Better health monitoring, assessment and action is a Big Data capability.  Predictive analytics is one of the most popular tools of Big Data.  This reblog reports on how Big Data is moving in and changing how healthcare is approaching risk and return for patient care.

Predicting and Preventing Health Issues with Big Data

by Rick Delgado

For eons, healthcare, in whatever form it came in, was fairly straightforward. Someone would get sick or injured, and a physician would prescribe a cure or treatment using the prevailing medical knowledge of the day. This has held true even in modern times, where many people only think about their health during those times when it fails them.

While this approach is a practical one, especially when many have other concerns on their mind, it doesn’t make for long term healthy outcomes. That’s why many medical institutions are trying to use the latest technology to improve patient care and help people live healthier lives by trying to predict health problems before they happen.

Some of this advanced technology is obvious and visible, while much of it happens behind the scenes. This is where big data is being used. On the surface, big data may seem like an odd choice for the healthcare industry, but it’s actually a perfect fit that can help health professionals predict and prevent health issues.

When broken down into its most basic parts, healthcare is really all about data. Doctors are often seen carrying clipboards filled out with patient data such as age, height, weight, blood pressure, blood type, and more. Even such rudimentary data can be challenging to collect and analyze.

Now factor in more advanced information like CAT scans, X-rays, and the biggest goldmine of health data — genetic data. It quickly becomes obvious that the amount of health data that is currently available or will one day become available is absolutely massive. All of this information needs to be taken into account by big data analytics in order to come up with the best, most accurate predictions. Better data sets essentially equal better results.

That’s really what the move to institute predictive analytics is all about — determining the biggest at-risk factors in patients and preventing health problems from developing. In a sense, it’s similar to how companies use business analytics to predict who is most likely to buy their products; instead, in the case of healthcare, it’s an analysis of factors taken from many different sources over the course of one person’s life. A checkup as an eight-year-old would be considered, much like an emergency room visit would twenty years later.

This leads to one of the biggest challenges that arises when using big data to predict health problems: the unstructured nature of the data. This is especially true when including genetic data. Other challenges include having the right technology on hand to store and analyze these massive sets of data, the ability to scale over time, and being able to protect that information.

While the prospect of utilizing big data in healthcare does present challenges, a number of solutions have come to forefront. Cloud computing is one of the biggest ones, in part because it solves questions over how to store large data sets and the scalability issue. The cloud’s general availability is also a major advantage, since doctors located nearly anywhere would be able to gain access to medical data for their patients.

The cloud is also always on, meaning no real downtime — a particularly valuable attribute when emergencies happen. Machine learning algorithms can also be employed to gain valuable insights into health records and genetic data as they identify patterns that may lead to future health issues.

While many of the benefits touted from using big data in healthcare are theoretical in nature, instances of it being put into practice have yielded positive results. In one example, the use of big data analytics was able to take health data and predict which patients were at risk for metabolic syndrome, a possible indicator of even worse health problems like type II diabetes and heart disease. By identifying risk factors and crafting personalized health plans, doctors were able to reduce instances of metabolic syndrome and other serious health problems while also decreasing the number of hospital visits and overall healthcare costs.

The deluge of health data is only just beginning. With wearable devices and health monitors as part of the Internet of Things (IoT), more data will be collected about people’s health habits and daily routines. With more data will come more precise predictions that could end up preventing serious health problems many years down the road. It may sound like science-fiction now, but in a few years, big data may be the secret ingredient that transforms healthcare as we know it.