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Five Ways Big Data Is Changing Healthcare

Posted by Battelle Staff on May 13, 2015

Humans today are more quantified than ever before: from electronic medical records to body-worn sensors like the FitBit, we’re drowning in a sea of data. Is there a way to tie it all together and make sense of it all?

Here are five ways that big data is changing health care.

1. Value-Based Pricing

How do you know how much value a medical device or pharmaceutical therapy gives to patients? The Centers for Medicare & Medicaid Services (CMS) are rolling out Hospital Value-Based Purchasing (VBP) as part of an effort to link Medicare’s payment system to Healthcare Quality Indicators. There are technologies that can help companies and healthcare providers understand and quantify value added for pricing purposes.

2. Personalized Medicine

Not everyone responds to a therapy the same way. Clinical trials can demonstrate safety and overall efficacy, but bigger data sets are needed to tease out the variables that result in different outcomes for different patients. Can we pull together multiple data sources to determine how variables like patient genetic profiles, compliance rates, dosages, timing, concurrent therapies and other factors impact patient outcomes? This data can be used to refine treatment protocols and drive personalized medical recommendations based on patient profiles.

3. Smart Medical Devices

Medical sensors and mHealth apps collect all kinds of data. But what can you do with it? New technologies go beyond just collecting and summarizing data. They analyze data sets so they can be used to make medical decisions and automate medical devices. For example, body-worn medical sensors or mHealth apps could send an automated alert to physicians when they detect the signs of an emerging medical problem or determine that a patient is not complying with therapies. Data analytics can also be used to automate device actions such as medication delivery based on medical sensor data.

4. Clinical Predictive Analytics

Clinical predictive analytics can be used to predict adverse outcomes before they occur. By looking for small changes that are correlated to development of specific problems, data analytics can provide early warning of kidney failure, heart failure and other serious conditions before a crisis occurs. For example, a program could analyze monitoring data for patients with chronic diseases like lupus, diabetes or congestive heart failure and provide an alert to healthcare workers when a patient is at risk for an adverse event. Clinical predictive analytics can also be used to predict how individual patients will respond to specific courses of action and drive medical decision making.

5. Neural Bypass Therapies

Data analytics can also be used to interpret signals from the brain and nervous system. Neurotechnologies powered by data analytics are bringing new hope to paralyzed patients. Battelle's EluciData technology is already being used to power an innovative neural bypass technology that circumvents damaged areas of the nervous system so the brain can communicate directly with the muscles.

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