healthcare-dataThose who work in healthcare research have to be on the ball an awful lot of the time if they’re to spot escalating problems, read symptoms and prescribe appropriate treatments in time. Like a peregrine falcon circling hundreds of feet above the earth, medical professionals are trained to spot the infinitesimal movements of symptoms far below, swooping in to make a diagnosis or prescribe a course of action. With their eyes trained on such specific details, however, other issues can sometimes escape their notice – like a hunter levelling his rifle to take a shot at the circling predatory bird.

Too much data?

In the analogy used above, the bird’s pinpoint eyesight is the architect of its own downfall, but in the case of many medical professionals, it’s often the patients that pay the price for the oversights of doctors, nurses and surgeons. Today, we have more healthcare data available to us than at any other time throughout human history, and hidden in all that healthcare data are patterns we’re often incapable of spotting for ourselves.

The answers to all kinds of medical mysteries are hidden in plain sight, undiscoverable for the simple fact that human brains aren’t built to make those kinds of connections based on raw data alone. Fortunately, those answers aren’t likely to remain undiscoverable forever, as automated data research has given us the power to uncover the kinds of mysteries we thought were forever lost to us…

Pioneering data analysis automation

Researchers at Stanford University have been putting automated data research to good use, coming to the kind of conclusions that would previously have required hours of detailed, targeted clinical trials to retrieve. The Stanford study recently came to the conclusion, for example, that two completely unrelated drugs were conspiring to raise blood sugar levels in patients prescribed both treatments. Paxil, an antidepressant and Pravachol, used for reducing cholesterol, were harmless on their own but raised patients’ blood sugar to dangerous levels when taken together. By analysing the healthcare data of thousands of individual patients, the Stanford team’s data mining techniques were able to discover the link between the two drugs, taken in combination so infrequently that no human had ever have noticed the potentially lethal cocktail on their own.

Techniques like those pioneered in Stanford University can help to improve the speed, accuracy and cost-effectiveness of healthcare research, crunching medical data captured in the day-to-day treatment of patients in order to recognise patterns that the human eye would miss time and time again. Just think of the secrets your healthcare institution’s data could hold! Make the most of your medical research data treasure trove with our automated healthcare data capture services, or contact us for a free, no-obligation workshop where you can learn about what we do in more detail.