New Tools are Helping Health Care Analysts Sort Through Mountains of Data

Although it is a critical part of the process, gathering and storing data is only the beginning of successful why population health management. Even organizations that have long been proactive about ensuring that every data point generated within their borders has found a good, safe home have often arrived an impasse when it came to deciding what to do next. Productively analyzing data, it turns out, is not always the straightforward task that it might at first seem to be.

There are a variety of reasons for this, with some of them being of a general nature and others inherent to clinical data management. The scales at which many health organizations operate are often an important factor, because the data collections they entail mean that especially sophisticated and powerful analytic tools are required if anything of interest is to be focused in on.

In fact, many current estimates see health care analysts spending as much as four-fifths of their working time, on average, not actually analyzing data but simply trawling through it. These attempts at narrowing down data sets to workable and interesting sizes do not leverage the real expertise of these often highly-paid experts, but instead have them performing something more akin to routine, low-skilled work.

Tools that can automate this process, at least to an extent, then, can be an important asset when it comes to health data management. Fortunately, these are now becoming widely available, with the upshot that health data analysts are now finding themselves with more time to devote to what they are best at and most valued for.

Health data management experts, in fact, often point to the growing popularity of such tools as one of the most exciting recent developments in the field. Although no such tool is likely, anytime soon, to ever substitute directly for human experience, intelligence, and intuition, the best of them are increasingly becoming able to make the kinds of productive initial judgments that put analysts in a better starting position.

As such tools become more and more sophisticated, in fact, it seems likely that the productivity of analysts throughout the industry will grow even further. Of course, data stores are growing, too, which means that the current state of the art with regard to this kind of technology will come to seem insufficient before too long. Steady advances, then, will help to ensure that the benefits that are becoming apparent today will last into the future.