additional information to help make better more informed decisions.
This is the promise of cognitive systems--a category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and understanding. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.
Needless to say, there are still challenges to broad adoption of this type of technology; federal laws aimed at protecting the privacy of healthcare data could limit the role of big data in healthcare. Indeed, critics are complaining that data brokers may have greater access to medical data than do patients.
Yet progress marches on and the time has come for healthcare providers to call on fleets of servers to spot hidden trends and anomalies in medical images as they diagnose patients using deep learning technology. This type of computing involves training systems called artificial neural networks to understand a variety of information provided to it. The information can range from audio, images, and other inputs where it can automatically make inferences about what it is and how it should response.
In healthcare this data could be a discussion between a healthcare provider and a patient. But it could also augment these discussions with related or contextual data such as X-rays, MRIs, CT scans, 3D medical images to better help identify and diagnose treatment options. The most advanced of these technologies is found with in Natural language processing or NLP. NLP focuses on linguistics and is especially concerned with the interactions between computers and humans, enabling computers to derive meaning from spoken or natural language input.
Although it’s hard to say what the future of healthcare may look like, it’s becoming evident that advanced forms of machine intelligence will likely play a much more critical role in augmenting and potentially eliminating misdiagnoses from the healthcare world. It seems that this focus area could be a key entry point into healthcare while also enhancing our own intelligence within the space.