Technology continues to transform the way we live our lives, greatly improving our ability to collect, store, and analyze information. This goes for the healthcare industry as well, where having quick access to accurate data is important for the best patient outcomes.

One powerful, but still-emerging aspect of the computer revolution is machine learning. Simply put, machine learning is the science of programming computers to learn for themselves. By taking in large amounts of data and learning to recognize patterns, computers can make fairly accurate predictions about similar or novel situations. Think IBM Watson or Google’s DeepMind. As the technology improves, more predictions can be made that will be even more accurate.

Healthcare is in the nascent days of trying to figure out how to tap into these advances. There's a massive amount of health data available worldwide, and this information can be used to make predictions about who will stay healthy, who is at risk for certain diseases and conditions, and how to provide healthcare more affordably and efficiently. Providers are learning how to tap into that data and analytics as they adopt strategies that aim to improve the health of entire communities.

Let's take a look at a few examples of how healthcare companies are using machine learning to provide better care.

Lasik Eye Surgery in India

Last year, Microsoft partnered with an eye care company in India, the LV Prasad Eye Institute (LVPEI). The LVPEI has been collecting patient data across India since 2010, and together with Microsoft they sought to improve patient outcomes by using this data in new ways. This was a pilot program to test the theory and software.

Using a cloud-based machine learning software known as Azure, the LVPEI and Microsoft collected millions of clinical data points from patients with eye diseases, including where they were located in the country. Using this data, they were able to predict the risk of regression for Lasik eye surgery with good success.

Doctors could be given a prediction of the possible success or failure of a particular operation, as well as how good the patient's vision would be one day and one week after the operation. With information like this doctors could better understand the risks involved for a particular patient, which lead to more effective treatments while helping to cut costs.

How Does Weather Affect Respiratory Disease?

One of the greatest benefits of machine learning is the ability to take very large sets of different kinds of data and search for correlations between them. This is exactly what they did recently in the United Kingdom at the North Tees and Hartlepool National Health Services Trust.

Instead of just looking at patient data, they took the novel approach of combining it with information about weather patterns and trends. They were then able to see how bad weather affected conditions like respiratory disease, as well as how it affected drug prescriptions across the area observed.

Without the power of machine learning, these two data sets would have remained isolated and the connections hidden. What other important information is hiding in the correlations between data sets like these?

Preventing Prescription Errors in Real-Time

MedAware was founded in 2012 after a young boy died because he was accidentally prescribed the wrong drug by his doctor. Their mission is to identify and prevent prescription errors in real time to prevent cases like this from happening.

By analyzing electronic medical records on a very large scale with machine learning algorithms, MedAware is trying to uncover patterns.. Since physicians generally prescribe the correct drugs, the algorithm can learn what a “normal” prescription would be in a given situation. If a different drug is being prescribed, the system can trigger a real-time alert to notify the physician.

In a pilot study in Israel, MedAware used previous data from tens of thousands of patients, including over 450,000 prescriptions. Both inpatients and outpatients were included.

In this retroactive test, an alert was generated for more than 3 percent of inpatients and more than 1 percent of outpatients. For inpatients, only 40 percent of those errors were caught by the nurses. The types of alerts included patients being described the wrong drug, being prescribed a drug that's contraindicated for a particular condition, mixing up prescriptions between patients, and failure to change the dose of a drug.

The are just a few examples of how machine learning can uncover hidden truths about the world around us and help transform healthcare into a system that truly seems intelligent. Central to all of this, however, is ensuring that the data is linked to right patient at the right place and right time, which is why the CHIME Healthcare Innovation Trust is at the forefront of trying to solve the problem of patient identification. We've been collecting massive amounts of data for years, and now is the time to make good use of it.

HeroX is hosting a revolutionary healthcare initiative, the CHIME National Patient ID Challenge. If you've got some great ideas on how to accurately identify patients in the U.S., enter for your chance to win $1,000,000!