The Healthcare industry is embracing the big data tech at a much faster pace, but all is not well here. The industry is at the precipice of Big Data revolution, but it lacks in one critical issue, and that is standardization of Health care data. Big data alone is not good enough to revolutionize the industry, it also must be good data. Currently, the huge chunk of healthcare data that the industry amasses on a daily basis is riddled with inconsistencies and inaccuracies, with frequent occurrence of scenarios where even though the data is good, they lack the right kind of information. A simple example of this peculiarity is medical imaging.
Doctors all around the world, stick to the practice of writing diagnostic notes directly on the images they take. Now, if a set of such images, is fed into a computer that runs a machine-learning algorithm, the computer based on that algorithm can learn how to analyze the image with high accuracy. But in the real world scenario, some doctors don’t stick to the practice of making annotations, then there is the little consistency situation in annotations among different physicians, such small and minor issues often lead to failure.
Healthcare industry people have to work with multi-institutional data sets, and thus often the issue arises where a machine-learning algorithm is implemented to identify the hospital from where a said sample came from rather than whether the sample is from a healthy individual or a sick patient who needs attention. Such key issues, should limelight the situation in the industry and the need for standardization.
Until the industry, finds ways to standardize healthcare data, the anticipated Big Data revolution is not happening.